Growth Versus Green: How Financial Development and Energy Transition Shape Environmental Sustainability in Emerging Economies

Abstract

Global carbon emissions are rising despite historic investments in renewables and net-zero pledges from nearly every economy. The current study examines the dynamic relationship between economic activities and environmental sustainability with reference to carbon emissions from emerging economies using the IPAT model utilizing panel data of 24 emerging economies from 2000 to 2019. Moreover, the study also used robust least square and fixed effect models for robustness purposes. The findings revealed that population (β = 0.103), economic growth (β = 1.090), financial development (β = 0.498), human capital (β = 1.073), and industrial structure (β = 0.158) are influential factors that significantly contributed to carbon emissions. In contrast, renewable energy transition (β = −0.375) has been identified as a mitigating factor that reduces emissions through the adoption of cleaner energy sources. This study provides empirical evidence to support the Environmental Kuznets Curve hypothesis, which states that economic development increases environmental pollution at the initial stage but fosters environmental improvements in later stages of economic growth. In addition, the econometric model for the moderating effect indicated that financial development reinforces the impacts of energy transition (from −0.141 to −0.201) in reducing emission levels, and significantly reduces the positive impact of human capital (from 0.931 to 0.503) and industrial structure (from 0.957 to 0.005). Therefore, green financial systems should be promoted to reduce carbon industries for sustainable economic growth and development, to assist in transitioning toward environmental sustainability in emerging economies.

Introduction

Global warming remains a major threat to sustainable development, despite historic investments in renewables and net-zero efforts from nearly every economy. Its effects, such as the rise in global temperature, natural disasters, and loss of biological diversity, are no longer secret and are being discussed at all levels of society (A. Khan et al., 2020). In addition, climate change endangers sustainability because of the degradation of the natural environment. Climate change is a formidable threat to sustainable global ecosystems worldwide. It not only affects health and environmental sustainability, but also enhances socioeconomic inequalities. Emerging economies are the most affected by these environmental issues because they are on the path of industrialization and a fast economic development trajectory. These countries are heavily reliant on traditional sources and production technologies causing increased carbon emissions (K. Zhang et al., 2014). Thus, they are left in a difficult scenario between environmental conservation and economic growth (Shahbaz et al., 2020). They also lack institutional quality, hindering their transition to cleaner energy as well as asymmetric technological development and shifting financial systems (Acheampong, 2018). It is imperative to investigate the relationship between economic growth, financial development (FD), and energy transition to ascertain the environmental impact of these economies to create a balance between prosperity and sustainability.

Increased economic growth has augmented the consumption of fossil fuels in a bid to enlarge economic activities, thus heightening greenhouse gas emissions (Jiang et al., 2022). The EKC hypothesis proposes an inverted U-shaped relationship between environmental pollution and economic growth. This hypothesis shows that the correlation between the declining quality of the environment and economic growth is an inverted U. Because degradation increases with economic growth, a negative change in degradation is witnessed as economies advance the application of sustainable practices (Kuznets, 2019). Grossman and Krueger (1991) pinpointed three essential effects: the scale effect, which has a negative influence, the technique effect, which has a positive influence, and the composition effect. In the emergent phases of industrialism, the tolerance of growth to industrial capacities is high, at the cost of the environment. Nevertheless, as these economies expand, they shift to acquiring cleaner technologies in resource management and, therefore, reduce environmental pollution. More developed economies have a stronger position to encourage competition in environmentally cleaner technologies and achieve a sustainable environment (Kiliç & Balan, 2018; Shahbaz & Sinha, 2019).

Beyond economic growth, rapid population growth and industrialization are also responsible for environmental degradation. The United Nations (2023) has indicated that the world population will exceed 8 billion by the end of 2022, with most of the growth being observed in developing and emerging economies. Such population growth increases the rate at which resources are depleted, the level of energy demand, and pollution, which provokes the goal of sustainable development. Similarly, the industrial structure, particularly the relative equilibrium of the primary, secondary, and tertiary sectors, is a central determinant in defining the environmental footprints of an economy. Economies with heavy manufacturing and resource-intensive industrial structures are more likely to have higher carbon emissions (J. Dong et al., 2021). Modernization of the industrial structure based on high-to-cleaner and more technologically oriented industries has been found to reduce carbon intensity through innovation and efficiency (Hou et al., 2023). Nonetheless, in this case, structural transformation is often contingent on the availability of green finance and investment in renewable technologies. This implies that industrial upgrading, FD, and energy transition are closely intertwined forces for attaining sustainable growth.

The issues of FD and environmental sustainability do not fit this narrative neatly. Financial growth can contribute to the improvement of technology to increase economic growth, but its impact on the environment remains inconclusive. On one hand, better financial frameworks contribute to the possibility of acquiring energy-intensive technologies and enhancing carbon emissions (Bashir et al., 2021). By contrast, FD for foreign direct investment allows green and environmental technologies to flourish (Soltani, 2024). FD can aid economies to transition into energy-efficient and environmentally innovative production with the help of FDI (Safi et al., 2021).

The energy sector alone causes almost three-quarters of total greenhouse gas emissions. This has necessitated a switch to cleaner energy systems worldwide. Although the world has been engaged in international agreements under the Paris Agreement, growth has been uneven: emerging economies, which account for more than 60% of global CO₂ emissions, still depend on coal and oil to meet their energy demands (World Bank, 2024). Over 50% of the world’s energy-related emissions are produced in Asia alone, whereas other emerging markets have positive trends toward higher emissions (International Energy Agency, 2025). Therefore, energy transition, which has been identified as a global challenge, is an important strategy for combating climate change. For example, the EU’s “Fit for 55” package shows that countries worldwide are committed to the use of renewable energy to reduce greenhouse-gas emissions by more than half by 2030. Nevertheless, the introduced barriers include high initial cost investment in new renewable energy technologies and the long periods taken in the research and development of such technologies (Ocal & Aslan, 2013). However, these barriers have not hindered green energy from being one of the main advocates of sustainable development objectives, particularly in the reduction of CO₂ emissions, as highlighted in the literature (Balsalobre-Lorente et al., 2018).

From a policy perspective, knowledge of the relationship between economic growth and environmental quality is becoming essential. The former conventional benchmarks of economic performance, including the rate of economic growth, are accompanied by an increased reference to human development. Research has shown that human development and CO₂ emissions per capita have a positive time-varying relationship. This shows that improvements in sociopolitical and economic status may lead to increased pressure on the environment (Kumar & Radulescu, 2024). However, few studies have examined the composite links between human development and CO₂ emissions using a multiple regression. This issue can be addressed in this study using human capital as a measure of development.

Although much progress has been made toward developing such concepts, many gaps still exist in the literature. Only a limited number of studies provide an integrated investigation of the relationship between economic growth, FD, and energy transition and CO₂ emissions, particularly for emerging economies. Specifically, this study seeks to answer the following critical questions: (i) How do industrial structure and economic growth affect CO₂ emissions? (ii) It examines how FD affects the relationship between human capital, energy transition, industrial structure, and CO₂ emissions.

Specifically, this study employs a multivariate framework to explore these interrelationships for 24 emerging economies for 2000 to 2019 using annual panel data. Thus, it makes several contributions to the existing literature. First, it includes the industrial structure, energy transition, human capital, FD, and CO₂ emissions in a single analytical framework, thereby offering a comprehensive perspective on the increased carbon footprints. Second, it examines the moderating effect of FD on how it can be used to benefit environmental protection. Finally, it presents policy solutions for emerging economies to reduce carbon emissions and to support sustainable development.

Literature Review

The dynamic relationship between carbon footprint, economic growth, and energy transition amid increasing environmental degradation has drawn significant academic interest. This study divides the literature into five interconnected themes to explore the nexus between economic growth, FD, energy transition, industrial structure, human capital, and carbon footprint. These themes collectively provide a comprehensive view of the various factors that influence CO₂ emissions and environmental sustainability.

Economic Growth and Carbon Footprint Nexus

Subsequent to the work of Grossman and Krueger (1991), the economic growth–environment relationship has been explored extensively in the literature (Abbasi et al., 2021; Lee et al., 2021; H. Yang et al., 2021). Over the past few years, researchers have considered the relationship between these two parameters in several ways. In particular, the sources of decreasing CO₂ emissions have attracted significant interest. Azevedo et al. (2018) showed that the role of GDP is multiple; that is, CO₂ emissions depend on GDP. Therefore, for the non-exclusive use of non-renewable energy sources, the amount of CO₂ emissions will rise with economic growth, but if the economic growth is under the mantle of renewable energy sources, then the emission of CO₂ will decrease. While the sources of natural resources exert pressure on the environment, a green economy, as guided by relevant national policies, can mitigate CO₂ emissions. H. Yang et al. (2021) established that green growth can play a useful role in reducing CO₂ emissions. Andreoni (2021) stated that the possible contraction of social and economic activities may contribute to reduced CO₂ emissions by a large margin. Therefore, the impact of GDP on pollutant emissions remains ambiguous and can be related to the sources of rising GDP. In literature, the relationship between economic growth and environmental outcomes is discussed in light of the EKC hypothesis. Taghvaee et al. (2022) proved the EKC hypothesis by showing that the complexity of the economy and structure of the sector play a major role in the environmental quality of OECD countries. Their findings showed that the majority of OECD countries stand on the rising part of the curve, with growth and complexity continuing to raise CO₂ emissions. Similarly, a machine-learning study by Taghvaee et al. (2025) across 164 countries in various developmental environment phases also confirmed the EKC, Pollution Halo, and Pollution Haven hypotheses. These new developments support the dynamism and heterogeneity of the growth and environment nexus and the importance of reconsidering the nexus in new economies with their unique financial and industrial sectors.

Financial Development and Carbon Footprint Nexus

FD plays a significant role in economic activity and environmental quality (F. Yang, 2019). These financially led economic activities have propelled high-energy consumption. Saud et al. (2018) and Samour et al. (2019) found that higher financial activities have an impact on economic activities and negative impacts on environmental quality. Salahuddin et al. (2016) posit that firms’ financing enhanced through the use of financial instruments leads to descending financial costs, hence promoting production activities requiring energy and CO₂ emissions in the short and long run. Amri (2018) established that FD can lead to environmental deterioration. Tsaurai and Chimbo (2019) also reveal an indirect relationship between FD, ICT, and CO₂ emissions. Thus, FD has a two-sided effect on environmental development. On one hand, it has the potential to increase the pace of industrial growth and energy consumption. Thus, the carbon footprint is compounded by increased resource consumption and production. Conversely, when the financial system operates effectively, it can be used as a stepping stone toward environmental improvement by mobilizing investment in more environmentally friendly technologies and stimulating the development of green innovations. Tamazian et al. (2009) postulated that financial resources promote the deployment of innovative technology on a cheap basis, and fund environmentally sustainable initiatives. Atsu et al. (2021) argued, enhancing financial services improves innovation and energy intensity. There are several approaches by which FD enables firms to undertake green technological investments by eradicating credit constraints. Park et al. (2018) also support the fact of the negative role of FD in minimizing CO2 emission in developed countries as the main financial institutions offer soft credit facilities that enhances research on development and renewable energy which enhances energy efficiency and reduce emission of carbon dioxide. Consequently, Xu et al. (2018) recommend that financial sectors avail financial services that support environmentally friendly production technologies and minimize environmental pollution.

Energy Transition and Carbon Footprint Nexus

Several studies have examined potential correlations between energy transitions and environmental conditions. For instance, Gençer et al. (2020) assessed the application of the SESAME tool to estimate life-cycle emissions in the context of energy transitions. They found that the transition of the energy sector evolved through transportation of the power sector and vertical and horizontal integration. Moreover, their findings support the fact that energy transition is an urgent requirement and a middle capture of emergent environmental dynamics. Cardoso and González (2019) reported that failure to incorporate efficient energy conservation measures has led to massive operations and impacts on the environment. Kokkinos et al. (2020) discussed how the energy transition can be initiated in a sustainable low carbon environment. This study concluded that energy supply to urban communities shapes low-carbon energy transformation politics. Poruschi and Ambrey (2019) also assessed the built environment effects of solar photovoltaic energy and found that a denser built environment can be reduced through the energy transition to solar paneling. Song et al. (2020) explored that any low-carbon energy transition strategies have potential adverse environmental impacts.

Industrial Structure and Carbon Footprint Nexus

This section summarizes previous literature on carbon emissions and industrial structure. The structural optimization of industries has emerged as one of the most effective measures to address environmental challenges resulting from economic decline and industrialization (Li et al., 2017). Jin (2007) examined the association between the transformation of industrial structure and CO₂ emissions, and insisted that demand-pull and export-pull development models deepen the environmental burden. Adom et al. (2012) examined the causality relationship between CO₂ emissions and industrial structure in the short run and the long run equilibrium. The data also indicate that China’s industrial sector emitted 7.8 GtCyr-1 and accounted for 24.1% of global emissions in 2015. This indicates that changes in China’s industrial structure are of unique national and international significance with regard to carbon emission reduction (J. Wang et al., 2019).

Human Capital and Carbon Footprint Nexus

The effects of human capital on the environment have recently received the attention of theorists. More recent work has dedicated efforts to explaining the environmental externalities of human capital employing conventional education, and only a few studies have employed the conventional education-based measure of human capital to explain the environmental externalities of human capital (Alvarado et al., 2021; Liu et al., 2018). Scholars have used panel data and single-country-level data analysis to examine human capital and environmental issues. From a panel perspective, Yao et al. (2019) and Yao et al. (2020) reviewed the literature on the perceptions and preferences of educated people for clean energy consumption rather than dirty energy. They found that human capital enhanced by tertiary education has a negative relationship with CO₂ emissions.

Similarly, Alvarado et al. (2021) found that economic development cannot decrease energy consumption from fossil sources, whereas human capital can reduce non-renewable energy. Pablo-Romero and Sánchez-Braza (2015) find a significant relationship between the substitutability of human capital and energy use. M. Khan (2020) found that CO₂ emissions would decrease with economic development per capita human capital, and the awareness and friendly environment and innovative technologies would be promoted. Hao et al. (2021) found that human capital could have reduced CO₂ emissions for G7 countries for the period 1991 to 2017. Similarly, Z. Khan et al. (2020) confirmed that human capital increased renewable energy consumption. Furthermore, Z. Khan et al. (2021) reported that enhancing human capital deepens the unfavorable nexus between CO₂ emissions and fiscal decentralization. Human capital in the same study was also seen to play a positive role in encouraging the use of renewable energy consumption, which was also captured in the research conducted (Mehrara et al., 2015). Mahmood et al. (2019) found that the human capital reduces CO₂ emissions. Human capital considerably decreases emissions, without compromising economic development Bano et al. (2018). Similarly, Z. Ahmed et al. (2020) concluded that human capital reduces environmental degradation and plays a moderating role in enhancing sustainable urbanization.

Population and Carbon Footprint Nexus

Population growth is one of the most persistent demographic forces contributing to carbon emissions and environmental changes. With an increase in population, the demand for food, shelter, and energy has accelerated the use of fossil fuels and natural resources. Therefore, growing populations are likely to increase the aggregate energy demand, infrastructural requirements, transportation volumes, and urban expansion. These are likely to increase carbon emissions unless offset by changes in technology or structure (M. Ahmed et al., 2023). This increasing demand is directly related to increased amounts of carbon dioxide, and is manifested in countries with developing and emerging economies, wherein energy production is still based on non-renewable energy sources (S. Wang et al., 2018). Fatima et al. (2024) also demonstrated that the demographic contribution to emissions can be particularly high in emerging economies due to booming population, insufficient renewable energy access, and fossil fuel consumption.

Population size also leads to urbanization, which in turn contributes to the problem by further fueling the use of energy in industrial output, building construction, and mobility (Rafiq et al., 2016). Nevertheless, the correlation between population and carbon emissions is not linear and depends on the income level, technological advancement, and energy efficiency (Liddle, 2014). In technologically developed and high-income economies, population increases may not have a strong impact on the environment owing to the progress of renewable energy, environmentally friendly infrastructure, effective legislation, and conscious use of natural resources. However, in low- and middle-income nations, ineffective environmental policies and inadequate use of clean technologies tend to increase the carbon footprint of the population (Milindi et al., 2022; Zhu et al., 2016). More recent studies have shown that a 1% increase in population growth can contribute to approximately 0.4%–0.5% of carbon emissions in middle-income countries and industrialization (Konuk et al., 2025). Therefore, knowledge of population trends and economic development, financial growth, and energy transitions is vital for estimating the environmental burdens in emerging economies.

Available studies have highlighted the complexity of the causes of carbon emissions, including economic growth, FD, industrial structure, human capital, energy transition, and demographic factors. However, previous research has provided information on these individual relationships without analyzing their interplay in the development patterns of emerging economies. Cross-country evidence is mixed because of differences in institutional capacity, technological advancements, and policy orientation. In addition, little empirical research has been conducted on the relationship between FD, human capital, energy transition, industrial structure, and the demographic aspect of population increase, despite its increasing importance in the discussion of sustainability. Relatively few studies combine these economic and social variables into an analytical framework that describes interactive measures of environmental outcomes. This study extends the literature by adding all six dimensions—economic, financial, industrial, human, demographic, and energy-related—to a single-panel analysis of emerging economies. By doing so, it enhances the knowledge of how these interrelated forces determine the carbon footprint and offers important insights for policies that seek to balance economic development and environmental sustainability.

Theoretical Framework

The theoretical background of the proposed study follows the IPAT identity and the Environmental Kuznets Curve (EKC) hypothesis. Both formulate the process by which socioeconomic factors combined with structural and technological factors influence environment. In IPAT model, the environmental impact (I) is the product of population (P), affluence (A), and technology (T) (Ehrlich & Holdren, 1971). This model emphasizes that environmental degradation is not a one-factor phenomenon but a composite of the demographic pressure, increase in income, and technological intensity.

The EKC hypothesis states that the dependency between environmental degradation and economic growth has an inverted U-shape (Grossman & Krueger, 1995; Stern, 2017). The scale effect prevails in the initial phases of development when a growing GDP leads to the growth of industrial activity and energy needs. When income reaches a specific level and the composition effect and techniques are created, economies begin to clean up and turn to more efficient technologies that result in a reduction in carbon emissions. The GDP in this model reflects the level of economic scale and economic development, whereas industrial structure reflects the structural composition, which defines the environmental intensity of production. However, the assumptions of EKC have also been criticized. For example, Xia et al. (2022) compared 67 developed and developing economies and showed that globalization and industrial relocation make the EKC curve more difficult to follow as a country of production with high pollution levels could be transferred to another country. This questions the belief that the rate of environmental degradation per country naturally decreases with increasing income. In addition, heterogeneous outcomes and N-shaped patterns have also been reported in the recent literature (Abbasi et al., 2023), implying that the typical inverted U-shape is not necessarily true in all contexts.

Population growth has a direct impact on environmental stress through demand for energy, infrastructure, and goods. In developing countries, there are increased emissions due to rapid urbanization and population growth (Shanggua, 2024). However, when production is increased to satisfy local domestic demand, human capital influences environmental performance via the technological channel. An increased level of education and skills promotes innovation and efficiency; however, in the short term, it can also lead to industrial and economic growth, which causes an increase in emissions before more environmentally friendly approaches are adopted (M. Khan et al., 2023).

Renewable energy transition is the technological change of power sources to cleaner ones, and is a mitigating factor in the framework of the IPAT and EKC. The increased use of renewable energy minimizes the use of fossil fuels, which results in a direct reduction of CO₂ and enhances the long-term sustainability of the future (M. Ahmad et al., 2023). The impact of industrial structure is that manufacturing and resource-intensive structures are more likely to expand emissions. A shift to service-based or high-tech structures has the potential to decrease carbon intensity (B. Dong et al., 2020; Luo & Xu 2025).

FD was added as a moderating variable to precondition the effects of human capital, industrial structure, and renewable energy transition on the environment. In principle, FD can increase or decrease emissions, based on the distribution of financial resources. Properly elaborate financial systems can be used to invest in energy-efficient and renewable energy technologies. This contributes to the positive environmental impact of renewable energy transition and increases human capital’s ability to foster innovation. However, when credit expansion is channeled to carbon-intensive sectors, FD can worsen the environmental performance of the domestic industry (Le & Ozturk, 2020; Ruza & Caro-Carretero, 2022). Therefore, the moderating role of FD determines whether financial systems facilitate or hinder sustainable growth.

This combined framework builds on the classic IPAT-EKC model, including the conditioning aspect of financial systems. It can be used to understand the relationships between the structural, demographic, and technological changes that occur in developing economies in a more detailed manner to determine the effect on carbon emissions. Hence, the research makes a theoretical contribution in the sense that it has shown that environmental outcomes are not only influenced by individual drivers but also by the interaction and trade-off between the drivers, moderated by the intensity and orientation of FD.

Methodology

Model Specification

This study follows the IPAT model, which connects environmental impacts (I) to population (P), affluence/development (A), and technology (T) (Dietz & Rosa, 1994; Ehrlich & Holdren, 1971).

$$I_t={\mathrm{\alpha }}_t{P}_t^{{\mathrm{\beta }}_1}{A}_t^{{\mathrm{\beta }}_2}{T}_t^{{\mathrm{\beta }}_3}{\mathrm{\epsilon }}_t$$

(1)

Here, I indicate the environmental impact, P is population, A signifies economic development, and T represents technological innovations. $\mathrm{\alpha }$ is a constant and $\mathrm{\beta }$ is an unknown parameter. Moreover, this study also includes other variables that are important in influencing carbon footprint and ecological sustainability. These variables include FD, human capital, and industrial structure (Adikari et al., 2023; K. Wang et al., 2019; Y. J. Zhang, 2011). After including more variables, the typical IPAT model in Equation 1 was extended as follows:

$$I_t={\mathrm{\alpha }}_t{P}_t^{{\mathrm{\beta }}_1}{A}_t^{{\mathrm{\beta }}_2}{T}_t^{{\mathrm{\beta }}_3}{\mathrm{FD}}_t^{{\mathrm{\beta }}_4}{\mathrm{HC}}_t^{{\mathrm{\beta }}_5}{\mathrm{INS}}_t^{{\mathrm{\beta }}_6}{\mathrm{\epsilon }}_t$$

(2)

In Equation 2, FD indicates financial development, HC is human capital, and INS is industrial structure. After taking logs, Equation 2 becomes:

$$\begin{array}{c}\multicolumn{1}{c}{\ln I_t={\mathrm{\alpha }}_t+{\mathrm{\beta }}_1\ln P_t+{\mathrm{\beta }}_2\ln A_t+{\mathrm{\beta }}_3\ln T_t+{\mathrm{\beta }}_4\mathrm{lnF}{D}_t}\\ \multicolumn{1}{c}{+{\mathrm{\beta }}_5\mathrm{lnH}{C}_t+{\mathrm{\beta }}_6\mathrm{lnIN}{S}_t+{\mathrm{\mu }}_i+{\mathrm{\lambda }}_t+{\mathrm{\epsilon }}_t}\end{array}$$

(3)

Where ${\mathrm{\mu }}_i$ is the country fixed effect and ${\mathrm{\lambda }}_t$ indicates the time fixed effect. To be more precise, all variables are presented in a natural logarithmic form based on both theoretical and econometric considerations. In theory, the IPAT and STIRPAT frameworks, which describe environmental impact as a multiplicative relationship between population, affluence, and technology, are consistent with the log linear specification by Imran et al. (2022). The natural logarithm transforms this multiplicative identity into a linear additive one, which can be easily estimated using regression techniques. The logarithmic transformation stabilizes the variance, decreases heteroscedasticity, and lowers the impact of extreme values typical of macro-panel data in emerging economies. In addition, the estimated coefficients can be used to estimate the elasticities that offer a direct measure of the percentage change in CO₂ emissions. Therefore, the log-linear form was more interpretable and statistically reliable.

Environmental degradation is measured by the carbon footprint of the economy at time t, which is a major contributor to environmental degradation. Therefore, the carbon footprint was measured in terms of CO₂ emissions per capita. The literature indicates that technological innovations play a crucial role in improving environmental quality by reducing the carbon footprint (Habiba et al., 2022; Z. Wang & Zhu, 2020). Various technological innovations, such as R&D expenditures, do not directly affect carbon footprint. Therefore, in this study, we focus on technological innovation regarding energy because energy majorly contributes to carbon footprints. Between 2010 and 2019, carbon footprints from emerging economies grew at an annual rate of 3.0%. Coal (34.6%) and oil (28.1%) were the major energy sources for carbon emissions. Moreover, biomass is a major energy source contributing to carbon emissions (Cui et al. 2023). Therefore, the current study uses the renewable energy transition (RET) as proxy for technology which explain how the economies are transformed their energy sources over the time. Gross domestic product (GDP) is used as a proxy for economic development, and symbol A is replaced by GDP. Therefore, Equation (3) was modified accordingly, and the new model (M-1) is presented in Equation 4.

$$\begin{array}{c}\multicolumn{1}{c}{\mathrm{lnC}{O_2}_{\mathrm{it}}={\mathrm{\alpha }}_{\mathrm{it}}+{\mathrm{\beta }}_1\mathrm{lnPO}{P}_{\mathrm{it}}+{\mathrm{\beta }}_2\mathrm{lnGD}{P}_{\mathrm{it}}+{\mathrm{\beta }}_3\mathrm{lnRE}{T}_{\mathrm{it}}}\\ \multicolumn{1}{c}{+{\mathrm{\beta }}_4\mathrm{lnF}{D}_t+{\mathrm{\beta }}_5\mathrm{lnH}{C}_t+{\mathrm{\beta }}_6\mathrm{lnIN}{S}_t+{\mathrm{\mu }}_i+{\mathrm{\lambda }}_t+{\mathrm{\epsilon }}_t}\end{array}$$

(M-1)

Analyzing the Validation of Environmental Kuznets Curve Hypothesis

To analyze the validation of the Environmental Kuznets Curve (EKC) in emerging economies, model (M-2) is specified in which the GDP square is added, while all the other variables are the same. Therefore, M-2 is a unique nonlinear model, along with all other variables such as FD, HC, INS, and RET. The functional form of M-2 is as follows:

$$\begin{array}{c}\multicolumn{1}{c}{\mathrm{lnC}{O_2}_{\mathrm{it}}={\mathrm{\alpha }}_{\mathrm{it}}+{\mathrm{\beta }}_1\mathrm{lnPO}{P}_{\mathrm{it}}+{\mathrm{\beta }}_2\mathrm{lnGD}{P}_{\mathrm{it}}+{\mathrm{\beta }}_3{\mathrm{lnGDP}}_{\mathrm{it}}^2}\\ \multicolumn{1}{c}{+{\mathrm{\beta }}_4\mathrm{lnRE}{T}_{\mathrm{it}}+{\mathrm{\beta }}_5\mathrm{lnF}{D}_{\mathrm{it}}+{\mathrm{\beta }}_6\mathrm{lnH}{C}_{\mathrm{it}}}\\ \multicolumn{1}{c}{+{\mathrm{\beta }}_7\mathrm{lnIN}{S}_{\mathrm{it}}+{\mathrm{\mu }}_i+{\mathrm{\lambda }}_t+{\mathrm{\epsilon }}_t}\end{array}$$

(M-2)

Analyzing the Moderating Impact of Financial Development

In the other models, such as M-3, M-4, and M-5, we included FD as a moderator to examine FD in the context of HC, RET, INS, and EKC. These models are specified as follows:

$$\begin{array}{c}\multicolumn{1}{c}{\mathrm{lnC}{O_2}_{\mathrm{it}}={\mathrm{\alpha }}_{\mathrm{it}}+{\mathrm{\beta }}_1\mathrm{lnPO}{P}_{\mathrm{it}}+{\mathrm{\beta }}_2\mathrm{lnGD}{P}_{\mathrm{it}}+{\mathrm{\beta }}_3{\mathrm{lnGDP}}_{\mathrm{it}}^2}\\ \multicolumn{1}{c}{+{\mathrm{\beta }}_4\mathrm{lnH}{C}_{\mathrm{it}}+{\mathrm{\beta }}_5\ln (F{D}_{\mathrm{it}}*H{C}_{\mathrm{it}})+{\mathrm{\mu }}_i+{\mathrm{\lambda }}_t+{\mathrm{\epsilon }}_t}\end{array}$$

(M-3)

$$\begin{array}{c}\multicolumn{1}{c}{\mathrm{lnC}{O_2}_{\mathrm{it}}={\mathrm{\alpha }}_{\mathrm{it}}+{\mathrm{\beta }}_1\mathrm{lnPO}{P}_{\mathrm{it}}+{\mathrm{\beta }}_2\mathrm{lnGD}{P}_{\mathrm{it}}+{\mathrm{\beta }}_3{\mathrm{lnGDP}}_{\mathrm{it}}^2}\\ \multicolumn{1}{c}{+{\mathrm{\beta }}_4\mathrm{logRE}{T}_{\mathrm{it}}+{\mathrm{\beta }}_5\log (F{D}_{\mathrm{it}}*\mathrm{RE}{T}_{\mathrm{it}})+{\mathrm{\mu }}_i}\\ \multicolumn{1}{c}{+{\mathrm{\lambda }}_t+{\mathrm{\epsilon }}_t}\end{array}$$

(M-4)

$$\begin{array}{c}\multicolumn{1}{c}{\mathrm{lnC}{O_2}_{\mathrm{it}}={\mathrm{\alpha }}_{\mathrm{it}}+{\mathrm{\beta }}_1\mathrm{lnPO}{P}_{\mathrm{it}}+{\mathrm{\beta }}_2\mathrm{lnGD}{P}_{\mathrm{it}}+{\mathrm{\beta }}_3{\mathrm{lnGDP}}_{\mathrm{it}}^2}\\ \multicolumn{1}{c}{+{\mathrm{\beta }}_4\mathrm{lnIN}{S}_{\mathrm{it}}+{\mathrm{\beta }}_5\ln (F{D}_{\mathrm{it}}*\mathrm{IN}{S}_{\mathrm{it}})+{\mathrm{\mu }}_i+{\mathrm{\lambda }}_t+{\mathrm{\epsilon }}_t}\end{array}$$

(M-5)

To determine the unknown coefficients, we followed the standard procedure for the panel data analysis. First, we apply the panel unit root test to confirm the stationarity of the series. Furthermore, we use the Westerlund (2007) cointegration test, which is robust in the presence of cross-sectional dependencies. The unknown coefficients were determined with the application of panel robust least squares, as this method efficiently tackles outliers, non-normality of data, and multicollinearity (Naz & Aslam, 2023). For robustness, we used a fixed effects model.

The study sample included 24 emerging economies: Brazil, Chile, China, Colombia, Czechia, Egypt, Greece, Hungary, India, Indonesia, Iran, Malaysia, Mexico, Nigeria, Pakistan, Peru, the Philippines, Poland, the Russian Federation, Saudi Arabia, South Africa, Türkiye, the United Arab Emirates, and Vietnam. These nations were chosen according to the fact that they are classified as emerging economies by global organizations like IMF and World Bank and because of the presence of consistent data on the variables of interest. It offers a wide representation of regions and industrial foundations, including manufacturing-based economies (e.g., China, India, and Indonesia) and resource-based economies (e.g., Saudi Arabia and Nigeria). This group of countries provides a more balanced insights on the economic growth-environment nexus.

The inclusion of a wide range of emerging economies brings diversity in production, industrial, and technological structures. This enables the model to capture variations in structural characteristics across countries. The analysis ensures that unobserved country-specific factors are adjusted by applying fixed effects. The adoption of a robust least-squares estimator following a logarithmic transformation of the variables was used to control the effects of outliers and heteroscedasticity. The specification of the fixed effects is used to reduce the country-specific characteristics that remain constant across time and the shocks occurring globally. This dual econometric approach ensures that the estimated parameters capture the true within-nation relationships between economic drivers and carbon emissions, without considering cross-country structural differences. The model specification thus captures the structural diversity of emerging economies, but at the same time ensures that the results in different countries and different time periods are comparable.

Although the dynamic panel methods used to measure the relationship between two variables or one lagged (e.g., System GMM or PVAR) are also appropriate, the current study used theoretically based IPAT and EKC models. As the relationship is unidirectional in nature, socioeconomic and technological factors have a unidirectional effect on environmental outcomes. Since the sample of cross-sectional units (24 emerging economies) is small and the time period from 2000 to 2019 is moderate, robust least squares is selected as the main estimator with specifications of fixed effects as the backup to establish robustness. This method is suitable for the theoretical basis of the model, the nature of the data, and research purposes.

Data

The current study considered 24 emerging economies, and data were collected for 20 years from to 2000 to 2019. The details of the study variables are provided below. The most dangerous gas emitted during human and economic activities, lives for a long time in the atmosphere, and contributes to global warming is CO₂. CO₂ emissions threaten environmental sustainability, and the combustion of fossil fuels is a major source of emissions. Therefore, CO₂ emission per capita (metric tons) is directly downloaded from www.data.worldbank.org. RET is equal to the share of renewable energy consumption in total energy consumption, and the industrial value-added to GDP is used as a proxy for INS (F. Dong et al., 2022). The data for POP, GDP per capita, RET, and INS is available at www.data.worldbank.org. The data for HC is obtained from https://rug.nl/ggdc/productivity/pwt/?lang=en. The moderator in the current study, FD, is crucial for economic growth. FD describe the provision of instruments and frameworks that makes the transaction easy and lower the cost of financial systems. FD is measured by considering the various indicators and comprehensive index of FD is developed by IMF. It includes six indicators that highlight the efficiency, accessibility, and depth of financial markets and institutions. It ranges from 0 to 1, and values close to 1 indicate higher FD. The data about FD index is available and accessible directly from www.data.imf.org. Table 1 presents the description of study variables.

Table 1.

Description of Variables.

IPAT model elements	Description	Proxy variable	Acronyms	Source
Environmental impact (I)	Environmental impact is measured as carbon footprints of an economy at time t	CO2 emission	CO2	WDI
Population (P)	Total Population at time t	Population	POP	WDI
Affluence/development (A)	Economic development of an economy	Gross domestic product	GDP	WDI
Technology	Technological innovation regarding energy	Renewable energy transition	RET	WDI
Other study variables
Financial development	Provision of instruments and frameworks that makes the transaction easy and lower the cost of financial systems	Financial development index	FD	IMF
Human capital	Average level of human capital per worker, based on years of schooling and the returns to education	Human capital index	HC	Penn World Table
Industrial structure	Role and strength of the industrial sector in the economy, indicating the level of industrialization and structural transformation	Industrial value-added to GDP	INS	WDI

Note. WDI = World Development Indicators; IMF = International Monetary Fund.

Results

Descriptive Statistics

Table 2 presents a descriptive analysis of the variables. Skewness indicates asymmetry of the variables, and a positive value indicates that all variables are positively skewed. Other parameters such as kurtosis determine the peak or flatness of the distribution. For normally distributed variables, the value of kurtosis should be 3; a value greater than 3 indicates a peaked distribution curve, whereas a kurtosis value lower than 3 indicates a flatter curve. Therefore, CO₂, POP, GDP, RET, and INS have kurtosis values greater than three, which ensures that these variables have peaked curves; only HC and FD have values lower than three, indicating a flat curve. In either case, it is concluded that not all variables are normally distributed, and the peaked curve also highlights the presence of outliers. Additionally, the p-value of the JB-test signifies the rejection of the null hypothesis, which confirms that the series is not normally distributed.

Table 2.

Descriptive Statistics of Variables.

Variable	CO2	POP	GDP	HC	FD	RET	INS
Mean	5.885	1.79E+08	9,227.58	2.58	0.41	21.70	32.95
Std. Dev.	5.587	3.40E+08	9,796.01	0.49	0.12	20.05	9.78
Min	0.498	3,275,333	755.48	1.46	0.17	0.00	13.36
Max	29.293	1.41E+09	62,139.59	3.67	0.68	88.10	66.43
Skewness	1.730	2.850	2.96	0.13	0.16	1.23	0.71
Kurtosis	6.440	9.640	13.89	2.56	2.47	4.46	3.47
Jb-Test	479.300	153.600	307.60	5.16	7.62	165.10	44.94
p-value	.000	.000	.00	.08	.02	.00	.00

Panel Unit Root Tests

To confirm the stationarity of the series, LLC, IPS, Pesaran’s CADF, and Fisher-ADF tests were used. All tests have a common null hypothesis, stating that the series is not stationary. The findings of all panel unit root tests are presented in Table 3, and indicate that all variables are stationary at 1st difference.

Table 3.

Stationarity Check of Variables.

Variables	LLC		IPS		Pesaran’s CADF		Fisher-ADF
	Level	First diff.	Level	First diff.	Level	First diff.	Level	First diff.
CO2	−0.97	−3.69*	1.70	−3.37*	−2.08	−3.12*	31.78	116.84*
Pop	−1.47	−5.37*	1.92	−3.87**	−2.53	−4.06*	26.48	89.27*
GDP	−2.22**	−4.24*	2.03	−1.84**	−1.97	−2.76*	51.58	94.27*
FD	−3.17*	−9.17*	−0.71	−8.08*	−2.78**	−4.11*	55.74	231.85*
HC	−6.82*	−3.22*	−1.29	−3.47*	−2.27	−3.89*	58.75	99.28*
RET	−0.15	−5.16*	3.84	−4.93*	−1.69	−2.75*	36.86	149.34*
INS	−4.29*	−8.17*	−0.28	−6.43*	−2.32	−3.10*	50.16	174.86*

*

and ** Shows significance level at 1% and 5%, respectively.

To determine the existence of long-run relationships among the variables across economies, we apply the Westerlund (2007) cointegration test. It considers cross-sectional dependencies and provides four tests indicating the group mean (Gt, Ga) and panel (Pt, Pa) tests. The findings of Westerlund (2007) in Table 4 regarding Pt and Gt reject the null hypothesis that there is no cointegration. Therefore, the results highlight the existence of a long-run relationship between variables.

Table 4.

Westerlund Cointegration Test.

Statistics	Value	Z-value	p-value
Gt	−2.88	−3.582	.000
Ga	−0.663	10.401	.940
Pt	−7.034	−4.475	.000
Pa	−1.277	8.398	.930

A robust least-squares procedure was applied to obtain the unknown coefficients. This technique provided reliable results in the case of a small sample size and outliers. Moreover, it addresses the problems of nonnormality and multicollinearity. Moreover, this technique is reliable for missing data (Naz & Aslam, 2023). The findings in Table 2 confirm that the variables are not normally distributed and there is a problem with outliers in the data. Therefore, the panel robust least-squares method is suitable for obtaining unknown parameters. Moreover, a fixed effect was used for robustness by applying the Mundlak method. This approach enhances the random effects specification with the variables. Compared with the Hausman test, this technique detects regressors related to individual effects.

Table 5 presents the findings of the robust least squares and fixed-effect regressions. Robust least squares indicate that all variables in M-1 are statistically significant. The coefficient of GDP indicates that it positively increases CO₂ emissions, which significantly degrade the environment. This finding implies that an increase in economic activity lowers the environmental sustainability. Similarly, a positive POP value indicates that population growth in emerging economies increases their CO₂ emissions. An increase in the number of individuals means an increase in the increased demand for conventional energy consumption. The increased use of traditional energy sources deteriorates environmental quality. The significant positive impact of FD on CO2 emissions demonstrates that financial sector development contributes to CO₂ emissions. Similarly, the positive and significant association of HC reduce environmental sustainability by increasing atmospheric CO₂ emissions. RET has a significantly negative impact on CO₂ emissions, highlighting that the transformation of the energy sector to renewable energy significantly lowers CO₂ emissions and improves environmental quality. The positive coefficient of INS implies that emerging economies’ INS strongly increases their CO₂ emissions. The fixed effect regression also provides the same findings regarding M-1, except for two variables, POP, which have an insignificant impact on CO₂ emissions.

Table 5.

Impact of Variables on CO2 Emission According to M-1 and M-2.

Variables	Robust least square					Fixed effect regression
	M-1		M-2		M-1		M-2
Constant	−3.244*	0.516	−6.011*	0.852	−2.164	.962	−4.77*	1.06
POP	0.110*	0.015	0.103*	0.016	0.058	.05	0.244*	.06
GDP	0.383*	0.030	1.090*	0.230	0.515*	.029	1.897*	.268
GDP2	—	—	−0.041*	0.013			−0.081*	.016
FD	0.496*	0.066	0.498*	0.067	0.028***	.013	0.052**	0.025
HC	1.170*	0.144	1.073*	0.153	0.218*	.102	0.26*	0.1
RET	−0.366*	0.014	−0.375*	0.014	−0.266*	.019	−0.236*	.019
INS	0.149*	0.053	0.158*	0.053	0.142*	.057	0.154*	.055
R 2	.88		.89		.72		.74

*

, **, and *** Shows significance level at 1%, 5% and 10%, respectively.

In M-2, additional variables such as GDP squared were included in the model, and both techniques confirmed the existence of the EKC in emerging economies. Therefore, the positive coefficient of GDP and the negative coefficient of GDP squared imply that an increase in economic activities increases CO₂ emissions, and after reaching a certain point, the rise in economic activities improves environmental quality by lowering CO₂ emissions. The impact of all the other variables in M-2 did not change and remained the same, as shown in M-1.

Table 6 provides the results of M-3, M-4, and M-5 with robust least squares and fixed-effect regressions. The models were specified to analyze the role of FD as a moderator in light of the EKC in emerging economies. All three models confirm the existence of EKC in emerging economies, while FD may have a moderating role in the relationship between HC (M-3), RET (M-4), and INS (M-5). In M-3, the interaction terms of HC and FD (HC × FD) and all other variables have a significant impact on CO₂ emissions. The reduced magnitude of the interaction term (HC × FD = 0.503) rather than the high individual impact of HC (0.931) implies that skilled individuals with FD in emerging economies enhance economic activities, leading to carbon emissions, but it may also promote the adoption of efficient technologies. This shows that FD may have a moderating role, which may facilitate HC to perform their economic activities efficiently.

Table 6.

Analyzing the Moderating Effect of Financial Development by Including the Interaction Terms.

Variables	Robust least square						Fixed effect regression
	M-3		M-4		M-5		M-3		M-4		M-5
Constant	−5.47*	1.46	−9.184*	0.954	−7.829*	1.197	−7.874*	1.173	−5.178*	0.925	−9.353*	1.137
POP	0.116*	0.022	0.060*	0.016			0.231*	0.068	0.164*	0.050	0.166*	0.06
GDP	0.065*	0.003	1.837*	0.218	0.538**	0.263	2.498*	0.292	1.764*	0.267	2.404*	.284
GDP2	−0.048*	0.020	−0.075*	0.013	−0.315*	0.015	−0.111*	0.017	−0.07*	0.015	−0.104*	.017
HC	0.931*	0.213					0.160*	0.003
FD × HC	0.503*	0.108					0.07*	.005
RET			−0.141*	0.025					−0.249*	0.022
FD × RET			−0.201*	0.020					−0.01*	0.012
INS					0.957*	0.060					0.203*	0.061
FD × INS					0.005*	0.001					0.015*	0.002
R 2	.74		.86		.79		.61		.73		.62

*

and ** Shows significance level at 1%, and 5% respectively.

In M-4, the significant negative impact of RET (−0.141) and interaction (RET × FD = −0.201) implies that FD strongly moderates the renewable energy transition. This demonstrates that emerging economies may also focus on FD to promote the adoption of renewable energy technologies. The fixed effects model also provided the same results.

For M-5, the findings revealed a significant positive impact of INS (0.203) and INS × FD (0.015) on CO₂ emissions in emerging economies. The magnitude of INS × FD is lower than the impact of INS on CO₂ emissions, which highlights that the INS interacting with FD may have little favorable impact on the environment. For example, INS increases CO₂ emissions, whereas with FD in economies, industries may start to adopt more environmentally friendly technologies that increase industrial production with minimum emissions.

Discussion

Emerging economies are often characterized by a high rate of population growth, increasing demand for energy, and rapid industrial expansion. All these factors significantly contribute to CO₂ emissions. Therefore, this study is crucial for emerging economies that face the dual challenge of achieving rapid economic development without compromising environmental sustainability. The inclusion of GDP square helps to understand the nonlinear relationship among variables to confirm the EKC hypothesis. Besides, the present study has investigated the moderating role of FD on HC, RET, and INS in the case of emerging economies, the outcomes of the study highlight the critical pathways through which emerging economies can augment their policies for sustainable environment. This study is closely connected with the Sustainable Development Goals (SDGs): affordable and clean energy (7th), industry, innovation and infrastructure (9th), and climate action (13th), which are highlighted as the major ones and can be helpful for policymakers aiming to contribute to economic development by protecting the environment for future generations.

The findings reveal a direct positive impact of POP on CO₂ emissions, implying that a rise in population in emerging economies strongly contributes to CO₂ emissions. POP is directly linked to the environment and primarily responsible for the emission of CO₂ into the atmosphere. A demographic NARDL and machine-learning study of China, India, and the USA concluded that in India, population growth raises CO₂ emissions; hence, demographic growth is a major contributor to environmental degradation in high-growth environments (M. Ahmed et al., 2023). Li et al. (2022) also found positive impact of population and urbanization on CO₂ emission and also identified the amplifying impact of demographic mobility and household consumption on carbon emission. Moreover, our findings regarding POP are in line with those of Naz and Aslam (2023). These findings indicate that, in addition to affluence and technology, population growth should be taken into account when creating carbon mitigation measures.

The results of the econometric models confirm the existence of the EKC hypothesis in emerging economies from 2000 to 2019. This implies that an increase in economic activity increases CO₂ emissions, and after reaching a certain point, a further increase in economic activity reduces CO₂ emissions. An inverted U-shaped relationship exists between economic growth and CO₂ emissions. Odei et al. (2025) also confirmed the EKC hypothesis in emerging economies.

The positive impact of FD on CO₂ emissions in emerging economies highlights that it boosts CO₂ emissions. FD attract the investment which augment the economic activities thereby increases the CO₂ emission. Moreover, development in the financial sector lowers financial cost (Charfeddine & Kahia, 2019), which attracts individuals to consume more durable automobiles. This change in consumption patterns increases energy demand leading to further pollution (Naz & Aslam, 2023). Finally, the availability of strong financial systems enhances traditional energy consumption as industries demand more energy as they expand in emerging economies (L. Wang et al., 2020). Furthermore, FD fosters physical infrastructure development leading to higher short-term CO₂ emissions.

The strong positive correlation between human capital (HC) and CO₂ emissions implies that a high level of skills and education leads to industrialization and higher productivity. This, in turn, increases the energy demand and carbon emissions. Adikari et al. (2023) also demonstrated that higher HC in the first place leads to higher CO₂ emissions. Likewise, Xiao et al. (2023) stated that HC raises emissions in lower-income countries, but only after a threshold does this effect start to decrease; however, it is less pronounced in emerging economies. Therefore, HC in emerging economies, with rapid economic and industrial growth, is likely to increase emissions unless well-sustained practice regimes are in place. However, when HC interacts with FD, the impact of the interaction term also persists but with a reduced magnitude. This implies that the interaction of educated and skilled individuals with FD can increase economic activities that continue utilizing the carbon-intensive process, but it also facilitates more efficient technologies and practices. This partially offsets the generally positive coefficient of the interaction term for CO₂. This indicates that, although financial systems enhance the level of CO₂, efficiency in the use of resources and better technologies offer a cushion to the extent of the impact.

The findings also revealed a significantly negative impact of RET on CO₂ emissions. Thus, supporting the use of clean energy sources in emerging countries. RET reduces the dependence on fossil energy sources, which are major sources of greenhouse gases (Hou et al., 2023). Therefore, integrating renewable energy into energy systems can not only directly address the emission problem but also help other industries address energy efficiency issues. This shift is especially important when it comes to decreasing the levels of carbon emissions in sectors that use a lot of energy (Yi et al. 2024). This is clear when we look at the effect of RET in relation to FD, as it has a further negative effect on CO₂ emissions. FD offers the funds as well as the financial structures for deploying large-scale renewable energy systems (Anton & Nucu, 2020). It also supports technological development in the field of clean energy efficiency, thereby improving the inventions in the field. It also guarantees that the available financial resources are directed toward sustainable energy solutions, increasing the pace and further solidifying the decrease in emissions (Seraj & Seraj, 2025).

In emerging economies, INS have been found to exert a significant and positive effect on CO2 emissions because of the dominant dependence of these sectors on energy-consuming industries such as manufacturing, construction, and heavy industries (Abdelaziz et al., 2011). Such industries are generally major economic and employment generators; however, they also use large amounts of fossil energy and emit high levels of GHGs. Evaluating Global Environmental Pollution Rate in the context of Emerging Economies clearly reveals that, as countries modernize faster, the output rate, competency factor, and emission rates increase, while the reduction in greenhouse gas emission rates is given low priority. Our findings are in line with those of Ayitehgiza (2020), as they also confirm the positive impact of INS on CO₂ emissions. However, when the INS comes into contact with the FD, the positive effect on CO₂ emissions still prevails but to a limited extent. Through the modernization of financial resources, industries can adopt better technologies for energy consumption and cleaner production (Ashton et al., 2018). For example, companies with better credit facilities, a measure of their capacity to secure funding (K. Zhang et al., 2021), are better placed to acquire renewable energy, efficient technologies, and ways of cutting emissions. These innovations reduce the effects of industrialization on the environment. Moreover, FD can help the structural change by turning away from high carbon emitting industries to low carbon emitting in emerging economies. This transition supports the moderating effect of conventional industry emissions on lowering the carbon intensity of the economy (J. Dong et al., 2020).

Conclusion

The existing problem of carbon emissions in the context of emerging economies was thoroughly investigated in this study to grasp the joint impact of economic growth, FD, industrial structure, human capital, and energy transition on environmental outcomes. Through the IPAT model framework, the econometric analysis of panel data from 24 emerging economies from 2000 to 2019 revealed that population, economic growth, the level of FD, human capital, and changes in industrial structure have marked impacts on the increased carbon footprint. However, energy transition is a mitigating factor that leads to a decrease in emissions in these economies. The Environmental Kuznets Curve hypothesis holds true, meaning that environmental quality deteriorates with economic growth and then starts to improve beyond a certain level of economic growth. FD reinforces the impact of the renewable energy transition. These findings offer a complex understanding of the means of encountering sustainable development and policy orientation.

Policy Recommendations and Study Limitations

The relationship between FD and carbon emissions is particularly important. FD provides resources for industries and governments, enabling them to invest in green technology and other sustainability-related activities. Policymakers in emerging economies should, therefore, prioritize building sound infrastructure for green financial instruments, including green bonds and carbon trading markets that promote more environmentally sustainable initiatives. There is also a need to compel financial institutions to target loans and funding sustainably related projects. Whether such initiatives are to be used to finance the public sector, or to offer alternative deals to private entities, complementary regulations are required to ensure that those financial mechanisms are directed toward viable and innovative projects. Linking FD to sustainable development initiatives can help emerging economies encourage the deployment of advanced transformative technologies and shift consumption patterns away from emitting industries while nurturing growth. Similarly, transitioning to clean energy sources and restructuring industries are essential for decreasing the carbon footprint. Government policies aimed at promoting renewable energy should include subsidies, tax exemptions, and cheap credits. This shift would decrease the reliance on fossils and ease energy vulnerability. In the industrial sector, increased stringency in environmental measures is required to ensure that resource-light and low-carbon technologies are applied. Such a shift can be promoted using one-off subsidization, guarantees, and policy regulations that would help industries commit to the targets of sustainable development. Additionally, service- and knowledge-based sector transitions can reduce environmental pollution from the manufacturing sector in emerging economies, thus supporting the prospect of a greener structure in emerging economies. Finally, human capital development is imperative for efficient growth and overcoming environmental issues. Education systems should include environmental sustainability in their curriculum so that the next generation can be awakened to environmental friendliness. Starting with the implementation of onshore and offshore vocational training programs in green skills and eco-innovation, a suitable workforce may be produced to drive change in a sustainable manner. Future work in this area could involve the development of new, environmentally friendly technologies and practices through the cooperation of academic institutions and industries in relation to governments. They make certain that human capital aligns with a spearhead decrease in carbon emissions and attains sustainable development goals.

Although this study presents valuable insights, it also has several limitations that provide open opportunities for future research. First, the 24 emerging economies are analyzed and might fail to represent cross-regional heterogeneity or institutional differences in other income groups. Second, the research uses data from 2019 or earlier, which might not be relevant to describing the post-pandemic dynamics of the financial system, energy transition policy, and industrial restructuring. Third, the panel robust least squares and fixed-effects estimations are effective in dealing with outliers and heteroscedasticity but not in the complete elimination of the possibility of endogeneity or dynamic feedback between variables. To expand on the current framework, future studies may use dynamic models such as system GMM or panel vector autoregression, use post-2020 data, and disaggregate the sector or region to gain deeper insight into the mechanisms that connect FD, human capital, and environmental sustainability.