Rethinking Foreign Direct Investment’s Role in Sustainable Development: Insights from the E-7 Economies Using Advanced Panel Data Methodologies

Abstract

Achieving a sustainable energy future is the cornerstone of global efforts to combat environmental degradation and align with corporate social responsibility (CSR) objectives. This study examines the complex relationship between energy consumption, carbon emissions, and the moderating influence of foreign direct investment (FDI) in the E-7 economies of Brazil, China, India, Indonesia, Mexico, Russia, and Türkiye from 2000 to 2022. Employing advanced panel data methodologies, including continuously updated fully modified (Cup-FM) and continuously updated bias-corrected (Cup-BC) techniques, we explored the long-term dynamics of energy use, urbanization, human capital, and FDI. Our findings reveal persistent cointegration among these variables, with energy consumption, urbanization, and human capital significantly contributing to CO₂ emissions. However, FDI has emerged as a critical mitigating factor, exhibiting a negative correlation with carbon emissions and moderating the emission-enhancing effects of urbanization and human capital. These results underscore the dual role of FDI as both an engine of economic growth and a catalyst for environmental sustainability. This study advocates for prioritizing green FDI inflows, particularly in renewable energy infrastructure, to harmonize economic development with global sustainability targets. By integrating CSR strategies with energy transition policies, this study provides actionable insights for policymakers and corporate leaders to foster sustainable development in rapidly industrializing economies. These findings contribute to the broader discourse on sustainable development, emphasizing the need for strategic investments and policy frameworks to achieve a low-carbon future.

1. Introduction

The E-7 economies of Brazil, China, India, Indonesia, Mexico, Russia, and Türkiye constitute a group of the world’s most dynamic emerging markets, collectively accounting for nearly half of the global GDP growth in recent decades [1]. These nations were selected for three key reasons: (1) their economic significance as major drivers of global investment flows and demand, with combined GDP growth rates consistently outperforming those of advanced economies; (2) their shared developmental characteristics, including rapid industrialization, accelerating urbanization, and growing energy consumption patterns, which mirror global sustainability challenges; and (3) their policy relevance, as these economies face similar challenges in balancing economic expansion with environmental objectives, making them particularly insightful for examining foreign direct investment (FDI)’s mediating role in sustainable development. Emerging countries develop rapidly and are marked by profound transformations in multiple dimensions. These include accelerated urbanization, escalating energy consumption, investment in human capital, and sustained economic expansion [2]. Human capital, which encompasses a workforce’s collective educational attainment, technical competencies, and cognitive skills, is a pivotal driver of economic development and the efficient allocation of productive resources [3]. Urbanization, an integral component of socioeconomic progress, exacerbates energy consumption and carbon dioxide (CO₂) emissions through intensified resource demand; however, its agglomerative effects also enable strategic urban planning, systemic efficiencies in energy infrastructure, and economies of scale in large-scale public transportation networks, fostering pathways for low-carbon transitions [4]. Energy consumption is a critical factor in these countries. Other factors that substantially impact the environment include the type of energy consumed and its efficiency. As these countries grow economically, energy demand increases, often resulting in increased CO₂ emissions, unless there is a substantial shift towards renewable sources [5]. Economic growth is impossible without increased industrial production and consumer demand, which increases CO₂ emissions. This relationship is nonlinear and influenced by the type of economic activity and technology used [6]. The role of FDI in this context is crucial and complex. On the one hand, FDI introduces capital, technologies, and practices that contribute to more efficient resource use and emission reduction. However, if not adequately regulated, FDI can also lead to an increase in environmentally harmful practices, such as exploiting natural resources and relying on carbon-intensive technologies [7]. This study aimed to dissect these relationships using empirical data and analysis. By understanding the interplay between these factors, this study provides insights into how emerging countries navigate their paths to economic growth while minimizing their environmental footprints. The findings of this study are intended to inform policymakers, business leaders, and international organizations about strategies that can lead to sustainable development in these critical regions of the world.

The motivation for this study is the urgent need to understand how various developmental factors contribute to CO₂ emissions in emerging economies. Significant changes in technological advancements, globalization, and environmental awareness represent critical windows for analysis [8]. Moreover, the role of FDI in moderating the impact of these factors on CO₂ emissions has not been explored extensively. FDI is often regarded as a driver of economic growth and vital for shaping sustainable development. Furthermore, the extent to which FDI mitigates the impact of these factors on CO₂ emissions has not been widely examined in previous studies [9]. However, the exact function that enables the efficient development of strategies and plans has not been well documented. The main objective of this study was to test the impact of human capital, energy usage, urbanization, and economic growth on CO₂ emissions in emerging economies, as well as how FDI moderates this relationship. This study used a novel panel data approach to determine the empirical relationships between these factors. Another key objective was to examine the long-run associations between these variables and assess the dynamic interactions and heterogeneity within panel data. This study aimed to comprehensively understand the factors that influence CO₂ emissions in emerging economies by achieving these goals. Figure 1 depicts the percentages of CO₂ emissions in the E-7 countries. The percentages represent each country’s contribution to changes in carbon emissions over time, highlighting the environmental impacts associated with economic activities in these countries.

As a crucial source of pollution and global warming, CO₂ emissions and their causes must be identified to improve environmental protection [11]. Therefore, this study used several methods to analyze environmental protection and sustainable development. First, it analyzed the effects of urbanization and human capital on CO₂ emissions in emerging economies. Second, in exploring the role of FDI, this study highlights its influence on CO₂ emission reduction by considering the effects of economic development and energy usage. Through infrastructure, an increase in FDI contributes to the fulfillment of basic needs, such as water and energy; thus, this variable is vital for environmental preservation and sustainability [12]. Third, this study stands out for its use of advanced econometric models, specifically the continuously updated fully modified (Cup-FM) and continuously updated bias-corrected (Cup-BC) models. These models offer advanced techniques with which to address endogeneity, unobserved heterogeneity, and bias, guaranteeing precise and reliable estimations. These models enhance the reliability and accuracy of the outcomes. This study has several important implications for policymakers and stakeholders in the development and promotion of policies and initiatives regarding sustainable development. The remainder of this paper is organized as follows: Section 2 presents the related empirical literature. Section 3 delves into the methodology, and Section 4 presents the empirical results and a thorough analysis. Finally, Section 5 presents the conclusions, policy recommendations, and potential avenues for future research.

2. Literature Review, Hypothesis Development, and Theoretical Framework

2.1. Literature Review and Hypothesis Development

This literature review explores the multifaceted impacts of human capital, energy usage, urbanization, and economic growth on CO₂ emissions in emerging economies while delving into the critical role of FDI in this dynamic. Poumanyvong and Kaneko [13] proposed three theories to explain the connection between urbanization and CO₂ emissions: compact cities, ecological modernization, and urban environmental transition theories. Ecological modernization theorists postulate that people with low incomes only care about their economic wellbeing; after reaching a certain level, they become concerned about the environment [14,15]. Based on the urban environmental transition theory, urbanization is believed to involve infrastructure improvement. Initially, individuals grapple with environmental concerns and gain interest in the repercussions of waste [16]. Compact city theorists believe that growing cities benefit citizens and the environment because they are equipped with better facilities and use resources more efficiently [17]. Empirical research has investigated the correlation between urbanization and CO₂ emissions. Depending on the viewpoint, a positive, negative, nonlinear, or insignificant relationship is argued to exist between variables. Salahuddin et al. [18] employed second-generation panel regression techniques that incorporated heterogeneity slope coefficients and cross-sectional dependence to assess the impact of urbanization on CO₂ emissions in 44 sub-Saharan African countries between 1984 and 2016. Their results suggested that urbanization contributes to CO₂ emissions. These results were further corroborated by other studies [19,20,21]. Figure 2 depicts the urbanization rates across the E-7 nations, measured as the percentage of the population residing in urban areas. This metric is a critical indicator of economic modernization and demographic transformation, reflecting shifts in the labor market, infrastructure development, and spatial reorganization.

We introduce the following hypothesis based on the above literature:

H₁. 

Urbanization initially increases CO₂ emissions but shows a reducing effect after reaching an economic development threshold.

According to Effiong [22], urbanization increases CO₂ emissions. Effiong [22] employed a generalized least squares (GLS) methodology to analyze the relationship between urbanization and CO₂ emissions across OECD economies from 1996 to 2018. The findings revealed that government effectiveness significantly moderates the positive correlation between urbanization and CO₂ levels, suggesting that enhanced institutional efficiency enables OECD countries to mitigate the environmental impact of urban expansion. Furthermore, the study explored the nonlinear dynamics between urbanization and CO₂ emissions using the environmental Kuznets curve (EKC) theoretical lens, offering nuanced insights into decoupling economic growth from ecological degradation. The EKC is a method for evaluating environmental performance and was derived from the inverted U-shaped graph developed by Kuznets in 1955 [23]. It represents the relationship between CO₂ and its influencing factors. From the Kuznets’ perspective, the environment suffers from economic growth; however, this relationship eventually improves as economic growth continues. According to the EKC hypothesis, income and environmental quality have an inverted U-shaped relationship, and environmental degradation is caused by economic development. However, once a specific threshold of economic growth is reached, ecological quality improves as per capita income increases. The shape of this curve indicates that environmental pollution initially increases with rising income but then begins to decline. Based on this inverted U-shaped relationship, Grossman and Krueger formulated the term “environmental Kuznets curve” [24], and Xiaoman et al. [25] posited that rising per capita income triggers heightened demand for environmental quality, incentivizing the adoption of cleaner sustainable technologies to mitigate ecological degradation.

Several empirical studies have supported EKC hypotheses on GDP growth and environmental quality [26,27,28]. This inverted U-shaped relationship was confirmed by Al-Mulali and Ozturk as well as Majeed et al. [29,30]. Using data from 1995 to 2019, Saqib et al. [31] explored the possibility of extending the traditional Kuznets curve to growing industrialized economies. According to their study, income expansion preceded environmental sustainability in these economies. Furthermore, technological modernization contributed to pollution mitigation. Using 54 African nations as case studies, Hussain et al. [32] examined the heterogeneous effects of urbanization and non-renewable energy usage on the environment. The research indicated that urbanization and the utilization of non-renewable energy sources in Africa have detrimental environmental effects. The relationship between economic development and CO₂ emissions followed an inverted U-shaped curve, consistent with EKC theory. By examining the Middle Eastern and North African economies, Kostakis et al. [33] proved the EKC hypothesis and suggested that energy use adversely affected environmental quality. According to Kaya Kanlı and Küçükefe [34], although EKC theory addresses the connection between the environment and income, it cannot be applied globally. Thus, regardless of income level, all countries must work to limit CO₂ emissions in different ways. Researchers have investigated the relationship between human capital and pollution from this perspective. Iorember et al. [35] conducted a study on the impact of renewable energy usage, human capital, and trade on the ecological footprint in South Africa and employed multiple structural break cointegration tests. They found that human capital is key to enhancing a nation’s environmental sustainability. Consequently, human capital can be used to regulate and reduce CO₂ emissions [36,37,38]. Figure 3 shows the human capital development in the E-7 economies. The y-axis represents the human capital index (HCI), measured using years of schooling and educational returns. It is crucial to understand how these nations invest in their populations to drive economic growth and competitiveness.

The second hypothesis is proposed as follows:

H₂. 

Higher human capital reduces CO₂ emissions through the adoption of cleaner technologies and environmental awareness.

Moreover, Gu et al. [40] employed a nonlinear panel autoregressive distributed lag method to assess the impact of human capital on CO₂ emissions in BRICS economies and found that a reduction in education increased CO₂ emissions. They believed human capital accretion was vital for reducing environmental degradation by lowering CO₂ emissions [41]. However, according to Hill and Magnani [42], more education leads to greater consumption of non-renewable energy. Although analyses of urbanization, human capital, and the CO₂ nexus have been conducted, a gap remains in terms of emerging countries. As highlighted above, FDI has been overlooked in previous studies as a conditional factor that moderates the connection between urbanization, CO₂, human capital, and CO₂ in emerging countries. Hence, this analysis attempts to fill this gap by first considering the role of FDI in the nexus between urbanization and human capital in emerging countries. We aim to contribute to the analysis of sustainable urbanization and development by considering new policies that mitigate pollution. Second, the importance of FDI in implementing sustainable development indicates that it can be a key instrument in the fight against pollution, particularly in emerging countries. According to FDI theory, various countries strive to attract FDI because of its potential economic benefits; international production networks, knowledge transfer, and productivity increase through FDI [43]. Figure 4 compares the FDI levels in the E-7 economies. The x-axis represents the average annual FDI inflow (% of GDP) for 2000–2022. It illustrates how these economies have attracted foreign investments over the past two decades.

Based on the above, we hypothesize the following:

H₃. 

Green FDI reduces CO₂ emissions, while traditional FDI increases emissions, depending on sectoral composition.

FDI contributes significantly to economic growth compared to national investment [44]. Wang [45] examined FDI’s contribution to reducing CO₂ emissions and found that FDI combined with human capital had a positive environmental impact in China. Foreign firms must introduce technologies tailored to the educational level of host countries because FDI inflows in countries with higher educational levels will reduce CO₂ emissions. However, Ahmad et al. [46] believe that the innovative and cleaner technologies provided by FDI reduce CO₂ emissions in host economies. The existing literature provides an understanding of the individual effects of human capital, energy usage, urbanization, and economic growth on CO₂ emissions in emerging nations. However, there is a gap in studies that comprehensively analyze these factors together in conjunction with the moderating role of FDI. Furthermore, the dynamics of these relationships may vary across different regions and developmental stages, suggesting the need for more nuanced studies. This study investigates the mitigation of sustainable development and climate change in emerging economies.

2.2. Theoretical Framework

This study is based on the EKC, which posits an inverted U-shaped relationship between economic growth and CO₂ emissions [47]. As the E-7 economies develop, CO₂ emissions initially rise with industrialization but decline after reaching an income threshold driven by structural shifts towards cleaner technologies and stricter environmental policies. This framework justifies examining how FDI accelerates or mitigates this transition. Complementing the EKC, ecological modernization theory (EMT) suggests that FDI can facilitate sustainable development by introducing advanced low-carbon technologies and fostering innovation [48]. However, the Pollution Haven Hypothesis warns that without robust regulations, FDI may exacerbate CO₂ emissions by relocating polluting industries to emerging markets. The interplay between these theories underscores the dual role of FDI: it can harm or help the environment depending on policy contexts and sectoral composition [49]. The impact of urbanization is interpreted through compact city theory and urban environmental transition theory [50,51]. Although urban expansion typically increases energy demand and CO₂ emissions, denser cities may eventually reduce their per capita emissions through efficient infrastructure, public transit, and economies of scale. Here, FDI quality matters: “green” FDI in renewable energy or smart urban planning can offset urbanization’s negative effects, whereas conventional FDI may intensify them. Human capital further moderates these dynamics. Educated populations demand cleaner environments and adopt sustainable practices; however, higher incomes may also increase consumption-based CO₂ emissions. FDI enhances this relationship by facilitating knowledge spillovers, for instance, by training the local workforce in eco-innovation or energy-efficient practices.

3. Model Description, Data, and Methodology

3.1. Model Description

This study evaluated the impact of human capital, urbanization, FDI, energy use, and gross domestic product on CO₂ emissions in the E-7 economies from 2000 to 2022. The variables were log-transformed as logarithmic transformations utilized in regression models to stabilize the variance, linearize relationships, and provide better interpretability, particularly for multiplicative impacts. Thus, the analysis was more comprehensive [52]. The generalized empirical relations are as follows:

$$C{O}_2=f (HC, URB, FDI, EC, GDP, {GDP}^2)$$

(1)

Equation (1) represents the regression, as follows:

$$C{O}_2={\beta }_0+{\beta }_1{HC}_{i,t}+{\beta }_2{URB}_{i,t}+{\beta }_3{FDI}_{i,t}+{\beta }_4{EC}_{i,t}+{\beta }_5{GDP}_{i,t}+{\beta }_6{{GDP}^2}_{i,t}+{\mu }_{i,t}$$

(2)

where ${\beta }_0$ is the constant term and ${\beta }_1$, ${\beta }_2$, ${\beta }_3$, ${\beta }_4$, ${\beta }_5$, and ${\beta }_6$ are the coefficients of the variables HC, URB, FDI, EC, GDP, and GDP², respectively. The variable ${\mu }_{i,t}$ denotes the error term. Subscripts i (i = 1, 2, …), n) and t (t = 1, 2…, T) denote economies and time, respectively. The influence of FDI with urbanization and human capital on CO₂ emissions is shown in Equations (3) and (4), respectively.

$$C{O}_2={\beta }_0+{\beta }_1{HC}_{i,t}+{\beta }_2{URB}_{i,t}+{\beta }_3{FDI}_{i,t}+{\beta }_4{FDI\times URB}_{i,t}+{\beta }_5{EC}_{i,t}+{\beta }_6{GDP}_{i,t}+{\beta }_7{{GDP}^2}_{i,t}+{\mu }_{i,t}$$

(3)

The moderating effect between FDI and urbanization ${\beta }_4{FDI\times URB}_{i,t}$ is described using interaction terms. In this analysis, we verified the EKC hypothesis by checking whether the coefficient of the GDP square is significant and negative, as expected.

$$C{O}_2={\beta }_0+{\beta }_1{HC}_{i,t}+{\beta }_2{URB}_{i,t}+{\beta }_3{FDI}_{i,t}+{\beta }_4{FDI\times HC}_{i,t}+{\beta }_5{EC}_{i,t}+{\beta }_6{GDP}_{i,t}+{\beta }_7{{GDP}^2}_{i,t}+{\mu }_{i,t}$$

(4)

The moderating effect between foreign direct investment and human capital ${\beta }_4{FDI\times HC}_{i,t}$ is explained using interaction terms. This study confirmed the EKC hypothesis by anticipating a significantly negative coefficient for the GDP square.

3.2. Data

This study examined the impact of human capital, foreign direct investment (FDI), urbanization, energy consumption, and economic growth on CO₂ emissions in the E-7 emerging economies (Brazil, China, India, Indonesia, Mexico, Russia, and Türkiye) using data from 2000 to 2022. Data gathered from World Development Indicators (WDI) [10] were used to derive the following: CO₂, computed in metric tons per capita, indicating the pollution level per person; economic growth (GDP), computed as GDP per capita and the growth of domestic products per inhabitant; energy consumption (EC), quantified as kilograms of oil equivalent per capita consumed by each inhabitant; urbanization (URB), quantified as the ratio of people living in cities or towns to the total population; and foreign direct investment (FDI) inflow, quantified as a percentage of the gross domestic product (GDP). The human capital index (HC) used years of schooling and educational returns to measure human capital; these data were sourced from the Penn World Table (PWT) [39].

3.3. Methodology

3.3.1. Cross-Sectional Dependency Test

The primary objective of the slope heterogeneity test was to assess whether heterogeneity existed among the gradients of the different variables in the dataset. Conversely, cross-sectional dependence (CD) allowed for the identification of interconnectedness between cross-sectional observations [53]. The test statistics were derived using the following equations:

$$y_{it}={\alpha }_i+{\beta }_i{x}_{it}+u_{it}i=7\dots \dots N,t=23\dots \dots T;$$

(5)

$$LM=T{\sum }_{i=1}^{N-1} {\sum }_{j=i+1}^N {\widehat{\rho }}_{IJ\to \overrightarrow{d}{X}^{2}N(N+1)2};$$

(6)

$$C{D}_{lm}=\sqrt{{\displaystyle \frac{N}{N(N-1)}}}{\sum }_{I=1}^{N-1} {\sum }_{J=i+1}^N \left(T{\widehat{\rho }}_{ij}-1\right);$$

(7)

$$C{D}_{lm}=\sqrt{{\displaystyle \frac{2T}{N(N-1)}}}{\sum }_{I=1}^{N-1} {\sum }_{J=i+1}^N \left({\widehat{\rho }}_{ij}\right);$$

(8)

$$C{D}_{lm}=\sqrt{{\displaystyle \frac{2}{N\left(N-1\right)}}}{\sum }_{I=1}^{N-1} {\sum }_{J=i+1}^N \left({\displaystyle \frac{\left(T-K\right){\widehat{\rho }}_{ij}^2-u_{Tij}}{v_{Tij}^2}}\right)\overrightarrow{d}\left(N,0\right).$$

(9)

3.3.2. Panel Unit Root Tests

Panel data analysis frequently utilizes second-generation panel unit root testing, CIPS, and CADF. A characteristic of second-generation panel unit root tests is their ability to reject the null hypothesis of CD. Therefore, these methods consider the relationships among cross-sections to overcome this issue [54]. The CIPS assessment carried out using the IPS approach and the regression equation of the CADF are presented in Equations (10) and (11):

$$\mathrm{For} \mathrm{CIPS}: {\mathsf{\Delta }Y}_{it}={\alpha }_{i}I+{\beta }_{it}i+{\gamma }_{i}i{Y}_{it}-1+{\delta }_i{Z}_{it}+{\epsilon }_{it}.$$

(10)

$$\mathrm{For} \mathrm{CADF}: {\mathsf{\Delta }Y}_{it}=Ii+{\beta }_{it}i+{\gamma }_{i}i{Y}_{it}-1+\sum j=1p{\delta }_{ij}{\Delta Y}_{it}-j+{\epsilon }_{it}.$$

(11)

3.3.3. Panel Cointegration Test

Panel cointegration tests assess whether a stable and long-lasting connection exists between time-series data. Panel data analysts commonly use the error correction-based tests developed by Kao [55] and Pedroni [56]. The error-correction-based method is a pioneering cointegration technique for analyzing panel data [57]. The Kao and Pedroni cointegration tests operate under the null hypothesis of no cointegration among the variables, with the alternative hypothesis asserting that cointegration exists across all panels [55,56]. In this study, we employed the Westerlund cointegration test, which extends this framework by evaluating the alternative hypothesis that cointegration is present in at least one panel, thereby offering a more nuanced assessment of cross-sectional dependencies and heterogeneous panel structures [57].

3.3.4. Continuously Updated Fully Modified and Continuously Updated Bias-Corrected Approaches

The selection of an appropriate estimation method is critical, as it directly affects the accuracy, reliability, and interpretability of the empirical findings. Among the widely used estimators in panel data analysis are the Cup-FM and Cup-BC approaches. These methods offer significant advantages, particularly in addressing endogeneity concerns and reducing estimation bias, thereby enhancing the robustness of dynamic panel data models. The Cup-FM estimator represents a sophisticated advancement in estimating dynamic panel data and provides improved efficiency and consistency in the presence of cross-sectional dependence and heterogeneous dynamics. Bai and Kao [58] presented the advantages of both fixed-effects and instrumental variable methods. For example, an effective solution to the endogeneity issue caused by unobserved heterogeneity constraints and simultaneity bias is achieved through structural means. A distinctive feature of the Cup-FM method is its ability to continuously update estimates with new information, enabling real-time analysis and supervision of dynamic relationships. The Cup-FM estimator has been proven effective in integrating fixed effects to control for unobserved heterogeneity, which is time-invariant over individuals or entities in panel data. The endogeneity issue has been effectively mitigated by applying instrumental variable (IV) techniques, which facilitate the derivation of unbiased and statistically consistent estimators. These estimators are particularly advantageous in dynamic panel models, especially when lagged dependent variables are incorporated as regressors to address autocorrelation and simultaneity bias. The methodological framework for the Cup-FM and Cup-BC estimators is formally represented by the following equation:

$$\begin{array}{r}\widehat{{\beta }_{\mathrm{cup} }}=⌊{\sum }_{i=1}^N \left({\sum }_{i=1}^T {\hat{y}}_{it}+\left({\hat{\beta }}_{\mathrm{cup} }\right)\times \left(x_{it}-{\bar{X}}_l\right)-T\left({\gamma }_i^{\mathrm{\prime }}{\hat{\beta }}_{\mathrm{cup} }\right)\mathsf{\Delta }{f}_{\mathrm{et} }+\left({\hat{\beta }}_{\mathrm{cup} }\right)+\stackrel{\mathrm{ˆ}}{\mathsf{\Delta }}\mu el+\left({\hat{\beta }}_{\mathrm{cup} }\right)\right)⌋\\ \times {⌊{\sum }_{i=1}^N {\sum }_{i=1}^T \left(x_{it}-{\bar{X}}_l\right)\left(x_{it}-{\bar{X}}_l\right)⌋}^{-1}.\end{array}$$

(12)

This study employed a novel method called the dynamic seemingly unrelated cointegrating regression (DSUR), introduced by Mark et al. [59]. The SUR model expands on the conventional SUR model by incorporating dynamics. It is utilized when there is a group of equations with internal variables that change over time, and the parameters of these equations are determined, considering any potential correlations and fluctuations between them. The SUR model for each equation j is displayed for cross-sectional unit i and period t as follows:

$$y_{ijt}={\alpha }_j+{\sum }_{k=1}^p {\beta }_{jk}{\gamma }_{ijt-k}+{\sum }_{m=1}^p {\beta }_{jm}{\gamma }_{ijt-m}+{\epsilon }_{ijt}.$$

(13)

4. Results and Discussion

The outcomes of the CD and slope heterogeneity analyses are presented in

Table 1. Outcomes of the CD and slope heterogeneity test.

	Breusch–Pagan LM Test	Pesaran Scaled LM Test	Adjusted LM Test	Pesaran CD Test	∆	Adj. ∆
CO2	315.302 a	17.833 a	127.934 a	12.582 a	29.87 a	103.109 a
HC	235.733 a	18.356 a	224.55 a	42.186 a	70.144 a	138.655 a
URB	179.885 a	37.878 a	138.041 a	54.954 a	57.97 a	126.852 a
FDI	244.548 a	27.477 a	109.788 a	21.012 a	49.767 a	122.94 a
EC	406.663 a	26.557 a	123.517 a	53.829 a	59.177 a	55.843 a
GDP	169.386 a	31.385 a	213.025 a	30.683 a	33.853 a	87.594 a

LM means the Lagrange multiplier; ∆, delta; and Adj. ∆, adjusted delta. Note: a is significant at the 1% level.

. The findings suggest that the research units have diverse properties and exhibit cross-sectional dependence and a shared dynamic. The results in Table 1 are statistically significant at the 1% level, indicating differences between units.

Table 2. Panel unit root test results.

Variables	CADF Statistic		CIPS Statistic		CADF Statistic		CIPS Statistic
	Constant		Constant		Constant and Trend		Constant and Trend
	Level	First Difference	Level	First Difference	Level	First Difference	Level	First Difference
CO2	−2.005	−4.048 a	−1.821	−6.149 a	−2.634	−7.823 a	−1.14	−3.253 a
HC	−1.527	−4.299 a	−1.209	−6.532 a	−1.808	−4.677 a	−2.531	−2.973 a
URB	−2.923	−2.868 a	−2.932	−3.473 a	−2.052	−3.786 a	−2.463	−4.604 a
FDI	−1.886	−3.007 a	−1.851	−2.094 a	−1.851	−3.191 a	−2.782	−7.095 a
EC	−2.827	−7.876 a	−2.069	−4.573 a	−1.413	−4.898 a	−1.918	−7.423 a
GDP	−1.471	−6.487 a	−2.304	−5.759 a	−2.956	−3.241 a	−2.635	−4.775 a

Note: a denotes significance at the 1% level.

shows that the test statistics for the CADF and CIPS are statistically significant at the 1% level after marking the first difference between the constant and trend. This suggests that these results support the notion that all the variables are integrated in the first order, specifically I(1).

Table 3. Panel cointegration analysis outcomes.

Pedroni [56]
Within Dimension	Statistics	Prob.	Between Dimension	Statistic	Prob.
Panel v-Statistic	1.788 b	0.038	Group rho-Statistic	1.313	0.904
Panel rho-Statistic	0.648	0.742	Group PP-Statistic	−2.228 b	0.014
Panel PP-Statistic	−2.328 b	0.010	Group ADF-Statistic	−2.315 b	0.010
Panel ADF-Statistic	−2.418 a	0.008
Kao [55]	t-Statistic	Prob.	Westerlund [57]	Z-Value	p-Value
ADF	−2.175 b	0.014	Gt	−4.069 a	0.000
			Ga	1.658	0.951
			Pt	−3.145 a	0.000
			Pa	−0.862	0.194

Note: b, and a denote significance at the 5%, and 1% levels, respectively.

presents the results of the panel cointegration tests, as well as Kao and Pedroni [55,56] and Westerlund [57], who conducted a panel cointegration test to determine whether the dependent and independent variables cointegrated in the long run. The analysis reveals a long-term relationship in the empirical equation, as assessed through the panel cointegration test results.

This study utilized novel panel data evaluation techniques, such as Cup-FM, Cup-BC, and DSUR.

Table 4. CUP-FM, CUP-BC, and DSUR results.

Variables	CUP-FM			CUP-BC			DSUR
	Coeff.	Std. Error	T-Stat	Coeff.	Std. Error	T-Stat	Coeff.	Std. Error	T-Stat
HC	0.105 a	0.036	3.052	0.149 a	0.039	3.858	0.121 a	0.018	7.989
URB	0.191 b	0.038	2.414	0.120 a	0.023	5.956	0.170 a	0.027	6.543
FDI	−0.092 a	0.023	−3.693	−0.080 b	0.037	−2.131	−0.131 a	0.016	−7.707
EC	0.158 a	0.022	7.254	0.151 a	0.022	7.189	0.102 a	0.018	5.655
GDP	0.168 a	0.026	6.151	0.151 a	0.033	5.006	0.133 a	0.022	6.377
GDP2	−0.077 b	0.035	−2.148	−0.101 a	0.020	−4.871	−0.131 a	0.026	−4.672
C	11.238 a	0.242	46.797	10.843 a	0.241	45.151	16.942 a	0.242	70.548
R2	0.889			0.898			0.906
Adj R2	0.951			0.937			0.945

Note: b, and a are significant at the 5%, and 1% levels, respectively.

,

Table 5. CUP-FM, CUP-BC, and DSUR results: the mediating effect of FDI and urbanization.

Variables	CUP-FM			CUP-BC			DSUR
	Coeff.	Std. Error	T-Stat	Coeff.	Std. Error	T-Stat	Coeff.	Std. Error	T-Stat
HC	0.080 a	0.016	3.665	0.078 a	0.004	27.808	0.172 a	0.005	27.125
URB	0.118 a	0.018	11.128	0.068 a	0.008	12.505	0.028 a	0.012	2.624
FDI	−0.025 a	0.007	−3.493	−0.043 a	0.005	−7.927	−0.092 a	0.009	−9.408
FDI × URB	−0.032 a	0.013	−2.661	−0.054 a	0.008	−24.456	−0.012 a	0.009	−22.654
EC	0.088 a	0.009	9.436	0.100 a	0.003	25.743	0.173 a	0.011	17.362
GDP	0.068 a	0.008	22.654	0.078 a	0.009	10.202	0.082 a	0.007	10.505
GDP2	−0.055 a	0.007	−13.133	−0.022 a	0.005	−3.656	−0.112 a	0.003	−23.664
C	10.365 a	0.360	42.568	11.324 a	0.356	42.453	18.467 a	0.356	77.678
R2	0.901			0.910			0.920
Adj R2	0.961			0.957			0.967

Note: a is significant at the 1% level.

and

Table 6. CUP-FM, CUP-BC, and DSUR results: the mediating effect of FDI and human capital.

Variables	CUP-FM			CUP-BC			DSUR
	Coeff.	Std. Error	T-Stat	Coeff.	Std. Error	T-Stat	Coeff.	Std. Error	T-Stat
HC	0.103 c	0.033	3.050	0.147 b	0.038	3.851	0.119 a	0.015	7.988
URB	0.091 c	0.037	2.412	0.119 a	0.022	5.955	0.168 a	0.025	6.540
FDI	−0.094 b	0.025	−3.696	−0.081 b	0.038	−2.130	−0.132 a	0.017	−7.708
FDI × HC	−0.080 a	0.028	−4.987	−0.103 a	0.044	−6.346	−0.115 a	0.033	−5.876
EC	0.157 a	0.021	7.253	0.150 a	0.021	7.188	0.109 a	0.019	5.654
GDP	0.169 a	0.027	6.152	0.150 a	0.033	5.007	0.135 a	0.021	6.379
GDP2	−0.078 c	0.036	−2.149	−0.102 a	0.021	−4.872	−0.132 a	0.028	−4.674
C	11.237 a	0.240	46.795	10.842 a	0.240	45.150	16.941 a	0.240	70.549
R2	0.888			0.899			0.905
Adj R2	0.950			0.938			0.944

Note: c, b, and a indicate significance levels of 10%, 5%, and 1%, respectively.

present the estimation results. Table 4 shows that a 1% increase in human capital leads to a 0.105% increase in CO₂ emissions, whereas a 1% increase in urban population leads to a 0.191% increase in CO₂ emissions. Additionally, FDI has a negative effect on CO₂ emissions, as a 1% increase in FDI decreases CO₂ emissions by 0.092%. Hence, an increase in FDI reduces CO₂ emissions in emerging economies. Furthermore, a 1% increase in energy use results in a 0.158% increase in CO₂ emissions. GDP positively affects CO₂ emissions, indicating that a 1% increase contributes to CO₂ emissions by 0.168%. GDP² negatively affects CO₂ emissions, confirming the EKC. A 1% increase in GDP² decreases CO₂ emissions by 0.077%. The Cup-FM outcomes are aligned with the Cup-BC and DSUR findings. The positive influence of human capital on CO₂ emissions in the E-7 countries is linked to multiple factors, such as improvements in human capital, education, and skills that drive economic growth and industrialization. It often leads to increased energy consumption, much of which is based on fossil fuels, thereby increasing CO₂ emissions. As human capital leads to better job opportunities and income, there is an increase in living standards, which increases the need for energy-intensive goods and services, further contributing to CO₂ emissions and stating that H₂ is rejected [60]. Figure 5 presents the findings shown in Table 4.

Urbanization increases CO₂ emissions in the E-7 countries because urban regions have higher energy demands, owing to their population, extensive industrial and commercial activities, and the need for transportation, heating, and cooling, which often rely on fossil fuels. Urbanization leads to more vehicles as people commute to work and meet personal needs, increasing fuel usage and CO₂ emissions. The proliferation of urban regions involves significant construction activity, which consumes energy and often uses materials such as cement and steel, the production of which is carbon-intensive [19]. Based on the above notation, H₁ is accepted. FDI negatively impacts CO₂ emissions in the E-7 economies, primarily through technology transfers and efficiency improvements. FDI facilitates the transfer of advanced and efficient technologies from developed to developing countries, promotes sustainable development, and reduces environmental pollution in the E-7 countries. The implementation of greener production methods and renewable energy sources causes a reduced reliance on fossil fuels and lowered CO₂ emissions. FDI plays a pivotal role in driving economic modernization by facilitating the transition towards service-oriented and high-technology industries, which are generally characterized by lower carbon intensity than conventional manufacturing and industrial sectors, showing that H₃ is accepted [61].

The reliance on fossil fuels for energy in the E-7 nations amplifies the positive relationship between the EC and CO₂ emissions. As these emerging economies expand, their escalating energy demands, driven by industrialization, transportation, and urban development, are predominantly met through coal, oil, and natural gas. The combination of these carbon-intensive energy sources releases significant quantities of CO₂, exacerbating emissions. Furthermore, the E-7 countries, at varying stages of development, have yet to achieve a comprehensive transition to cleaner renewable energy alternatives. Consequently, rising EC levels correlate with increased CO₂ emissions, reflecting the persistent carbon dependency of energy systems [62]. GDP’s positive effect on CO₂ emissions in the E-7 economies is linked to economic growth and its associated increases in industrial, transportation, and energy activities. Specifically, as GDP grows, it typically signifies higher industrial output, more vehicles, and higher energy consumption across various sectors, including residential and commercial sectors. In the early phases of development, these countries often rely on carbon-intensive energy sources derived from fossil fuels, such as coal, natural gas, and oil, whose combustion for energy and transportation leads to increased CO₂ emissions [63].

The squared GDP term and the EKC hypothesis negatively impact CO₂ emissions in the E-7 countries. The EKC hypothesis demonstrates an inverse relationship between CO₂ and economic growth (measured by GDP), which follows an inverted U-shape. As GDP increases, CO₂ emissions also increase because economic growth is typically driven by industrialization and increased energy consumption, often from the use of fossil fuels [64]. However, beyond a certain point of economic development, further increases in GDP lead to decreased CO₂, where the “GDP squared” term comes into play with a negative coefficient. As countries expand and mature, they prefer to adopt advanced, efficient, and cleaner technologies to reduce their CO₂ emissions per unit of economic output. Economic growth often raises public awareness and demands for environmental protection, leading to better regulations, pollution control measures, and investments in sustainable practices and technologies [65].

The findings in Table 5 are significant, and Table 4 shows the results for the Cup-FM, Cup-BC, and DSUR methods. The interaction term FDI × URB has a negative effect on CO₂ in the E-7 economies, indicating that FDI moderates the relationship between urbanization and CO₂ emissions. FDI introduces cutting-edge technologies and managerial techniques to the host nation. When these investments are directed towards urban areas, they introduce more energy-efficient infrastructure, green technologies, and sustainable urban planning practices. It mitigates the typical increase in CO₂ emissions associated with urbanization [66]. FDI also includes investments in renewable energy projects and environmentally friendly technologies in urban areas. These investments help offset the carbon footprint of growing cities by reducing their dependence on fossil fuels [67,68]. FDI stimulates less carbon-intensive sectors, such as services and high-tech industries, within urban regions. This diversification shifts economic activities in cities away from heavy industries, which are major contributors to CO₂ emissions. Consequently, the negative effect of the FDI × URB interaction on CO₂ indicates the critical importance of foreign investments in enabling sustainable urban development in the E-7 countries, leading to a more environmentally friendly urbanization process [66]. Figure 6 presents the findings shown in Table 5.

Table 6 shows the negative effect of the FDI × HC interaction term on CO₂ emissions in the E-7 countries. It is a synergistic effect of combining foreign investment with a skilled and educated workforce. FDI often brings innovative technologies and practices into the host country. These technologies can be efficiently implemented and utilized by a well-educated and skilled workforce (high human capital), leading to more sustainable and less carbon-intensive industrial processes. Enhanced human capital maximizes the benefits of FDI by adopting new technologies and methods more effectively, resulting in improved energy efficiency and reduced CO₂ emissions [69,70]. FDI and human capital foster an environment conducive to innovation. It often results in the development and adoption of greener technologies and sustainable practices within industries, thereby reducing CO₂ emissions. Workforces with advanced human capital are more likely to achieve these results; when such a workforce collaborates with foreign firms, it advocates and implements environmentally friendly practices that reduce the carbon footprint of its operations. Thus, the interaction between FDI and human capital mitigates CO₂ emissions by enhancing technological adoption, operational efficiency, and environmental sustainability in the economic activities of the E-7 countries [9].

5. Conclusions and Policy Propositions

This study examined the impacts of human capital, urbanization, FDI, energy usage, and GDP on CO₂ emissions, explicitly identifying FDI’s mediating role with urbanization and human capital in the E-7 countries between 2000 and 2022. The Cup-FM, Cup-BC, and DSUR methods were employed, and the results show that human capital, urbanization, energy use, and GDP positively affect CO₂ emissions, whereas FDI and GDP² negatively affect CO₂ emissions. FDI mitigates the positive impact of urbanization and human capital on CO₂ emissions in the E-7 economies. Similar coefficient values were obtained for all estimations, verifying the robustness of the model. Policymakers should encourage FDI, especially green FDI, by establishing policies that facilitate foreign investment in urban areas. Thus, an increase in green FDI could improve environmental protection. Encouraging green FDI presents several challenges. It means appropriate public policies (regulatory quality and taxation) and good governance (transparency), which can create a favorable political and economic environment for its implementation. For example, the governments of these countries can set climate standards and promote green FDI by requiring investors to comply. Through these policies, emerging countries should relax the circulation of goods and services between member countries, which would facilitate the implementation and use of hybrid cars, electric cars, and appliances, as well as the development of environmentally friendly roads, railways, and bridge systems.

These countries should implement sustainable urbanization by planning and investing in public transportation, creating walkable neighborhoods, building green infrastructure, and fostering economic diversity. Low-carbon apartments and green environments should be constructed to make them accessible to the general population. Furthermore, cities can promote sustainable transportation, energy efficiency, and waste reduction. FDI can reduce the positive association between human capital and CO₂ emissions by easing awareness-raising programs in emerging countries. Government decision-makers should promote remote work and telecommuting to reduce CO₂ emissions. By reducing energy use, these countries can reduce their CO₂ emissions. Efficient urbanization planning is a prerequisite for managing the growth of existing cities and facilitating the construction of new cities. The effectiveness of sustainable policies should be based on countries’ development levels, as it can be difficult for policymakers to balance economic growth and environmental sustainability. Future research should consider incorporating political and institutional factors, such as political stability, which can contribute to environmental degradation. Political stability influences FDI growth by highlighting a country’s safety.