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Article

Nexus of Natural Resources, Renewable Energy, Capital Formation, Urbanization, and Foreign Investment in E7 Countries

by
Zuyao Wang
1 and
Runguo Xu
2,*
1
Business School, University of New South Wales, Sydney, NSW 2052, Australia
2
School of International Relations, Yonsei University, Seoul 03722, Republic of Korea
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(24), 11290; https://doi.org/10.3390/su162411290
Submission received: 19 November 2024 / Revised: 17 December 2024 / Accepted: 18 December 2024 / Published: 23 December 2024
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Abstract

:
The global trend of rapid economic development and urbanization has created questions regarding the quality of the environment. In the group of emerging economies (E7), environmental challenges have intensified due to specific dynamics unique to these nations. This research is focused on determining the influence of urbanization (UBNZ), renewable energy (RWNE), capital formation (CPFR), foreign direct investment (FDIN), and natural resources (NTRR) on the ecological footprint (ECLF) of the E7 economies. The study employs the Panel Autoregressive Distributed Lag (PMG-ARDL) approach to examine these relationships, utilizing data spanning the period of 1990–2022. The results reveal that a 1% increase in the CPFR, NTRR, and UBNZ leads to increases in the ECLF of 0.0581%, 0.0263%, and 0.0299%, respectively. Conversely, a 1% increase in RWNE and FDIN reduces the ECLF by 0.0207% and 0.0556%, respectively, in the E7 economies. The study’s findings are further validated through robustness testing via the fully modified ordinary least squares (FMOLS) method. The study concludes with actionable policy recommendations aimed at enhancing environmental quality within these economies. These recommendations include promoting renewable energy adoption, attracting environmentally sustainable foreign investments, and implementing strategies to manage urbanization and natural resource use effectively.

1. Introduction

Recently, the occurrence of climate disruption and the increase in global temperatures have emerged as significant concerns on a global scale, impeding progress toward achieving sustainability objectives. The scarcity of clean water, deterioration of soil, and loss of biodiversity are significant obstacles to achieving sustainable development globally [1]. The primary drivers exacerbating these environmental issues include population increase, rapid economic activity, extensive utilization of fossil fuels, and NTRR exploitation [2]. Despite this unfavorable depiction, the consumption of world resources nevertheless surpasses their production. On the basis of the most recent statistics provided by the GFN, the average global capacity per capita in 2022 was 1.5 global hectares, whereas the actual ECLF per person was 2.6 gha [3]. There is currently an ecological deficiency of 1.1 g per hectare. Moreover, current research, such as the study conducted by [4], suggests that there are clear indicators that the green deficit will persist in the near future. Notwithstanding these discoveries, the reluctance of both advanced and emerging nations to relinquish their ambitious goals for economic expansion and job creation undermines the effectiveness of the environmental action plan. Hence, researchers must discover methods to mitigate environmental strain without compromising economic objectives [5]. Since the last decade, there has been a significant amount of interest in discovering the sources of environmental problems [6]. Initial studies commonly associated environmental deterioration with both economic expansion [7] and energy usage [8]. The literature on the causes of environmental degradation has acquired a significant pace and become more diverse over time. The authors of [9] studied the effects of many economic, political, and social factors on environmental degradation. The authors of [2] analyzed the problems of environmental degradation in 2023. Nevertheless, to determine the factors responsible for environmental degradation, it is necessary to choose a comprehensive and reliable environmental indicator. In the literature, CO2 emissions are preferred for their ease of data collection as well as their status as the major GHG contributors [10]. However, these still represent only a small fraction of the environmental damage [11]. Ref. [12] consider CO2 emissions as an incomplete indicator of the NTRR. The fact that CO2 is considered an indicator of harm limits the correct evaluation of damage to environmental types that extend beyond air pollution. According to the data, more than 80% of the global population currently lives in ecologically impacted areas [13,14,15]. The ECLF measures the area of arable land and the amount of water resources available to societies for the purpose of recharging their existing resources and treating their waste [16]. Unlike CO2, ECLF provides a precise measure of the actual level of ecological damage [17].
FDIN refers to the movement of financial resources that is typically linked to the exchange of knowledge, technology, and managerial methods from one country to another. Over the last twenty years, FDINs have emerged as crucial components of global-scale globalization efforts. FDI has three impacts on the product side of the economy: scale impact, composition impact, and technology impact [18]. The scale impact refers to the impact on the overall amount of economic activity that occurs when there is an increase in investment in the economy. The phenomenon of scale is anticipated to contribute to ecological damage. The impact of FDINs is demonstrated by a change in the structure of industries in the economies that receive it. The environmental and ecological outcomes can vary depending on the industries that are either growing or declining, according to the composition impact. The technological effect pertains to the dissemination of novel knowledge and methodologies, encompassing advanced technologies that possess the capacity to enhance productivity and the condition of ecosystems. FDI affects both income and income inequality when it is considered from a consumption point of view for an economy. When income is spread evenly among people, there are more consumers from an average level of income, resulting in greater ecological impact. [19] support the opposite conclusion in their analysis of OECD countries. In fact, FDINs have been found to have a positive effect on income inequality, with a rise rather than a decrease. These findings suggest that FDINs have a moderating effect on environmental degradation.
Climate change is an issue of a dynamic environment that has global impacts and is a topic of intense debate. The United Nations prioritized sustainable development by enacting sustainable development goals [20]. Organizations prioritize sustainable development, the enhancement in environmental quality, and the promotion of social welfare as crucial strategic objectives. Consequently, policymakers are faced with a dilemma regarding the balance between enhancing environmental quality and fostering economic growth. Their duty is to reveal the main factors causing environmental issues while also taking steps to avoid further environmental damage and support the development of sustainability. In addition to the United Nations’ planned climate action, it is imperative for the globe to be prepared for unpredictable economic circumstances. The COVID-19 pandemic has underscored the urgent need for global policies to address and mitigate uncertainties effectively. Therefore, global environmental degradation poses significant concerns [21,22]. Moreover, the current resources can aid in efficiently resolving these challenges. The long-term importance of NTRR in the environment and economy is well acknowledged [23]. This study aims to empirically re-evaluate the relationships among NTRR, unpredictable economic policies, UBNZ, and ECLF in a substantial cohort of seven developing nations.
The presence of NTRR serves as a motivating factor for developing countries. However, many resource-rich countries have been shown to make compromises in terms of environmental protection [24]. Wealthy countries with abundant NTRR have successfully utilized their resources to create a sustainable environment and economy, resulting in significant benefits [25]. Nevertheless, emerging nations continue to face challenges in utilizing their existing resources. Hence, reassessing the significance of NTRR in determining ECLF in developing economies is intriguing.
Moreover, the significance of energy in any economy cannot be disregarded. With the global shift towards RWNE sources, the implementation of an energy structure can contribute to attaining a sustainable environment. Multiple studies have been conducted on the importance of RWNE and ECLF in growing economies [26,27,28,29]. The demand for energy sources is increasing due to the expanding UBNZ. Therefore, every economy must strategizes and allocate additional energy resources to manage the process of the UBNZ. Furthermore, the UBNZ utilizes the existing biocapacity within the terrestrial limits of a country. The UBNZ can have a negative effect on the ECLF. Research has assessed the impact of the UBNZ on the environment [30,31,32]. The impact of RWNE is significantly assessed in the seven growing economies, in addition to NTRR and UBNZ. The impact of RWNE is rarely quantified in E7 economies, despite the influence of government initiatives and the UBNZ.
The E7 countries are a group of seven emerging economies that are gaining significant influence in the global economic landscape. These countries include Turkey, India, Mexico, China, Russia, Brazil, and Indonesia. The E7 stands apart from other groups, such as the G7 or G20, because of its rapid economic growth and significant global influence. While the G7 consists of advanced economies, the E7 represents a shift towards emerging markets, characterized by a focus on industrialization, development, and rising participation in global trade. As these nations continue to grow, they play an instrumental role in influencing global economic trends and are increasingly central to discussions on sustainable development, green growth, and climate change. E7 economies have grown notably and are forecasted to outpace the G7 economies [33]. From 2015 to 2050, 25% of the global economy is expected to be affected. It is expected that they will have an average rate of 3.5%, while the growth rate of the G7 countries will be 1.6%. E7 countries experienced a surge in energy consumption and, subsequently, CO2 emissions, which increased to 41% in 2018 [34]. In addition, with the exponential spread in all areas, NTRR consumption, the generation of renewable energy, the UBNZ process, and the ecological deficit have become issues of concern. Moreover, E7 economies are destabilized by the uncertainty dominating the world. The ECLF in E7 nations has been influenced by high manufacturing rates and has slowly changed to RWNE.
Despite the growing body of literature on environmental sustainability, several research gaps remain unexplored. Specifically, while the relationship between capital formation and the ecological footprint has been studied to some extent, it has not been rigorously examined within the context of E7 countries. Similarly, limited research exists on the interplay between foreign direct investment (FDIN) and the ecological footprint in these economies, despite FDIN’s significant role in shaping environmental quality through industrial and economic activities. Moreover, the integration of variables aligned with the SDGs, particularly Goals 11, 12, and 13, remains underexplored in studies focusing on environmental sustainability in the E7 bloc.
Moreover, this study aims to bridge these gaps by examining the nexus between natural resources, renewable energy, urbanization, capital formation, and foreign direct investment in the E7 countries. Using the PMG-ARDL model, this study provides a robust analysis of both short- and long-term dynamics among these variables, offering valuable insights for policymakers and stakeholders striving to achieve environmental sustainability. Thus, this research not only expands the scope of the literature but also proposes actionable strategies for E7 nations to balance economic growth with environmental conservation. Building on these discussions, this study investigates the following research inquiries:
  • How does the UBNZ influence the environmental quality (EQ) of E7 economies?
  • To what extent does RWNE improve the EQ of E7 economies?
  • What is the role of the CPFR in shaping the EQ of E7 economies?
  • How does FDIN affect the EQ of E7 economies?
  • In what ways does NTRR impact the EQ of E7 economies?
Conducting an empirical reassessment of the ECLF in the region of E7 can enhance and contribute to existing information in the following ways. The initial aim is to examine the practical effects of NTRR, RWNE consumption, UBNZ, FDIN, and CPFR on the ECLF in E7 countries. Moreover, correlations are examined via universally accepted econometric models. The following statistical tests were conducted: the CSD test for cross-sectional dependence, the panel unit root test for a stationarity check, the panel cointegration test, and the PMG ARDL test to examine the long- and short-term associations among the study parameters. The econometric estimations mentioned here are highly recommended and commonly utilized in the current body of literature. The forthcoming sections of the study will include a comprehensive background study and past research, a description of the method and model used, an analysis of the results and discussions, and a conclusion with policy recommendations.

2. Literature Review

2.1. NTRR vs. ECLF

NTRR refers to the revenue from forests, oil, minerals, and natural gas. NTRR is the sum of all earnings from the exploitation of these natural resources. It is obtained by the net difference between the selling price and the cost of production. The problem of global warming has sparked attention to the economics of natural resources. As a result, new fields of economics, including environmental economics, ecological economics, and energy economics, which focus on natural resources from different angles, have begun to develop [35,36]. Furthermore, many previous works have explored the relationship between NTRR and ECLF, and these investigations have shown a positive correlation [37,38]. Nevertheless, [39] reported a negative relationship between ECLF and NTRR in 2020. A sustainable environment in newly industrialized economies has developed through the use of the financial globalization of NTRR and the adoption of RWNE sources [35]. Similarly, a study on the MINT economies revealed that NTRRs are harmful because they cause an increase in CO2 [40]. Ref. [23] checked the variables and impacts of NTRR on CO2. The study showed that the negative impact of NTRR greatly benefited CO2 in China.
The governments of developing nations are highly interested in attracting FDINs to promote industrial output and achieve economic progress, even if it means utilizing NTRR [41]. Developing nations prioritize economic growth, trade expansion, and employment possibilities over environmental concerns during the initial stages of development [42]. However, the issue is exacerbated in the same economies. Ref. [39] conducted a study that analyzed the influence of NTRR on the ECLF in BRICS countries. They discovered that NTRR decreased the ECLF. However, [43] carried out a study that reported opposite results. They reported that, in those same countries, in reality, NTRR increased the ecological footprint. However, the results can differ for several reasons, such as the size of the sample, methodology, duration of the research, and extra variables. Nonetheless, these contradictory results require further research to establish the precise relationship between NTRR and the environmental footprint since previous findings have been inconclusive.
H1. 
NTRR negatively impacts the environmental quality of E7 economies.

2.2. RWNE vs. ECLF

Socioeconomic activities are closely linked since RWNE is a significant factor. Therefore, this study is important for determining the impact of RWNE on the state of the ecosystem. A number of studies have shown that the utilization of nonrenewable sources of energy is destroying the environment, and if this matter is not considered, it can alter the climate [44,45,46,47]. Moreover, RWEEs have been confirmed to improve air quality, as evidenced by studies by [48,49,50,51]. Nevertheless, particular research has recognized a slight impact of RWNE on climate quality [52,53]. Moreover, research conducted by [54] revealed that RWNE is causing an increase in greenhouse gas (GHG) emissions in India. Ref. [55] studied the negative climatic impact of RWNE on European countries. Ref. [56] reported that the removal of regulatory barriers dramatically stimulates harvesters to use biogas plants. To build biogas units, management needs an economic plan, user training, daily operations, and expert technical support. Ref. [57] reported that energy investment has a positive effect on economic development but has negative consequences for the environment. To establish an accurately calibrated energy system, governments should strive to achieve a balance between the various parts of the global energy demand and actively promote policy integration in resource management. Ref. [56] asserted that NTRR causes environmental degradation in Nigeria, Gambia, and Senegal. In other countries, the impact is positive but limited. Furthermore, financial inclusion enhances the pace of Ghana’s ecological influence. The investigation concluded that there is no harm or negative impact on other nations. The investigation did not find any associations among other economies. This paper examines the policy implications, limitations, and areas for future research.
H2. 
RWNE positively impacts the environmental quality of E7 economies.

2.3. CPFR vs. ECLF

A literature search did not reveal any prominent studies that explored the relationship between the CPFR and ECLF. Nevertheless, in [58], the utilization of the NARDL technique revealed a direct correlation between the CPFR and ECLF, as well as a reciprocal link, in Pakistan. Consequently, this study examines the CPFR and its correlation with the ECLF in E7 economies via the PMG-ARDL approach. The analysis revealed significant correlations between the CPFR and various economic variables. The literature has extensively examined the topics of energy consumption and the CPFR in various economies. This is supported by several studies, including those by [59,60,61,62,63,64]. Ref. [65] forecasts the ecological consequences of the CPFR by considering the gross fixed CPFR, which highlights the financial aspect of the overall demand for goods produced and services rendered. Research has shown that the proportion of gross fixed CPFRs in relation to the total carbon footprint varies dramatically across different countries. The authors argued that the carbon footprint of gross capital development is relatively decoupled through the use of a structural deconstruction approach. An analysis of the correlation between sustainable energy use and capital development in the developed financial market in the United Kingdom was conducted by [64]. To reach the objective of the study, data from the period of 1970–2013 were collected via the Zivot–Andrew structural break test, ARDL bounds testing, and Johansen cointegration methods. The results of the study suggest that there is cointegration among the variables under study. A strong link was revealed between the growth of the monetary sector and energy consumption in the UK economy. The results of their analysis suggest that demands for energy in the economy have a beneficial influence on monetary sector development. Numerous studies have investigated the primary impacts of energy consumption, the CPFR, and economic dynamics [66,67,68,69].
H3. 
CPFR negatively impacts the EQ of E7 economies.

2.4. FDIN vs. ECLF

Ref. [70] identified three pathways through which the impact of FDINs on the environment might be explained: the technique effect, scale effect, and income effect. For example, when the technology factor is considered, FDI is expected to improve the quality of the environment by providing assistance for local entrepreneurs who can adopt innovative technologies, such as environmentally sustainable technologies. This is because of the spillover effect generated by FDINs. Studies have shown that FDI spillovers from other regions within the same industry have a negative effect on production technology. This is due to increased rivalry within the industry [71]. When foreign investment is accompanied by the use of green technology, the environmental quality improves. Technology transfer through FDINs is considered a key approach in this process for middle-income economies. This has positive effects on the adoption of renewable energy. The importance of FDI in carrying technology to developing countries cannot be overstated. This could encourage the adoption of clean technologies and more effective management strategies, thereby reducing the use of nonrenewable resources. Many developing countries intentionally use environmental regulations to lure international corporations and stimulate FDINs. They ignore environmental energy efficiency and therefore aggravate environmental issues [72]. FDI can promote GDP and, at the same time, deteriorate the environmental status [73]. Ref. [74] used the AMG estimator in research that covered 20 nations. Their results suggest that there is no major association between FDIN and ECLF. According to [75], in their study of Asian countries from the period of 1975–2017, FDIN had a positive effect on ECLF. They employed ARDL methodology to achieve this result. Ref. [76] study the causal interconnections among FDI, the ECLF, and GDP in five Southeast Asian countries. The outcomes reveal that the study variables have a positive effect on GDP and help increase the ECLF.
H4. 
FDIN positively impacts the EQ of E7 economies.

2.5. UBNZ vs. ECLF

Many studies have analyzed the relationship between urbanization and the environmental footprint, although the results are not consistent [36,39]. A study was conducted by [77] to explore the relationship between the UBNZ and ECLF in MENA countries. Data from the period of 1996–2012 show that the UBNZ impairs the environment by increasing the ECLF. The correlation in [39] was that the UBNZ and ECLF were detrimental among the BRICS states. Ref. [78] researched the impact of the UBNZ on the economic freedom of the G7 countries. The UBNZ is causing a decline in environmental quality. Hence, it is crucial to use environmental technologies in urbanized areas to ensure their long-term sustainability. Ref. [79] discovered that the UBNZ is reducing the ECLF in 110 economies. The study conducted by [80] yielded similar results. In their study, [38] reported that the UBNZ caused a decrease in the ECLF in Pakistan.
H5. 
The UBNZ negatively impacts the environmental quality of E7 economies.

2.6. Literature Gap

After reviewing the literature, this study’s contributions can be summarized as follows. First, capital production is included in the ecological footprint function for the E7 countries. Only a small number of studies have examined the connection between the CPFR and the ecological footprint, and this relationship has not been previously confirmed within the framework of E7 economies. The study variables are derived from the SDGS (11, 12, and 13) agenda, which aims to be accomplished by 2030. This aspect is not commonly found in the existing work. Our study examines several factors, including NTRR, renewable energy, UBNZ, capital creation, and FDIN, that contribute to the improvement of the E7 blocs’ EQ and status. The present literature on environmental economies lacks sufficient documentation on this topic. Economies are increasing their economic output by harnessing both renewable and non-RWNE sources. These economic activities are driving many countries to increase FDIN. These activities pollute the air quality. Minimal research has been conducted to examine the correlation between FDINs and ECLF. This study examines the relationship between FDIN and ECLF in E7 economies. Furthermore, the present study offers a comprehensive and rigorous examination of the PMG-ARDL model in relation to both long- and short-term links between variables. The majority of studies advocate and utilize the PMG-ARDL technique. Ultimately, this study provides a comprehensive plan for E7 countries as they strive to achieve environmental sustainability.

3. Methodology

This study investigated the relationships between ECLF, RWNE, NTRR, CPFR, FDIN, and UBNZ within the E7 economies. The theoretical framework integrates sustainable development (SD) theory, resource dependency (RD) theory, and urban ecology (UE) theory, collectively referred to as the SRU model, to explain the dynamics of these variables. According to RD theory, countries rich in NTRR often exploit these resources for economic growth, which may negatively affect environmental quality by increasing the ECLF [81]. This aligns with our H1 that NTRR negatively impacts environmental quality in E7 economies. The theory also highlights that the efficient management of NTRR could mitigate environmental degradation, which is central to exploring how resource utilization impacts ECLF in our study.
SD theory links economic growth, environmental quality, and social welfare, emphasizing the need for sustainable development that meets current needs without compromising the future. This theory supports H4, which states that FDIN, by fostering green technologies and infrastructure, can positively influence environmental quality. However, if FDINs are directed toward environmentally harmful sectors, they could exacerbate environmental degradation, reinforcing the need for policies that direct investments toward sustainability [82]. UE theory explores the relationship between the UBNZ and environmental quality, suggesting that urbanization can strain environmental resources. While rapid urbanization can lead to a higher ECLF, it also provides an opportunity for efficient resource use and sustainable planning, which can mitigate negative environmental outcomes [83]. This finding supports H5, which states that the UBNZ negatively impacts environmental quality, although with the potential for positive outcomes through green urban planning.
Additionally, RWNE offers a pathway for reducing environmental harm by shifting towards renewable energy sources, which is consistent with our H2 that RWNE positively impacts environmental quality. CPFR, by promoting green technologies and sustainable infrastructure, can further mitigate environmental degradation, aligning with H3 that the CPFR negatively impacts environmental quality. By integrating these theories, this study provided a wide-ranging analysis of the interplay between RWNE, NTRR, CPFR, FDIN, UBNZ, and EQ. The theoretical framework serves as the foundation for understanding the complex relationships that inform sustainable development strategies in the E7 economies.
Prior research has examined the relationships among RWNE, GDP, CO2, and the UBNZ [84]. Refs. [85,86] studied RWNE, real GDP, and CO2 relationships. Ref. [58] studied the relationships among RWNE, CO2, and the UBNZ. Furthermore, [87] undertook a study to assess the relationship between environmental sustainability (ES) and agro-economic performance. To the best of our knowledge, no research has examined the relationship between the specified factors and the ECLF via PMG-ARDL. The PMG-ARDL method was used here for the first time to analyze the effects of NTRR, RWNE, CPFR, UBNZ, and FDIN in the context of a panel data model, which included E7 countries in the period of 1990–2022. The analysis continued by examining CSD, slope homogeneity, variable stationarity, and variable cointegration. The Hausman test was then used to determine whether the right method to be employed is MG, DFE, or PMG for the investigation of the relationships. The data were tested for validity via the FMOLS test. Furthermore, the causality of the parameters was studied to improve the understanding of the interactions and help policy makers make decisions. The Hausman test confirming the appropriateness of the PMG demonstrates the significance of the test in analyzing a particular dataset. The novelty of the PMG-ARDL approach lies in its ability to perform full-scale panel data analysis, which includes general long-run relationships and single-specific short-run dynamics. It was selected after proper tests were applied to verify the issues of cross-sectional dependence, smoothness, and cointegration of variables. In the context of this research, PMG-ARDL was the most applicable approach, as it best captured the specifics of both similarities and differences among players in the BRICS economies. While CS-ARDL works on the basis of individual-specific long-run relationships and may miss commonalities, PMG-ARDL is good at pooling information across entities while satisfying heterogeneity in short-run dynamics. The study model is expressed by the following equations, as proposed by [88]:
E C L F = f N T R R R W N E C P F R U B N Z F D I N
The logarithmic form of the equation is represented in Equation (2):
l E C L F i , t = α + β 1 l N T R R i , t + β 2 l R W N E i , t + β 3 l C P F R i , t + β 4 l U B N Z i , t + β 5 l F D I N i , t + ε i , t
The logarithmic transformation was applied to the variables to maintain a consistent level of variability in the time series. The symbol β represents the intercept term and is assigned values from β1 to β5 to denote the intercept for each variable being analyzed. The study begins by assessing cross-sectional dependence via the [89] CSD test, as outlined in Equation (3):
C D = 2 T N N − 1 ∑ i = 1 n − 1   ∑ j = i + 1 n   𝜕 i j t
The symbol ∂ represents the error of the association. The variables T and N are employed to denote the time and cross-sections, respectively. The CSD test provides information on the interdependence among the countries in the panel and assists in selecting an appropriate cointegration test, such as the Kao or [90] tests. Studies do not choose Kao if the results of the CSD test are significant. In such a case study, [90] is adopted to check for cointegration. Following the CSD test, this work conducted a slope homogeneity check to determine whether the variables are homogenous or heterogeneous. Ref. [91] highlighted the significance of testing slope homogeneity in heterogeneous panel data, as demonstrated in Equation (4):
Δ ~ = N N − 1 S ~ − K 2 K
Δ ~ a d j = N N − 1 S ~ − E Z ~ i T v a r Z ~ i T
After the slope test is confirmed, the unit root test is used to examine the stationarity of the variables under consideration. If there is cross-sectional dependence, studies avoid first-generation unit root tests and employ second-generation CIPS or CADF tests. In this study, unit root test statistics are computed via Equation (6):
C I P S = δ ^ s e δ ^
The CIPS unit root test is a crucial tool in panel data analysis, particularly for testing the unit root of variables. It compares the test statistic against critical values to determine whether the Ho of a unit root can be accepted or rejected. The key advantage of this approach is its ability to account for CSD, a common feature in panel datasets. Unlike traditional unit root tests, the CIPS test offers more robust and accurate inferences by incorporating the cross-sectional structure of the data. This makes it an indispensable method for researchers aiming for reliable results in panel data analysis [92].
Once unit root stationarity among the variables is confirmed, the next step is to conduct a cointegration test. If the results of the CSD test are significant, the Kao cointegration test cannot be used. Therefore, this study adopted the cointegration concept proposed by [90] to overcome the CSD issue. The equations illustrating this concept are depicted below. The study in [90] is particularly useful in cointegration analysis because his method addresses cross-sectional dependence, ensuring more accurate and reliable inference regarding the relationships between variables in panel data models.
G t = 1 N ∑ i = 1 N   𝜕 i ! S E 𝜕 i !
G a = 1 N ∑ i = 1 N   T 𝜕 i ! 𝜕 i ! 1
P t = 𝜕 ! S E 𝜕 ′
𝜕 ! = P a T
The annual correction is denoted by the symbol 𝜕 ! = P a T .
The Hausman specification test is employed to assess the estimators of random and fixed effects, offering useful insights into the suitability of each method [93]. The current study employed this test to determine whether the MG or PMG approach should be used for subsequent analysis. This test is crucial because it ensures that the chosen estimation method aligns with the underlying structure of the data, thereby enhancing the reliability and robustness of the findings. The equation for the Hausman test is presented in Equation (8).
H = β F E − β R E ′ V F E − V R E − 1 β F E − β R E
Hausman’s test statistic Equation (8) follows a chi-squared (χ2) distribution when the Ho is that the variations in coefficients are not systematic. In Equation (8), βFE represent fixed effects, whereas βRE represent random effects of coefficients. The covariance matrices for the fixed effects estimator and random effects estimator are denoted as VFE and VRE, respectively, as defined by [93]. Opting to discard the null hypothesis indicates a favor of the random effects estimator.
With positive results from the Hausman test, the PMG-ARDL is used to study the long- and short-term associations among the variables. The mathematical equation for the PMG-ARDL approach proposed by [94] is shown below:
Δ y i t = A + ∅ y i t − 1 + α i ∑ i = 1 p   Δ y i t − i + π i ∑ i = 1 p   Δ l N T R R i t − i + ω i ∑ i = 1 p   Δ l R W N E i t − i + ψ i ∑ i = 1 p   Δ l C P F R i t − i + Ω i ∑ i = 1 p   Δ l U B N Z i t − i + ¥ i ∑ i = 1 p   Δ l F D I N i t − i + β 1 y i t − 1 + β 2 l N T R R i t − 1 + β 3 l R W N E i t − 1 + β 4 l C P F R i t − 1 + β 5 l U B N Z i t − 1 + β 6 l F D I N i t − 1 + η i + ε i t
In the given model, the dependent variable coefficient is represented by ∅. The short-run coefficients are denoted by α i , π i , ω i , ψ i , Ω i , and ¥, whereas the long-run coefficients are represented by β1 to β6. The cross-validation process was conducted to check robustness via the FMOLS test [95] to assess the accuracy and reliability of the findings from the PMG-ARDL experiment. The consistency between the FMOLS test yields and the PMG-ARDL yields reflects the strength and accuracy of both our model and the findings. This added step improves the credibility of our model by ensuring the coherency of our results between the different analysis approaches. The equation of the FMOLS test is provided below.
γ ~ F M O L S = N − 1 ∑ i = 1 N   ∑ t = 1 t   U i t − U ¯ i 2 − 1 × Σ t = 1 T U i t − U ¯ i S ^ i t − T Δ ^ ϵ μ
This study finally evaluated the causality of the results via the [96] model. This model is used to investigate causal links among variables in panel data settings. It provides researchers the ability to ascertain the direction and magnitude of causality between factors while taking into consideration possible cross-sectional dependence and heterogeneity. This test offers an important instrument for analyzing the dynamics and interrelationships between variables in panel datasets. This test is expressed in the following mathematical form:
y i , t = α i ∑ k = 1 K   γ t k y i , t − k + ∑ k = 1 K   β l k x i , t − k + e i , t
Furthermore, the relationships among the variables and tests employed are shown in Figure 1.
Moreover, Table 1 presents information about the data used in the study. The study used data from GFN, WDI, and WGI, covering variables such as ECLF, CPFR, FDIN, RWNE, NTRR, and UBNZ. These data provide insights into the environmental impact, investment trends, economic globalization, renewable energy, NTRR, and UBNZ for a comprehensive analysis of sustainable development.

4. Results

The descriptive statistics offer a thorough overview of each variable, and the results are presented in Table 2. The mean ECLF per capita across all observations is approximately 2.75, with a median of approximately 2.70. The maximum observed ECLF is approximately 6.88, whereas the minimum is approximately 0.68. The standard deviation is approximately 1.51, indicating variability around the mean, with a skewness of approximately 0.84 and a kurtosis of approximately 3.17, suggesting the presence of outliers or extreme values. The CPFR accounts for approximately 25.74% of the GDP, in the range of 14.39–45.98%, whereas the FDIN accounts for approximately 1.99% of the GDP, in the range of −2.76–6.19%. RWNE has an average consumption of approximately 25.40%, in the range of 3.18–58.65%, and NTRR has an average consumption of approximately 4.72% of GDP, in the range of 0.14–22.00. The UBNZ presents an average urban population of approximately 60.15%, in the range of 25.55–87.76%. These variables demonstrate varying levels of dispersion, with skewness and kurtosis providing insights into their distributional characteristics. Overall, these statistics provide a comprehensive overview, aiding in understanding variability and potential outliers within the dataset.
Figure 2 shows the trends in the ECLF for the E7 countries over the period of 1990–2022. China exhibits a steady increase in ECLF, whereas Russia’s ecological footprint fluctuates but maintains a relatively high level. India and Brazil also show increasing trends, albeit with some fluctuations. Turkey’s ecological footprint increases gradually.
Prior to conducting the PMG-ARDL test, it is necessary to perform specific preliminary tests to verify the robustness and reliability of our data, variables, and their interactions. Thus, our initial step is to examine the multicollinearity and presence of CSD among the variables. Additionally, the dataset’s multiple correlations were assessed via the more robust and effective variance inflation factor (VIF) test method. Table 3 shows the outcomes of the VIF test. The results indicate the absence of multicollinearity in the dataset. All values are less than 5, indicating the absence of multicollinearity in the dataset.
To verify the CSD among the E7 nations, the study employed the [97] test. The CSD outcomes are displayed in Table 4. The probability values clearly indicate the presence of CSD among the E7 countries. The presence of CSD among the E7 countries suggests that the ECLF of one country is influenced by the footprints of others within the group. This implies the existence of interdependencies.
Once the cross-sectional dependence was confirmed, the subsequent step was to examine slope homogeneity. The results of the slope test are displayed in Table 5, indicating a lack of homogeneity. Both the test results for the delta and adjusted delta are statistically significant at the 1% level, which indicates the rejection of the null hypothesis for the slope homogeneity. This implies that the connections between variables among various entities are not uniform.
After confirming the heterogeneity of the variables through the slope test, the next step is to conduct the second-generation CIPS unit root test. This test was chosen because of the presence of CSD identified earlier. Importantly, the Levin–Lin-Chu (LLC) test is not suitable in cases where there is CSD in the data, hence the preference for the CIPS over the LLC. Additionally, the augmented Dickey–Fuller (ADF) test might not capture the intricacies associated with CSD. The CIPS is preferred for its strength in dealing with CSD and producing appropriate unit root testing in panel datasets. The outcomes of the CIPS unit root test are presented in Table 6. At the I(0) level, FDIN remains at the 1% significance level, and ECLF, RWNE, and CPFR are significant at the 5% level. However, NTRR and UBNZ are not stationary I(0). However, all the variables under study are I(1) differenced and stationary. This implies that some variables may not be stationary at their original levels, but they are stationary after differencing, which means that the variables will have a stable long-term relationship with each other.
Furthermore, it is important to verify the cointegration of the variables before running the PMG-ARDL analysis. To check this, the study used the [90] test, a test that is widely used in panel data analysis. The cointegration test outcomes, shown in Table 7, support the cointegration between the variables to be studied. Notably, the values of Gt, Ga, Pt, and Pa are all significant at the 1% level, showing a strong cointegration among the variables. This result supports the application of the PMG-ARDL method, as it starts from the assumption that the variables are cointegrated, thus allowing for the correct estimation of dynamic relationships in panel datasets.
The study first considered the appropriate estimator to use before adopting the PMG-ARDL. To conduct this, the Hausman test, which is critical in panel data analysis, aimed at determining whether the fixed effects (DFE), random effects (MGs), or pooled mean group (PMG) estimator should be used. The Hausman test results in Table 8 do not yield a significant probability value. Therefore, the work concludes that PMG is the best method for our analysis, as opposed to the DFE and MG estimators. The PMG method provides various benefits, such as the ability to model both individual-specific and time-specific effects and heterogeneous slopes across panels. The PMG estimator is also a good helper in overcoming endogeneity and serial correlation problems that guarantee that the parameter estimates in panel data settings are both valid and efficient. Owing to these characteristics, the PMG-ARDL method is successful in capturing the complex dynamics and interrelationships of the data at hand. This increases the power of our empirical results.
After confirming the Hausman test results, the PMG approach is found to be a suitable method for examining the relationships among the study variables. Hence, our work utilized PMG-ARDL, and the outcomes are presented in Table 9.

5. Discussions

The output ECT was in line with the requirement, which is both significant and negative. Ultimately, the CPFR is significantly positively correlated with the ECLF, with a coefficient of 0.054. This indicates that 1% growth in the CPFR will cause 0.054% growth in the ECLF. This positive relationship suggests that the growth of the CPFR could lead to a higher ECLF in the near future because of increased economic activity and the consumption of resources. Our findings are consistent with the works of [98,99,100]. Furthermore, this result also validates H3 of the current study. On the other hand, FDIN has a negative correlation with ECLF; this holds in both the long and short run. In the long term, 1% FDIN growth is linked to a 0.056% decrease in the ECLF, whereas in the short term, it is 0.0301%. This implies that, with the increase in FDIN, the ECLF tends to decrease. Another possible reason is that high levels of FDINs may result in the development of better technologies and environmental regulations. This leads to a decrease in the environmental impact. These results are consistent with the results obtained by [36,74,101]. This result also validates H4 of the current study. The long-term relationship between RWNE and ECLF is negative, with a coefficient of −0.021. This shows that a 1% rise in RWNE consumption is linked with a 0.021% decrease in the ECLF in the long run. Similarly, the short-term coefficient of RWNE is −0.0471, which means that an increase in RWNE consumption leads to a short-term decrease in ECLF. This negative connotation implies that the greater utilization of RWNE sources might help reduce the ECLF, in turn, by lowering emissions and resource consumption. Our results are in line with the results of [44,49,50,102,103]. This result also supports H2 of the current study. The regression coefficient of NTRR on ECLF is equal to 0.0411 in the long term. This means that a 1% increase in the use of NTRR results in a 0.0411% increase in the ECLF in the long run. Nevertheless, a small coefficient of NTRR (0.0074) indicates that, in the short term, natural resource utilization has a minimal effect on the ECLF. This enabling relationship means that the greater utilization of NTRR leads to an increase in the ECLF with time. The results are in line with the results of [83,104]. This result also supports H1. In the long and short run, the UBNZ has a positive and strong relationship with the ECLF. However, in the short run, this increase is 0.0931%. This implies that high UBNZ levels are associated with high ECLF, which may be due to the high resource use and waste production associated with city life. The results are in line with those of [22,36,105,106]. This outcome supports H5 of the current study. Figure 3 presents a summary of the findings, showing the relationships between NTRR, CPFR, UBNZ, RWNE, FDIN, and ECLF. These findings indicate that NTRR, CPFR, and UBNZ positively contribute to ecological degradation, whereas RWNE and FDIN promote ecological sustainability.
To ensure the reliability of our findings, this work conducted FMOLS tests as a robustness check on our results obtained from the PMG-ARDL analysis. The FMOLS results, as shown in Table 10, closely mirror those obtained from the PMG-ARDL approach. Specifically, CPFR, NTRR, and UBNZ exhibit positive and statistically significant relationships with ECLF, whereas RWNE and FDIN show negative associations with ECLF. These consistent findings between the two methods indicate the robustness and reliability of our results. This alignment underscores the validity of our conclusions and provides further confidence in the relationships identified between the variables under study.
The causality among the variables was tested via the Dumitrescu–Hurlin causality test, and the results are presented in Table 11. The findings reveal a noteworthy pattern: ECLF and UBNZ depict bidirectional causality, pointing to feedback links between the two variables. On the other hand, the other variables, CPFR, FDIN, NTRR, and RWNE, have unidirectional relationships with the ECLF. In particular, CPFR, FDIN, NTRR, and RWNE are found to have considerable causal impacts on the ECLF, demonstrating the one-way influence of these factors on the ECLF.
This detailed inspection of causality offers intriguing perspectives on the mechanics and correlations among the variables being analyzed, thereby further elaborating on their influence on ECLF.

6. Conclusions and Policy Implications

The importance of addressing environmental degradation has become more urgent in recent years. The lack of sufficient regulations and practices in this area can be disastrous for the economy, human life, and our very existence. The cooperative approach, which involves the participation of governmental and nongovernmental organizations, is one of the critical ways to eliminate the degradation of the environment and ensure the sustainability of our ecosystems. This integrated approach is also needed to protect the environment and ensure a better future for the next generations. To address such disputes and recommend appropriate measures to address this issue, this article focused on E7 economies. This study examined the short- and long-term correlations between FDIN, NTRR, UBNZ, RWNE, and CPFR and their impact on the environmental quality of E7 economies, measured through ECLF. Resource dependence theory was employed to provide theoretical justification for the findings. The analysis used the PMG-ARDL approach, leveraging data from the period of 1990–2022 to explore these relationships. To confirm the robustness of the outcomes, the study employed the FMOLS technique, which confirms the reliability of the findings.
The findings indicate that CPFR, NTRR, and UBNZ are positively associated with ECLF, suggesting that these variables negatively affect the environmental quality of the E7 nations. According to the FMOLS results, a 1% increase in CPFR, NTRR, and UBNZ results in increases of 0.0581%, 0.0263%, and 0.0299%, respectively, in the ECLF. Similarly, the PMG-ARDL results show that a 1% increase in CPFR, NTRR, and UBNZ elevates the ECLF by 0.0544%, 0.0411%, and 0.0301%, respectively. These findings align with H1, H3, and H5. Conversely, FDIN and RWNE are negatively related to ECLF, indicating their potential to mitigate environmental pressures. A 1% increase in RWNE and FDIN reduces the ECLF by 0.0207% and 0.0556%, respectively. These results are consistent with H2 and H4.
The results illustrate a complex relationship between economic activities and environmental sustainability, emphasizing the need for _targeted policies to reduce the ECLF and promote sustainable development. However, the conclusions should be understood within the limitations of this study, including its focus on E7 nations and specific variables. While these findings provide valuable insights, they do not suggest the universal applicability of the management model. Future research should expand the scope to additional regions and sectors to validate and generalize the results.
Employing specific policy measures is critical for addressing environmental degradation and reducing ECLF in E7 economies. The results indicate that CPFR, NTRR, and UBNZ have an adverse effect, whereas FDIN and RWNE have potential for improvement. Given the positive associations between CPFR, NTRR, and UBNZ with ECLF, policy makers should focus on environmental mitigation measures for these variables. This might involve the introduction of strict regulations and rewards to help industries related to the CPFR, natural resource extraction, and urban development adopt sustainable practices. In addition, support for green urban planning, resource management solutions, and RWNE projects can reduce the environmental damage linked to these factors. In contrast, the negative relationship between FDIN, RWNE, and ECLF implies possibilities for utilizing FDIN and RWNE capacity to improve environmental sustainability. Policymakers could motivate green investments, technology transfers, and RWNE projects to decrease carbon emissions and ECLF in the E7 economies. A multisectoral approach, which involves regulatory measures, incentives, and investments in sustainable practices and technologies, is critical. Partnerships between public and nongovernmental organizations, as well as international collaboration, play a cardinal role in addressing environmental issues and guarantee the perpetuation of a quality life in the future.
This study contributes significantly to the literature by investigating the relationships among FDIN, NTRR, RWNE, CPFR, and UBNZ with ECLF in E7 countries, utilizing the PMG-ARDL. The findings highlight practical implications for policymakers, such as the need to regulate foreign investment and resource utilization while promoting renewable energy and sustainable urban development to achieve environmental balance.
Despite its contributions, this study has several limitations that present opportunities for further research. Expanding the scope to include variables such as economic complexity, financial development, and human capital could enhance understanding. Future studies might also employ advanced econometric techniques, such as CS-ARDL or MMQR, and explore different panel structures or time intervals to gain deeper insights. Additionally, examining policies that foster green investments and sustainable energy use could offer actionable strategies for balancing economic growth with environmental sustainability. These efforts would advance the broader understanding of sustainable development dynamics.

Author Contributions

Z.W.: Conceptualization; Data curation; Formal analysis; Writing—review & editing; R.X.: Conceptualization; Data curation; Formal analysis; Writing—original draft; Writing—review & editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

List of Abbreviations

AbbreviationFull form
NTRRNatural resource extractions
RWNERenewable energy
CPFRCapital formation
UBNZUrbanization
FDINForeign direct investment
ECLFEcological footprint
SDGsSustainable development goals
EQEnvironmental quality
CO2Carbon dioxide
EKCEnvironmental Kuznets curve
R&DResearch and development
ICTInformation and communication technologies
CSDCross-sectional dependence
LLCLavin–Lin–Chu
ADFAugmented Dickey–Fuller

References

  1. Chen, D.; Ma, X.; Mu, H.; Li, P. The Inequality of Natural Resources Consumption and Its Relationship with the Social Development Level Based on the Ecological Footprint and the HDI. J. Environ. Assess. Policy Manag. 2010, 12, 69–86. [Google Scholar] [CrossRef]
  2. Uzar, U.; Eyuboglu, K. Do Natural Resources Heal the Environment? Empirical Evidence from Turkey. Air Qual. Atmos. Health 2021, 14, 37–46. [Google Scholar] [CrossRef]
  3. Eufrasio Espinosa, R.M.; Lenny Koh, S.C. Forecasting the Ecological Footprint of G20 Countries in the next 30 Years. Sci. Rep. 2024, 14, 8298. [Google Scholar] [CrossRef]
  4. Saqib, N.; Usman, M. Are Technological Innovations and Green Energy Prosperity Swiftly Reduce Environmental Deficit in China and United States? Learning from Two Sides of Environmental Sustainability. Energy Rep. 2023, 10, 1672–1687. [Google Scholar] [CrossRef]
  5. Burki, U.; Tahir, M. Determinants of Environmental Degradation: Evidenced-Based Insights from ASEAN Economies. J. Environ. Manag. 2022, 306, 114506. [Google Scholar] [CrossRef]
  6. Katircioglu, S.; Katircioĝlu, S.; Saqib, N. Does Higher Education System Moderate Energy Consumption and Climate Change Nexus? Evidence from a Small Island. Air Qual. Atmos. Health 2020, 13, 153–160. [Google Scholar] [CrossRef]
  7. Grossman, G.M.; Krueger, A.B. Economic Growth and the Environment. Q. J. Econ. 1995, 110, 353–377. [Google Scholar] [CrossRef]
  8. Dinda, S.; Coondoo, D.; Pal, M. Air Quality and Economic Growth: An Empirical Study. Ecol. Econ. 2000, 34, 409–423. [Google Scholar] [CrossRef]
  9. Uzar, U. More Press Freedom More Renewable Energy: Analysis for High Income OECD Countries. J. Clean. Prod. 2023, 428, 139386. [Google Scholar] [CrossRef]
  10. Uddin, G.A.; Salahuddin, M.; Alam, K.; Gow, J. Ecological Footprint and Real Income: Panel Data Evidence from the 27 Highest Emitting Countries. Ecol. Indic. 2017, 77, 166–175. [Google Scholar] [CrossRef]
  11. Ahmad, M.; Ahmed, Z.; Majeed, A.; Huang, B. An Environmental Impact Assessment of Economic Complexity and Energy Consumption: Does Institutional Quality Make a Difference? Environ. Impact Assess. Rev. 2021, 89, 114–149. [Google Scholar] [CrossRef]
  12. Ulucak, R.; Apergis, N. Does Convergence truly Matter for the Environment? An Application Based on Club Convergence and on the Ecological Footprint Concept for the EU Countries. Environ. Sci. Policy 2018, 80, 21–27. [Google Scholar] [CrossRef]
  13. Langnel, Z.; Amegavi, G.B.; Donkor, P.; Mensah, J.K. Income Inequality, Human Capital, Natural Resource Abundance, and Ecological Footprint in ECOWAS Member Countries. Resour. Policy 2021, 74, 102–137. [Google Scholar] [CrossRef]
  14. Uzar, U.; Eyuboglu, K. Does Income Inequality Increase the Ecological Footprint in the US: Evidence from FARDL Test? Environ. Sci. Pollut. Res. 2023, 30, 9514–9529. [Google Scholar] [CrossRef]
  15. Solarin, S.A.; Bello, M.O. Persistence of Policy Shocks to an Environmental Degradation Index: The Case of Ecological Footprint in 128 Developed and Developing Countries. Ecol. Indic. 2018, 89, 35–44. [Google Scholar] [CrossRef]
  16. Li, X.; Xiao, L.; Tian, C.; Zhu, B.; Chevallier, J. Impacts of the Ecological Footprint on Sustainable Development: Evidence from China. J. Clean. Prod. 2022, 352, 137–153. [Google Scholar] [CrossRef]
  17. Ahmed, Z.; Ahmad, M.; Rjoub, H.; Kalugina, O.A.; Hussain, N. Economic Growth, Renewable Energy Consumption, and Ecological Footprint: Exploring the Role of Environmental Regulations and Democracy in Sustainable Development. Sustain. Dev. 2022, 30, 595–605. [Google Scholar] [CrossRef]
  18. Doytch, N. The Impact of Foreign Direct Investment on the Ecological Footprints of Nations. Environ. Sustain. Indic. 2020, 8, 100085. [Google Scholar] [CrossRef]
  19. Asteriou, D.; Dimelis, S.; Moudatsou, A. Globalization and Income Inequality: A Panel Data Econometric Approach for the EU27 Countries. Econ. Model. 2014, 36, 592–599. [Google Scholar] [CrossRef]
  20. United Nations. United Nations World Urbanization Prospects-Population Division; United Nations: New York, NY, USA, 2019. [Google Scholar]
  21. Yu, J.; Shi, X.; Guo, D.; Yang, L. Economic Policy Uncertainty (EPU) and Firm Carbon Emissions: Evidence Using a China Provincial EPU Index. Energy Econ. 2021, 94, 105071. [Google Scholar] [CrossRef]
  22. Xu, P.; Hussain, M.; Ye, C.; Wang, J.; Wang, C.; Geng, J.; Liu, Y.; Chen, J. Natural Resources, Economic Policies, Energy Structure, and Ecological Footprints’ Nexus in Emerging Seven Countries. Resour. Policy 2022, 77, 102747. [Google Scholar] [CrossRef]
  23. Ling, G.; Razzaq, A.; Guo, Y.; Fatima, T.; Shahzad, F. Asymmetric and Time-Varying Linkages between Carbon Emissions, Globalization, Natural Resources and Financial Development in China. Environ. Dev. Sustain. 2021, 24, 6702–6730. [Google Scholar] [CrossRef]
  24. Dogan, E.; Altinoz, B.; Tzeremes, P. The Analysis of ‘Financial Resource Curse’ Hypothesis for Developed Countries: Evidence from Asymmetric Effects with Quantile Regression. Resour. Policy 2020, 68, 101773. [Google Scholar] [CrossRef]
  25. Hussain, M.; Dogan, E. The Role of Institutional Quality and Environment-Related Technologies in Environmental Degradation for BRICS. J. Clean. Prod. 2021, 304, 127059. [Google Scholar] [CrossRef]
  26. Danish; Ulucak, R. Renewable Energy, Technological Innovation and the Environment: A Novel Dynamic Auto-Regressive Distributive Lag Simulation. Renew. Sustain. Energy Rev. 2021, 150, 111433. [Google Scholar] [CrossRef]
  27. Mehmood, U. Biomass Energy Consumption and Its Impacts on Ecological Footprints: Analysing the Role of Globalization and Natural Resources in the Framework of EKC in SAARC Countries. Environ. Sci. Pollut. Res. 2022, 29, 17513–17519. [Google Scholar] [CrossRef]
  28. Yang, Q.; Du, Q.; Razzaq, A.; Shang, Y. How Volatility in Green Financing, Clean Energy, and Green Economic Practices Derive Sustainable Performance through ESG Indicators? A Sectoral Study of G7 Countries. Resour. Policy 2022, 75, 102526. [Google Scholar] [CrossRef]
  29. Zeraibi, A.; Balsalobre-Lorente, D.; Murshed, M. The Influences of Renewable Electricity Generation, Technological Innovation, Financial Development, and Economic Growth on Ecological Footprints in ASEAN-5 Countries. Environ. Sci. Pollut. Res. 2021, 28, 51003–51021. [Google Scholar] [CrossRef]
  30. Du, K.; Li, P.; Yan, Z. Do Green Technology Innovations Contribute to Carbon Dioxide Emission Reduction? Empirical Evidence from Patent Data. Technol. Forecast. Soc. Change 2019, 146, 297–303. [Google Scholar] [CrossRef]
  31. Du, L.; Wei, C.; Cai, S. Economic Development and Carbon Dioxide Emissions in China: Provincial Panel Data Analysis. China Econ. Rev. 2012, 23, 371–384. [Google Scholar] [CrossRef]
  32. Ozturk, I.; Al-Mulali, U.; Saboori, B. Investigating the Environmental Kuznets Curve Hypothesis: The Role of Tourism and Ecological Footprint. Environ. Sci. Pollut. Res. 2016, 23, 1916–1928. [Google Scholar] [CrossRef] [PubMed]
  33. Gövdeli, T. E7 Ülkelerinde Yaşam Beklentisi, Doğrudan Yabanci Yatirimlar, Dişa Açiklik Ve Ekonomik Büyüme: Heterojen Panel Analizi. Makal. Sist. 2019, 54, 731–743. [Google Scholar] [CrossRef]
  34. BP. BP Statistical Review of World Energy; BP: London, UK, 2018. [Google Scholar]
  35. Miao, Y.; Razzaq, A.; Adebayo, T.S.; Awosusi, A.A. Do Renewable Energy Consumption and Financial Globalization Contribute to Ecological Sustainability in Newly Industrialized Countries? Renew. Energy 2022, 187, 688–697. [Google Scholar] [CrossRef]
  36. Zafar, M.W.; Zaidi, S.A.H.; Khan, N.R.; Mirza, F.M.; Hou, F.; Kirmani, S.A.A. The Impact of Natural Resources, Human Capital, and Foreign Direct Investment on the Ecological Footprint: The Case of the United States. Resour. Policy 2019, 63, 101428. [Google Scholar] [CrossRef]
  37. Awosusi, A.A.; Adebayo, T.S.; Altuntaş, M.; Agyekum, E.B.; Zawbaa, H.M.; Kamel, S. The Dynamic Impact of Biomass and Natural Resources on Ecological Footprint in BRICS Economies: A Quantile Regression Evidence. Energy Rep. 2022, 2022, 1979–1994. [Google Scholar] [CrossRef]
  38. Hassan, S.T.; Xia, E.; Khan, N.H.; Shah, S.M.A. Economic Growth, Natural Resources, and Ecological Footprints: Evidence from Pakistan. Environ. Sci. Pollut. Res. 2019, 26, 2929–2938. [Google Scholar] [CrossRef]
  39. Danish; Ulucak, R.; Khan, S.U.D. Determinants of the Ecological Footprint: Role of Renewable Energy, Natural Resources, and Urbanization. Sustain. Cities Soc. 2020, 54, 101996. [Google Scholar] [CrossRef]
  40. Aziz, N.; Sharif, A.; Raza, A.; Jermsittiparsert, K. The Role of Natural Resources, Globalization, and Renewable Energy in Testing the EKC Hypothesis in MINT Countries: New Evidence from Method of Moments Quantile Regression Approach. Environ. Sci. Pollut. Res. 2021, 28, 13454–13468. [Google Scholar] [CrossRef]
  41. Ahmed, Z.; Asghar, M.M.; Malik, M.N.; Nawaz, K. Moving towards a Sustainable Environment: The Dynamic Linkage between Natural Resources, Human Capital, Urbanization, Economic Growth, and Ecological Footprint in China. Resour. Policy 2020, 67, 101677. [Google Scholar] [CrossRef]
  42. Wu, D.; Geng, Y.; Pan, H. Whether Natural Gas Consumption Bring Double Dividends of Economic Growth and Carbon Dioxide Emissions Reduction in China? Renew. Sustain. Energy Rev. 2021, 137, 110635. [Google Scholar] [CrossRef]
  43. Nathaniel, S.; Nwodo, O.; Sharma, G.; Shah, M. Renewable Energy, Urbanization, and Ecological Footprint Linkage in CIVETS. Environ. Sci. Pollut. Res. 2020, 27, 19616–19629. [Google Scholar] [CrossRef]
  44. Abou Houran, M.; Mehmood, U. How Institutional Quality and Renewable Energy Interact with Ecological Footprints: Do the Human Capital and Economic Complexity Matter in the Next Eleven Nations? Environ. Sci. Pollut. Res. 2023, 356, 201–223. [Google Scholar] [CrossRef]
  45. Alper, A.E.; Alper, F.O.; Ozayturk, G.; Mike, F. Testing the Long-Run Impact of Economic Growth, Energy Consumption, and Globalization on Ecological Footprint: New Evidence from Fourier Bootstrap ARDL and Fourier Bootstrap Toda–Yamamoto Test Results. Environ. Sci. Pollut. Res. 2023, 30, 42873–42888. [Google Scholar] [CrossRef]
  46. Khan, I.; Han, L.; Bibi, R.; Khan, H. Linking Natural Resources, Innovations, and Environment in the Belt and Road Initiative Countries Using Dynamic Panel Techniques: The Role of Innovations and Renewable Energy Consumption. Environ. Sci. Pollut. Res. 2022, 29, 59666–59675. [Google Scholar] [CrossRef]
  47. Li, R.; Wang, X.; Wang, Q. Does Renewable Energy Reduce Ecological Footprint at the Expense of Economic Growth? An Empirical Analysis of 120 Countries. J. Clean. Prod. 2022, 346, 131207. [Google Scholar] [CrossRef]
  48. Sharma, R.; Sinha, A.; Kautish, P. Does Renewable Energy Consumption Reduce Ecological Footprint? Evidence from Eight Developing Countries of Asia. J. Clean. Prod. 2021, 285, 124867. [Google Scholar] [CrossRef]
  49. Xu, Y.; Li, S.; Zhou, X.; Shahzad, U.; Zhao, X. How Environmental Regulations Affect the Development of Green Finance: Recent Evidence from Polluting Firms in China. Renew. Energy 2022, 189, 917–926. [Google Scholar] [CrossRef]
  50. Samour, A.; Baskaya, M.M.; Tursoy, T. The Impact of Financial Development and FDI on Renewable Energy in the UAE: A Path towards Sustainable Development. Sustainability 2022, 1208, 1208. [Google Scholar] [CrossRef]
  51. Yasmeen, R.; Zhaohui, C.; Hassan Shah, W.U.; Kamal, M.A.; Khan, A. Exploring the Role of Biomass Energy Consumption, Ecological Footprint through FDI and Technological Innovation in B&R Economies: A Simultaneous Equation Approach. Energy 2022, 244, 122703. [Google Scholar] [CrossRef]
  52. Pata, U.K.; Samour, A. Do Renewable and Nuclear Energy Enhance Environmental Quality in France? A New EKC Approach with the Load Capacity Factor. Prog. Nucl. Energy 2022, 149, 104249. [Google Scholar] [CrossRef]
  53. Xue, C.; Shahbaz, M.; Ahmed, Z.; Ahmad, M.; Sinha, A. Clean Energy Consumption, Economic Growth, and Environmental Sustainability: What Is the Role of Economic Policy Uncertainty? Renew. Energy 2022, 184, 899–907. [Google Scholar] [CrossRef]
  54. Sahoo, M.; Sahoo, J. Effects of Renewable and Non-Renewable Energy Consumption on CO2 Emissions in India: Empirical Evidence from Disaggregated Data Analysis. J. Public. Aff. 2022, 22, 37–49. [Google Scholar] [CrossRef]
  55. Bölük, G.; Mert, M. Fossil & Renewable Energy Consumption, GHGs (Greenhouse Gases) and Economic Growth: Evidence from a Panel of EU (European Union) Countries. Energy 2014, 74, 439–446. [Google Scholar] [CrossRef]
  56. Ali, K.; Jianguo, D.; Kirikkaleli, D. Modelling the Natural Resources and Financial Inclusion on Ecological Footprint: The Role of Economic Governance Institutions. Evidence from ECOWAS Economies. Resour. Policy 2022, 79, 103115. [Google Scholar] [CrossRef]
  57. Khan, I.; Zakari, A.; Zhang, J.; Dagar, V.; Singh, S. A Study of Trilemma Energy Balance, Clean Energy Transitions, and Economic Expansion in the Midst of Environmental Sustainability: New Insights from Three Trilemma Leadership. Energy 2022, 248, 123619. [Google Scholar] [CrossRef]
  58. Baz, K.; Xu, D.; Ali, H.; Ali, I.; Khan, I.; Khan, M.M.; Cheng, J. Asymmetric Impact of Energy Consumption and Economic Growth on Ecological Footprint: Using Asymmetric and Nonlinear Approach. Sci. Total Environ. 2020, 718, 137364. [Google Scholar] [CrossRef]
  59. Georgantopoulos, A. Electricity Consumption and Economic Growth: Analysis and Forecasts Using VAR/VEC Approach for Greece with Capital Formation. Int. J. Energy Econ. Policy 2012, 2, 37–53. [Google Scholar]
  60. Inglesi-Lotz, R. The Impact of Renewable Energy Consumption to Economic Growth: A Panel Data Application. Energy Econ. 2016, 53, 58–63. [Google Scholar] [CrossRef]
  61. Kum, H.; Ocal, O.; Aslan, A. The Relationship among Natural Gas Energy Consumption, Capital and Economic Growth: Bootstrap-Corrected Causality Tests from G-7 Countries. Renew. Sustain. Energy Rev. 2012, 16, 2361–2365. [Google Scholar] [CrossRef]
  62. Li, R.; Leung, G.C.K. The Relationship between Energy Prices, Economic Growth and Renewable Energy Consumption: Evidence from Europe. Energy Rep. 2021, 7, 1712–1719. [Google Scholar] [CrossRef]
  63. Ouédraogo, I.M. Electricity Consumption and Economic Growth in Burkina Faso: A Cointegration Analysis. Energy Econ. 2010, 32, 524–531. [Google Scholar] [CrossRef]
  64. Rafindadi, A.A.; Mika’Ilu, A.S. Sustainable Energy Consumption and Capital Formation: Empirical Evidence from the Developed Financial Market of the United Kingdom. Sustain. Energy Technol. Assess. 2019, 35, 265–277. [Google Scholar] [CrossRef]
  65. Södersten, C.J.; Wood, R.; Hertwich, E.G. Environmental Impacts of Capital Formation. J. Ind. Ecol. 2018, 22, 55–67. [Google Scholar] [CrossRef]
  66. Cao, M.; Kang, W.; Cao, Q.; Sajid, M.J. Estimating Chinese Rural and Urban Residents’ Carbon Consumption and Its Drivers: Considering Capital Formation as a Productive Input. Environ. Dev. Sustain. 2020, 22, 5443–5464. [Google Scholar] [CrossRef]
  67. Kamran, H.W.; Haseeb, M.; Nguyen, V.C.; Nguyen, T.T. Climate Change and Bank Stability: The Moderating Role of Green Financing and Revewable Energy Consumption in ASEAN. Talent. Dev. Excell. 2020, 12, 136–171. [Google Scholar]
  68. Olopade, B.C.; Okodua, H.; Oladosun, M.; Matthew, O.; Urhie, E.; Osabohien, R.; Adediran, O.; Johnson, O.H. Economic Growth, Energy Consumption and Human Capital Formation: Implication for Knowledge-Based Economy. Int. J. Energy Econ. Policy 2020, 10, 37–43. [Google Scholar] [CrossRef]
  69. Rahman, Z.U.; Ahmad, M. Modelling the Relationship between Gross Capital Formation and CO2 (a)Symmetrically in the Case of Pakistan: An Empirical Analysis through NARDL Approach. Environ. Sci. Pollut. Res. 2019, 26, 8111–8124. [Google Scholar] [CrossRef]
  70. Jiang, Y. Foreign Direct Investment, Pollution, and the Environmental Quality: A Model with Empirical Evidence from the Chinese Regions. Int. Trade J. 2015, 29, 212–227. [Google Scholar] [CrossRef]
  71. Driffield, N.; Taylor, K. FDI and the Labour Market: A Review of the Evidence and Policy Implications. Oxf. Rev. Econ. Policy 2000, 16, 201–236. [Google Scholar] [CrossRef]
  72. Yao, X.; Yasmeen, R.; Li, Y.; Hafeez, M.; Padda, I.U.H. Free Trade Agreements and Environment for Sustainable Development: A Gravity Model Analysis. Sustainability 2019, 11, 597. [Google Scholar] [CrossRef]
  73. Chandran, V.G.R.; Tang, C.F. The Impacts of Transport Energy Consumption, Foreign Direct Investment and Income on CO2 Emissions in ASEAN-5 Economies. Renew. Sustain. Energy Rev. 2013, 24, 445–453. [Google Scholar] [CrossRef]
  74. Solarin, S.A.; Al-Mulali, U. Influence of Foreign Direct Investment on Indicators of Environmental Degradation. Environ. Sci. Pollut. Res. 2018, 25, 24845–24859. [Google Scholar] [CrossRef]
  75. Sabir, S.; Gorus, M.S. The Impact of Globalization on Ecological Footprint: Empirical Evidence from the South Asian Countries. Environ. Sci. Pollut. Res. 2019, 26, 33387–33398. [Google Scholar] [CrossRef]
  76. Sheng Yin, X.; Hussain, J. The Implication of Technological Innovation and Tourism Development on FDI-Growth-Environment Nexus in Association of Southeast Asian Countries: A Simultaneity Modelling Analysis. Energy Sources Part B Econ. Plan. Policy 2021, 16, 878–902. [Google Scholar] [CrossRef]
  77. Al-Mulali, U.; Ozturk, I. The Effect of Energy Consumption, Urbanization, Trade Openness, Industrial Output, and the Political Stability on the Environmental Degradation in the MENA (Middle East and North African) Region. Energy 2015, 84, 382–389. [Google Scholar] [CrossRef]
  78. Ahmed, Z.; Zafar, M.W.; Ali, S. Danish Linking Urbanization, Human Capital, and the Ecological Footprint in G7 Countries: An Empirical Analysis. Sustain. Cities Soc. 2020, 55, 102064. [Google Scholar] [CrossRef]
  79. Yasin, I.; Ahmad, N.; Chaudhary, M.A. The Impact of Financial Development, Political Institutions, and Urbanization on Environmental Degradation: Evidence from 59 Less-Developed Economies. Environ. Dev. Sustain. 2021, 23, 6698–6721. [Google Scholar] [CrossRef]
  80. Nathaniel, S.P.; Iheonu, C.O. Carbon Dioxide Abatement in Africa: The Role of Renewable and Non-Renewable Energy Consumption. Sci. Total Environ. 2019, 679, 337–345. [Google Scholar] [CrossRef]
  81. Hassan, S.T.; Xia, E.; Huang, J.; Khan, N.H.; Iqbal, K. Natural Resources, Globalization, and Economic Growth: Evidence from Pakistan. Environ. Sci. Pollut. Res. 2019, 26, 15527–15534. [Google Scholar] [CrossRef]
  82. Shahbaz, M.; Sinha, A. Environmental Kuznets Curve for CO2 Emissions: A Literature Survey. J. Econ. Stud. 2019, 46, 106–168. [Google Scholar] [CrossRef]
  83. Wang, Y.; Shinwari, R.; Haseeb Payab, A.; Feng, J. Harmonizing Sustainability: Unveiling the Nexus of Public Private Investment, Natural Resources, and Environmental Dynamics by Applying ARDL and Machine Learning Approach. Ecol. Indic. 2024, 161, 111931. [Google Scholar] [CrossRef]
  84. Bosah, C.P.; Li, S.; Ampofo, G.K.M.; Liu, K. Dynamic Nexus between Energy Consumption, Economic Growth, and Urbanization with Carbon Emission: Evidence from Panel PMG-ARDL Estimation. Environ. Sci. Pollut. Res. 2021, 28, 61201–61212. [Google Scholar] [CrossRef] [PubMed]
  85. Acheampong, A.O. Economic Growth, CO2 Emissions and Energy Consumption: What Causes What and Where? Energy Econ. 2018, 74, 677–692. [Google Scholar] [CrossRef]
  86. Chontanawat, J. Relationship between Energy Consumption, CO2 Emission and Economic Growth in ASEAN: Cointegration and Causality Model. Energy Rep. 2020, 6, 660–665. [Google Scholar] [CrossRef]
  87. Adedoyin, F.F.; Alola, A.A.; Bekun, F.V. The Nexus of Environmental Sustainability and Agro-Economic Performance of Sub-Saharan African Countries. Heliyon 2020, 6, 4878. [Google Scholar] [CrossRef]
  88. Kayabas, Y.E. The Relationship between Trade Liberalization, Sea Freight, and Carbon-Dioxide Emissions within the Perspective of EKC: The Case of Mexico. Int. J. Energy Econ. Policy 2023, 13, 364–372. [Google Scholar] [CrossRef]
  89. Pesaran, M.H. Testing Weak Cross-Sectional Dependence in Large Panels Testing Weak Cross-Sectional Dependence in Large Panels. Econ. Rev. 2015, 34, 1089–1117. [Google Scholar] [CrossRef]
  90. Westerlund, J. New Simple Tests for Panel Cointegration. Econom. Rev. 2005, 24, 297–316. [Google Scholar] [CrossRef]
  91. Pesaran, M.H.; Smith, R. Estimating Long-Run Relationships from Dynamic Heterogeneous Panels. J. Econom. 1995, 68, 79–113. [Google Scholar] [CrossRef]
  92. Pesaran, M.H. A Simple Panel Unit Root Test in the Presence of Cross-Section Dependence. J. Appl. Econom. 2007, 22, 265–312. [Google Scholar] [CrossRef]
  93. Hausman, J.A. Specification Tests in Econometrics. Econometrica 1973, 46, 1251–1271. [Google Scholar] [CrossRef]
  94. Blackburne, E.F.; Frank, M.W. Estimation of Nonstationary Heterogeneous Panels. Stata J. 2007, 7, 197–208. [Google Scholar] [CrossRef]
  95. Phillips, P.C.B.; Hansen, B.E. Statistical Inference in Instrumental Variables Regression with i(1) Processes. Rev. Econ. Stud. 1990, 57, 99–125. [Google Scholar] [CrossRef]
  96. Dumitrescu, E.I.; Hurlin, C. Testing for Granger Non-Causality in Heterogeneous Panels. Econ. Model. 2012, 29, 1450–1460. [Google Scholar] [CrossRef]
  97. Pesaran, M.H. General Diagnostic Tests for Cross Section Dependence in Panels. SSRN Electron. J. 2021, 60, 3–50. [Google Scholar] [CrossRef]
  98. Mujtaba, A.; Jena, P.K.; Bekun, F.V.; Sahu, P.K. Symmetric and Asymmetric Impact of Economic Growth, Capital Formation, Renewable and Non-Renewable Energy Consumption on Environment in OECD Countries. Renew. Sustain. Energy Rev. 2022, 160, 112300. [Google Scholar] [CrossRef]
  99. Baek, J. A New Look at the FDI-Income-Energy-Environment Nexus: Dynamic Panel Data Analysis of ASEAN. Energy Policy 2016, 91, 22–27. [Google Scholar] [CrossRef]
  100. Gao, Z.; Geng, Y.; Wu, R.; Zhang, X.; Pan, H.; Jiang, H. China’s CO2 Emissions Embodied in Fixed Capital Formation and Its Spatial Distribution. Environ. Sci. Pollut. Res. 2020, 27, 19970–19990. [Google Scholar] [CrossRef]
  101. Wu, Q. From Bits to Emissions: How FinTech Benefits Climate Resilience? Empir. Econ. 2024, 67, 2009–2037. [Google Scholar] [CrossRef]
  102. Xu, L.; Wang, X.; Wang, L.; Zhang, D. Does Technological Advancement Impede Ecological Footprint Level? The Role of Natural Resources Prices Volatility, Foreign Direct Investment and Renewable Energy in China. Resour. Policy 2022, 76, 102559. [Google Scholar] [CrossRef]
  103. Raihan, A.; Mainul Bari, A.B.M. Energy-Economy-Environment Nexus in China: The Role of Renewable Energies toward Carbon Neutrality. Innov. Green. Dev. 2024, 3, 100139. [Google Scholar] [CrossRef]
  104. Hussain, M.; Abbas, A.; Manzoor, S.; Bilal; Chengang, Y. Linkage of Natural Resources, Economic Policies, Urbanization, and the Environmental Kuznets Curve. Environ. Sci. Pollut. Res. Int. 2023, 30, 1451–1459. [Google Scholar] [CrossRef] [PubMed]
  105. Amin, A.; Dogan, E. The Role of Economic Policy Uncertainty in the Energy-Environment Nexus for China: Evidence from the Novel Dynamic Simulations Method. J. Environ. Manag. 2021, 292, 112865. [Google Scholar] [CrossRef]
  106. Ramzan, M.; Razi, U.; Usman, M.; Sarwar, S.; Talan, A.; Mundi, H.S. Role of Nuclear Energy, Geothermal Energy, Agriculture, and Urbanization in Environmental Stewardship. Gondwana Res. 2024, 125, 150–167. [Google Scholar] [CrossRef]
Sustainability 16 11290 g001
Figure 1. Conceptual workflow.
Figure 1. Conceptual workflow.
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Figure 2. ECLF trends for E7 economies.
Figure 2. ECLF trends for E7 economies.
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Figure 3. Summary of key findings.
Figure 3. Summary of key findings.
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Table 1. Data information.
Table 1. Data information.
VariablesAbbreviationsUnitsSources
Ecological footprintsECLFGlobal hectares per personGFN
Capital formationCPFR% of GDPWDI
Foreign direct Net inflows as % of GDPWDI
investmentFDIN
Renewable energyRWNE% of total energyWGI
Natural resourcesNTRR% of GDPWDI
UrbanizationUBNZ% of total populationWDI
Table 2. Descriptive statistics.
Table 2. Descriptive statistics.
ECLFCPFRFDINRWNENTRRUBNZ
Mean2.74546925.741141.99739425.403994.71704060.15211
Median2.70296923.689571.88851921.243043.27266668.45000
Maximum6.87779545.976326.18688258.6528621.9964587.76095
Minimum0.68015614.38661−2.7574403.1800000.13972025.54700
Std. Dev.1.5080047.2215971.35568717.092884.39266019.48402
Skewness0.8410530.8996560.1493270.2957341.688702−0.467566
Kurtosis3.1657293.1952913.3705251.6687575.6622371.733885
Table 3. Outcomes of the VIF test.
Table 3. Outcomes of the VIF test.
ParametersVIF IndexThreshold
NTRR2.750.364
RWNE2.370.422
CPFR1.690.592
UBNZ1.690.592
FDIN1.710.585
Table 4. Outcomes for CSD.
Table 4. Outcomes for CSD.
TestsStatistical Measurementp Value
Breusch–Pagan LM132.0503 ***0.0000
Pesaran-scaled LM16.05531 ***≡
Pesaran CD8.062641 ***≡
NOTE: *** indicate probability at the 1% level. CSD = Cross-sectional dependence. ≡ indicates value 0.0000.
Table 5. Slope homogeneity.
Table 5. Slope homogeneity.
TestsValuesp Values
Delta (δ)13.670 ***0.000
Adj. Delta (adj. δ)15.400 ***≡
NOTE: *** indicate probability at the 1% level. ≡ indicates value 0.000.
Table 6. CIPS unit root test.
Table 6. CIPS unit root test.
SeriesI(0)I(1)
ECLF−3.000 **−5.267 ***
NTRR−2.651−5.779 ***
RWNE−2.903 **−5.237 ***
CPFR−2.871 **−5.273 ***
UBNZ−1.276−1.598
FDIN−3.375 ***−5.692
Critical values: −2.73 (10%)−2.84 (5%)−3.06 (1%)
NOTE: *** and ** indicate significance at the 1% and 5% levels, respectively.
Table 7. Outcomes for Westerlund cointegration.
Table 7. Outcomes for Westerlund cointegration.
StatisticStatistical MeasurementZp Values
Gt−5.492 ***−7.9010.000
Ga−23.438 ***−2.7160.003
Pt−17.971 ***−10.8170.000
Pa−27.847 ***−5.2490.000
NOTE: *** indicate probability at the 1% level.
Table 8. Outcomes for the Hausman test.
Table 8. Outcomes for the Hausman test.
SummaryChi-Stat.p ValuesDecision
PMG vs. MG4.5998850.3309PMG
PMG vs. DFE4.9688160.2905PMG
Table 9. PMG—ARDL test.
Table 9. PMG—ARDL test.
ParametersCoefficientsStd. Error.t Valuesp Values
Long-term Equation
CPFR0.054487 ***0.00508610.712330.0000
FDIN−0.055674 ***0.015614−3.5656530.0005
RWNE−0.020777 ***0.001789−11.614130.0000
NTRR0.041124 ***0.0099444.1357360.0001
UBNZ0.030185 ***0.00210114.367010.0000
Short-term Equation
ECT−0.22721 8 ***0.087088−2.6090570.0098
D(CPFR)0.012583 **0.0046521.9543790.0475
D(FDIN)−0.030141 **0.025713−1.2722330.0426
D(RWNE)−0.047086 **0.019824−1.2823630.0386
D(NTRR)0.007350 **0.0110620.6879550.0262
D(UBNZ)0.093088 *0.0656530.7619440.0748
NOTE: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Table 10. Robustness check (FMOLS test).
Table 10. Robustness check (FMOLS test).
ParametersCoeff. ValuesStd. Errort Valuesp Values
CPFR0.058100 ***0.0111745.1994230.0000
FDIN−0.084648 ***0.028022−3.0207230.0028
NTRR0.026352 **0.0129722.0314690.0435
RWNE−0.001611 **0.002234−0.4956730.0451
UBNZ0.029998 ***0.0093273.2163440.0015
NOTE: *** and ** indicate significance at the 1% and 5% levels, respectively.
Table 11. Causality test.
Table 11. Causality test.
Causal-RelationW-ValueZbar-Value.p Values
CPFR → ECLF2.532010.410380.6815
ECLF → CPFR5.22684 ***3.437380.0006
FDIN → ECLF1.87664−0.325780.7446
ECLF → FDIN6.53132 ***4.902659 × 10−7
NTRR → ECLF1.72450−0.496670.6194
ECLF → NTRR3.66074 *1.678240.0933
RWNE → ECLF5.02016 ***3.205230.0013
ECLF → RWNE1.68873−0.536850.5914
UBNZ → ECLF6.00969 ***4.316722 × 10−5
ECLF → UBNZ7.22203 ***5.678501 × 10−8
NOTE: ***, and * indicate significance at 1% and 10% levels, respectively.
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Wang, Z.; Xu, R. Nexus of Natural Resources, Renewable Energy, Capital Formation, Urbanization, and Foreign Investment in E7 Countries. Sustainability 2024, 16, 11290. https://doi.org/10.3390/su162411290

AMA Style

Wang Z, Xu R. Nexus of Natural Resources, Renewable Energy, Capital Formation, Urbanization, and Foreign Investment in E7 Countries. Sustainability. 2024; 16(24):11290. https://doi.org/10.3390/su162411290

Chicago/Turabian Style

Wang, Zuyao, and Runguo Xu. 2024. "Nexus of Natural Resources, Renewable Energy, Capital Formation, Urbanization, and Foreign Investment in E7 Countries" Sustainability 16, no. 24: 11290. https://doi.org/10.3390/su162411290

APA Style

Wang, Z., & Xu, R. (2024). Nexus of Natural Resources, Renewable Energy, Capital Formation, Urbanization, and Foreign Investment in E7 Countries. Sustainability, 16(24), 11290. https://doi.org/10.3390/su162411290

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