Institutional and Financial Drivers of Renewable Energy Consumption and Carbon Emissions: Evidence from Developed Economies

Abstract

The study sheds light on the subtle interactions among financial development, foreign direct investment (FDI), and the quality of regulatory frameworks, with particular reference to their deep influence on renewable energy use and carbon emissions across 22 developed countries from 2002–2021. The results show an interesting tendency: Financial development and FDI will reduce reliance on renewable energy, whereas a significant increase in GDP per capita will increase reliance. Secondly, carbon emissions have a negative association with the adoption of renewable energy and financial development, though both reduce environmental quality; there is a positive relation between real gross domestic product (GDP) and energy depletion in terms of these toxic emissions. The significant role of regulatory quality as a moderator in this process is particularly striking. There is a direct correlation between financial stability and more robust regulation, resulting in reduced financial liquidity available for investing in renewable projects and restricting the free flow of clean FDI. Crucially, the paper argues that when combined with strong regulation, FDI is more likely to contribute to reductions in emissions, while FYGD, nevertheless regulated at a high level of quality, should raise emissions. Winding up, the result indicates that neither financial depth nor institutional quality, in isolation, is sufficient to deliver significant environmental improvement. Thus, it is urgent to adopt sound green finance policies and to formulate focused regulatory systems that integrate financial development and foreign direct investment with a broader sustainability agenda.

1. Introduction

The global economy, as a complex system, is deeply conditioned by the complexity of relations between production and consumption. These are all essential drivers of growth and development, but they also exert numerous pressures on the fragile natural environment and limited resources. The dominant global economic model, especially given the extensive and intensive use of fossil fuels, is among the main drivers of this distressing environmental trend. Several environmental issues, ranging from air and water pollution to deforestation and the persistent overexploitation of the natural environment, pose not only a threat to the fragile equilibrium of ecosystems but also an unhealthy burden on human health/development. Indeed, the immediate environmental issues have led to air pollution, resulting in an estimated 5 million premature deaths annually. As a result of this situation, air pollution causes approximately 5 million premature deaths each year. Additionally, water pollution and deforestation cost the global economy billions of dollars annually [1].

Significant structural changes have also been observed, along with a significant increase in total carbon emissions worldwide. In the first half of the 20th century, the majority of global carbon emissions originated from Europe and the United States. In 1900, over 90% of total emissions were produced by these two regions, and in 1950, over 85% were [2]. However, in the second half of the 20th century, the majority of global carbon emissions shifted to other regions, primarily Asia. China has become the country with the highest carbon emissions during this period. Today, the total carbon emissions of the US and Europe account for less than one-third of the global total [3].

In light of these negative effects, reducing greenhouse gas emissions has become a priority goal of global environmental policies. Historically, the priority in combating climate change has been given to controlling greenhouse gas emissions from production sources. Still, today it is understood that consumption-based carbon emissions also play a significant role. In this context, international initiatives such as the Paris Agreement aim to reduce greenhouse gas emissions by 25% and keep the global temperature increase below 2 °C, if possible, limiting it to 1.5 °C [1]. These goals require the development of balanced policies that balance economic growth and environmental protection. The energy sector is one of the largest sources of carbon emissions. The intensive use of fossil fuels in energy production exacerbates environmental problems, making the transition to renewable energy sources critical for both energy security and environmental sustainability. Developed countries are making significant investments in renewable energy technologies and implementing regulatory frameworks to promote their adoption. The mobilisation of financial resources, technological innovations, and effective regulations is a key factor in increasing renewable energy consumption [4].

However, as countries increase renewable energy consumption and reduce carbon emissions, the effects of financial and regulatory factors vary across countries. Improving regulatory quality ensures the efficient functioning of markets while financial development facilitates the channelling of investments into sustainable areas. Therefore, to better understand the relationship between renewable energy consumption and carbon emissions in developed countries, it is necessary to investigate the interactive effects of regulatory quality and financial development.

Climate change and environmental degradation are among the most critical issues awaiting a global solution. In this context, developed economies in particular are taking a leading role in achieving carbon-neutral targets due to both their energy consumption levels and their technology and capital structures. However, for this process to be successful, it is not enough to invest only in environmental technologies; the functioning of the financial system and the quality of the regulatory framework are also decisive. This study aims to guide policymakers and investors by revealing how these factors interact to advance environmental sustainability. Additionally, it offers a holistic perspective that emphasises that climate policies must be based not only on technical but also on an institutional and financial foundation.

From the above discussion, it can be concluded that there is very little evidence to suggest that institutional capacity has a significant impact on energy transformation and emission reduction, particularly in developed economies. There is also evidence that this does not exist in European countries [5]. This study makes a unique contribution to the literature by examining the interactive effects of two important structural elements, financial development and regulatory quality, on renewable energy consumption and carbon emissions, specifically in developed countries. Many studies in the literature have focused on the direct use of renewable energy or on the causes of carbon emissions; however, the combined effects of institutional factors, such as financial development and regulatory quality, on these environmental outcomes have not been sufficiently analysed. The specific objective of this study is to quantitatively determine the effects of financial and institutional factors on renewable energy consumption and carbon emissions, and to reveal the role of institutional quality in these effects. There is a need for empirical studies that explain the impact of institutional capacity on energy transformation and emission reduction, particularly in developed economies. This study aims to fill this gap.

Despite the extensive literature on renewable energy and environmental sustainability, an important scientific issue remains insufficiently clarified. While prior studies have examined the direct effects of financial development, foreign direct investment (FDI), and institutional quality on renewable energy consumption or carbon emissions, the interactive effects among these determinants have not been systematically analysed in developed economies. In particular, it remains unclear whether regulatory quality amplifies or weakens the environmental consequences of financial deepening and foreign capital inflows in structurally mature economic systems.

Moreover, most empirical studies rely on mean-based panel estimators, implicitly assuming homogeneous effects across countries. However, developed economies differ substantially in their levels of renewable energy penetration and carbon intensity. Therefore, the impact of financial and institutional variables may vary across different conditional distributions of environmental performance. Ignoring this heterogeneity may conceal important distributional dynamics. Accordingly, the scientific problem addressed in this study is to determine how financial development and FDI shape renewable energy consumption and carbon emissions under different regulatory environments, and whether regulatory quality acts as a conditioning institutional mechanism rather than a simple direct determinant. By employing a quantile-based panel framework, this study seeks to uncover the heterogeneous and interactive effects that remain underexplored in the existing literature.

This study consists of four sections following the introduction. In the second section, the relevant literature and hypotheses are presented. In the third chapter, the study’s methodology is presented, and the econometric method is explained. In the fourth chapter, the analysis findings are presented, and in the fifth and final chapter, the discussion and conclusions are provided.

2. Literature Review

Threats to environmental sustainability, particularly air pollution and global warming driven by human activities, continue to pose significant challenges for both researchers and policymakers [1]. In response, a substantial body of literature has emerged focusing on the economic, financial, and institutional determinants of environmental degradation. Recent studies increasingly emphasise that environmental outcomes are shaped not only by economic growth and energy structures but also by the quality of financial systems and institutional frameworks.

From a theoretical perspective, financial development can influence environmental quality through multiple channels. On the one hand, well-developed financial markets facilitate access to capital, lower financing costs, and promote investments in clean technologies and renewable energy projects, thereby contributing to environmental sustainability. On the other hand, financial deepening may stimulate economic expansion, industrialisation, and energy demand, potentially leading to higher carbon emissions, particularly in the absence of effective regulatory oversight. Similarly, regulatory quality plays a crucial role in shaping investment incentives, ensuring environmental compliance, and guiding financial resources toward sustainable activities. Consequently, the interaction between financial development, institutional quality, renewable energy consumption, and carbon emissions is inherently complex and context-dependent.

2.1. Regulatory and Financial Determinants of Renewable Energy Consumption

The impact of financial development on renewable energy consumption (REC) varies considerably across country groups, methodologies, and financial indicators. Several studies document a positive relationship, suggesting that financial deepening facilitates renewable energy investments by easing credit constraints and improving capital allocation efficiency. For instance, ref. [6] for Turkey and ref. [7] For India, we find that financial development significantly increases renewable energy consumption. Similarly, ref. [8] conclude, based on a sample of 55 countries, that financial sector development plays a pivotal role in accelerating the diffusion of renewable energy technologies.

However, contrary evidence also exists. Ref. [9] report a negative effect of financial development on renewable energy use in African economies, while ref. [10] find a similar pattern in South Asia. Ostadzad and Ghafoorian Yavarpanah [11] identify a U-shaped relationship between financial development and REC in OECD countries, indicating nonlinear dynamics. These mixed findings suggest that financial development does not uniformly promote renewable energy adoption and that country-specific structural and institutional factors play a decisive role.

Foreign direct investment (FDI), as another critical financial determinant, also exhibits heterogeneous effects on renewable energy consumption. While ref.[12] document a positive influence of FDI on REC in Sub-Saharan Africa, refs. [13,14] find negative effects in European economies and Somalia, respectively. Tan and Uprasen [15] report that FDI discourages renewable energy consumption in BRICS countries, whereas ref. [16] show that FDI supports renewable energy development globally. These conflicting results highlight that the environmental impact of FDI largely depends on host-country characteristics, including institutional quality, regulatory frameworks, and energy policies.

The role of regulatory quality in shaping renewable energy outcomes has gained increasing attention. Strong regulatory institutions can reduce policy uncertainty, enhance investor confidence, and promote long-term investments in renewable energy infrastructure. Nonetheless, empirical evidence remains mixed. While ref. [17] demonstrate that regulatory quality positively affects renewable energy consumption in a diverse sample of countries, ref. [18] found no significant effect, and ref. [19] even identify negative impacts. Ref.[20] further shows that regulatory improvements in South Africa may temporarily reduce the share of renewable energy in total consumption due to structural adjustment processes.

The transmission mechanism between aggregate FDI and renewable energy consumption may operate through multiple channels. On the one hand, FDI can promote renewable energy adoption through technology transfer, managerial know-how, and access to international capital markets. On the other hand, in developed economies characterised by mature fossil-fuel infrastructures, foreign capital may reinforce existing energy structures by financing established carbon-intensive sectors. This structural lock-in effect may explain why aggregate FDI does not automatically translate into increased renewable energy consumption. Therefore, the net impact of FDI depends on sectoral allocation patterns and the regulatory framework governing investment flows.

Overall, the literature suggests that financial development, FDI, and regulatory quality exert complex and sometimes contradictory influences on renewable energy consumption. This heterogeneity underscores the need for further empirical investigation, particularly in developed economies where institutional frameworks and financial systems are relatively advanced. Accordingly, the following hypotheses are proposed:

H1: 

Financial development increases renewable energy consumption in developed economies.

H2: 

Foreign direct investment reduces renewable energy consumption.

H3: 

Regulatory quality increases renewable energy consumption.

2.2. Regulatory and Financial Determinants of Carbon Dioxide Emissions

The impact of financial development on environmental outcomes is mixed and controversial, with both positive and negative effects reported [21,22,23]. The environmental consequences of financial development remain highly debated. While some studies argue that financial deepening promotes cleaner production technologies and energy efficiency, others suggest that it fuels industrial expansion and carbon-intensive activities. For instance, refs. [24,25] find that financial development reduces CO₂ emissions, whereas refs. [26,27,28,29] document a positive relationship. Additionally, ref. [30] reveal that the impact of financial development varies across different emission quantiles, indicating nonlinear effects.

Similarly, the relationship between foreign direct investment (FDI) and carbon emissions is highly context-dependent. According to the pollution haven hypothesis, multinational enterprises tend to relocate pollution-intensive production activities to host countries with relatively lax environmental regulations, thereby increasing carbon emissions [3,31]. In contrast, the pollution halo hypothesis argues that foreign investors introduce cleaner technologies, advanced managerial practices, and higher environmental standards, which can contribute to reducing environmental degradation and carbon emissions [32]. Empirical evidence supports both hypotheses, indicating that the net environmental impact of FDI critically depends on host-country characteristics, particularly regulatory quality, institutional capacity, and environmental policy frameworks [33,34,35].

Regulatory quality represents a central pillar of environmental governance. Effective regulatory institutions enhance policy enforcement, reduce regulatory uncertainty, and foster sustainable investment patterns. Several studies confirm that strong regulatory frameworks significantly curb carbon emissions [36,37,38,39], although opposing evidence exists for specific regions [40].

Given these mixed findings, further research is warranted to clarify the roles of financial development, FDI, and regulatory quality in shaping carbon emissions in developed economies. Accordingly, the following hypotheses are formulated:

H4: 

Financial development reduces carbon emissions in developed economies.

H5: 

Foreign direct investment reduces carbon emissions.

H6: 

Regulatory quality reduces carbon emissions in developed economies.

2.3. The Moderating Effect of Regulatory Quality on the Relationship Between Financial Development and Renewable Energy Consumption

The moderating influence of regulatory quality is equally critical in understanding the environmental implications of financial development and FDI. While financial expansion can intensify carbon emissions through increased production and energy use, strong regulatory institutions can mitigate these adverse effects by enforcing environmental standards and promoting cleaner technologies.

However, empirical studies examining this moderating role remain limited. Alinsato et al. [41] find that institutional quality negatively moderates the financial development–renewable energy relationship in African economies, while ref. [12] report that governance quality strengthens the positive impact of FDI on renewable energy use in Sub-Saharan Africa. These mixed results imply that the interaction between financial flows and institutional quality is highly context-specific.

Given the advanced institutional frameworks and financial systems in developed economies, it is plausible that regulatory quality enhances the effectiveness of financial development and FDI in promoting renewable energy adoption. Based on this reasoning, the following hypotheses are proposed:

H7: 

Regulatory quality strengthens the impact of financial development on renewable energy consumption.

H8: 

Regulatory quality strengthens the impact of foreign direct investment on renewable energy consumption.

2.4. The Moderating Effect of Regulatory Quality on the Relationship Between Financial Development and Carbon Dioxide Emissions

The moderating influence of regulatory quality is equally critical in understanding the environmental implications of financial development and foreign direct investment (FDI). While financial expansion can support environmental sustainability by facilitating access to green technologies and cleaner production processes, it may also intensify energy consumption and CO₂ emissions through increased economic activity and industrial expansion [22,24]. Consequently, the direction and magnitude of the relationship between financial development and environmental quality largely depend on countries’ institutional structures, particularly the effectiveness of regulatory frameworks.

A growing body of empirical literature demonstrates that regulatory quality significantly shapes the interaction between financial development and environmental outcomes. For instance, ref. [42] show that strong regulatory institutions in GCC countries substantially dampen the adverse environmental effects of financial development. Similarly, ref. [43] find that financial development in Saudi Arabia contributes to reducing carbon emissions only when supported by a robust institutional structure. Ref. [44], focusing on South Asian economies, report that high institutional quality mitigates the negative environmental consequences of financial expansion, reinforcing the importance of governance mechanisms in steering financial resources toward sustainable pathways.

Parallel evidence is also observed in the context of foreign direct investment. Ref. [45] reveals that FDI can contribute to environmental improvement in MENA countries, but only under conditions of strong institutional quality. Ref. [46] further demonstrate that the interaction between FDI and governance quality significantly reduces carbon emissions in the ECOWAS region. Consistent with these findings, Huang et al. [3] show that regulatory quality in G20 economies weakens the emission-enhancing impact of FDI, highlighting the pivotal role of regulatory frameworks in transforming foreign capital inflows into environmentally sustainable outcomes.

Taken together, these studies provide compelling evidence that regulatory quality serves as a critical moderating factor in the nexus between financial development, FDI, and carbon emissions. Strong regulatory institutions not only enhance environmental governance but also ensure that financial and investment flows are channelled toward cleaner technologies and sustainable economic activities. Considering this theoretical and empirical background, this study posits that regulatory quality plays a mitigating and moderating role in both the financial development–carbon emissions and FDI–carbon emissions relationships. Accordingly, the following hypotheses are formulated:

H9: 

Regulatory quality mitigates the impact of financial development on carbon emissions.

H10: 

Regulatory quality mitigates the impact of foreign direct investment on carbon emissions.

3. Data and Methodology

3.1. Methodology

The main objective of this study is to empirically examine the regulatory and financial determinants of renewable energy consumption and carbon emissions in developed economies. In this context, the effects of financial development and foreign direct investment on both renewable energy consumption (REC) and CO₂ emissions have been analysed. Additionally, the moderator-variable approach was used to examine how regulatory quality shapes the effects of these two economic variables on environmental outcomes. The study aims to improve predictive accuracy by including control variables, such as energy intensity and GDP per capita, in the model. In line with this objective, panel data were collected from 22 developed countries with complete data for the period 2002–2021. The data was obtained from multiple sources, including the World Development Indicators (WDI), the International Monetary Fund (IMF), and the International Energy Agency (IEA). The Regulatory Quality Index was used to measure regulatory quality. In the study, renewable energy consumption, which is an important indicator of environmental sustainability, is considered as one of the dependent variables. Renewable energy consumption is measured as a percentage of total final energy consumption. Foreign direct investment inflows are measured as a percentage of gross domestic product. To measure financial development, the Financial Development Index developed by the International Monetary Fund (IMF) was used. This index provides a comprehensive assessment of countries’ financial systems by covering key dimensions, including the depth, access, and efficiency of financial institutions and markets. GDP per capita is measured in 2015 constant US dollars. Carbon dioxide (CO2) emissions are measured in tons per capita. Finally, energy intensity is measured as the ratio of energy supply to gross domestic product (GDP) at purchasing power parity. The following fully logarithmic models were created with reference to the study variables. Within the scope of variable transformations, the logarithmic transformation, commonly used in the econometric literature, has been applied to improve the distributional properties of the series, reduce the impact of outliers, and address potential heteroscedasticity. However, since 48 of the total 440 observations for the foreign direct investment (FDI) variable contained negative values, it was not possible to apply the logarithmic transformation to these observations, as logarithms are mathematically undefined for negative values. Therefore, these observations were included in the model as missing values without any transformation. The remaining observations are entirely positive and contain no zero values, so there have been no technical issues with the logarithmic transformation. This approach is a standard practice frequently adopted in the empirical literature, aiming to maintain the statistical validity, consistency, and reliability of the estimation results [47,48,49].

The study estimated the following econometric models based on the frameworks proposed by Almulhim et al. [1] and Xu et al. [50], with adaptations made to align with the research objectives.

To examine the regulatory and financial determinants of renewable energy consumption (lnREC), Model 1 is defined as follows:

lnREC = f(lnFD, lnFDI, lnRQ, lnEI, lnPGDP)

(1)

The empirical form of the model is as follows:

lnREC_it = α_it + β₁ lnFD_it + β₂ lnFDI_it + β₃ lnRQ_it + β₄ lnEI_it + β₅ lnPGDP_it + ε_it

(2)

To examine the regulatory and financial determinants of carbon dioxide emissions (lnCO₂), Model 2 is defined as follows:

lnCO₂ = f(lnREC, lnFD, lnFDI, lnRQ, lnEI, lnPGDP)

(3)

In the models, lnREC represents renewable energy consumption, lnCO₂ represents carbon dioxide emissions, lnFD represents financial development, lnFDI represents foreign direct investment, lnRQ represents regulatory quality, lnEI represents energy intensity, and lnPGDP represents GDP per capita.

The empirical form of the model is as follows:

lnCO_2(it) = α_it + β₁ lnREC_it + β₂ lnFD_it + β₃ lnFDI_it + β₄ lnRQ_it + β₅ lnEI_it + β₆ lnPGDP_it + ε_it

(4)

In the models, α_i,t represents the constant term, β₁, β₂, …, β₆ represent the coefficients, and ε_i,t represents the error term.

To examine the moderating effect of regulatory quality on the financial determinants of renewable energy consumption (lnREC), Model 3 is defined as follows:

lnREC = f(lnFD, lnFDI, lnFDxlnRQ, lnFDIxlnRQ,lnEI, lnPGDP)

(5)

The empirical form of the model is as follows:

$$\begin{gathered} E{C}_{it}={\mathrm{\alpha }}_i+{\mathrm{\beta }}_1\ln F{D}_{it}+{\mathrm{\beta }}_2\ln FD{I}_{it}+{\mathrm{\beta }}_3\left(\ln F{D}_{it}\times \ln R{Q}_{it}\right) +{\mathrm{\beta }}_4\left(\ln FD{I}_{it}\times \ln R{Q}_{it}\right) \\ +{\mathrm{\beta }}_5\ln E{I}_{it}+{\mathrm{\beta }}_6\ln PGD{P}_{it}+{\mathrm{\epsilon }}_{it} \end{gathered}$$

(6)

To examine the moderating effect of regulatory quality on the financial determinants of carbon dioxide emissions (lnCO₂), model 4 is defined as follows:

lnCO₂ = f(lnREC, lnFD, lnFDI, lnFDxlnRQ, lnFDIxlnRQ,lnEI, lnPGDP)

(7)

$$\begin{array}{cc}\ln \mathrm{C}{{\mathrm{O}}_2}_{\mathrm{i}\mathrm{t}}& ={\mathrm{\alpha }}_{\mathrm{i}}+{\mathrm{\beta }}_1\ln \mathrm{R}\mathrm{E}{\mathrm{C}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\beta }}_2\ln \mathrm{F}{\mathrm{D}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\beta }}_3\ln \mathrm{F}\mathrm{D}{\mathrm{I}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\beta }}_4\left(\ln \mathrm{F}{\mathrm{D}}_{\mathrm{i}\mathrm{t}}\times \ln \mathrm{R}{\mathrm{Q}}_{\mathrm{i}\mathrm{t}}\right)+{\mathrm{\beta }}_5\left(\ln \mathrm{F}\mathrm{D}{\mathrm{I}}_{\mathrm{i}\mathrm{t}}\times \ln \mathrm{R}{\mathrm{Q}}_{\mathrm{i}\mathrm{t}}\right)\\ & +{\mathrm{\beta }}_6\ln \mathrm{E}{\mathrm{I}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\beta }}_7\ln \mathrm{P}\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}\end{array}$$

(8)

The moderator variable lnFDxlnRQ is shown in Models 3 and 4.

3.2. Data Set of the Research

The explanations for the variables listed in

Table 1. Variable Description and Measurement.

Variables	Type of Variable	Measurement	Notation	Data Source
Renewable Energy Consumption	Dependent/Independent variables	% of total final energy consumption	REC	WDI- Washington DC, USA
CO2 Emissions per capita	Dependent	ton/capita	CO2	IEA- Paris, France
Financial Development	Independent variables	Financial Development Index	FD	IMF- Washington DC, USA
Foreign direct investment, net inflows	Independent variables	% of GDP	FDI	WDI- Washington DC, USA
Regulatory quality	Moderator Independent variables	Regulatory Quality Index	RQ	WDI- Washington DC, USA
Energy Intensity	Control Variables	Energy intensity level of primary energy (MJ/$2017 PPP GDP)	EI	WDI- Washington DC, USA
Per capita GDP	Control Variables	Constant 2015 USD	PGDP	WDI- Washington DC, USA

can be summarised as follows. In the study, renewable energy consumption, a key indicator of environmental sustainability, is used as a dependent variable. This variable is measured as the share of renewable sources in total final energy consumption. The proportion of renewable energy use is critically important as it reflects countries’ sustainable energy policies, the level of low-carbon transformation, and environmentally friendly development strategies. Examining the factors that determine renewable energy consumption in developed economies is significant for both assessing policy effectiveness in combating climate change and evaluating the environmental consequences of institutional quality and financial development. Additionally, findings in the literature indicate that increased use of renewable energy reduces CO₂ emissions and strengthens energy security [51,52], which theoretically supports the inclusion of this variable in the model.

The IMF’s Financial Development Index was used to measure financial development. This index comprehensively assesses countries’ financial systems by covering the dimensions of depth, access, and efficiency of financial institutions and markets. The index’s preference stems from its strong framework for explaining the direct and indirect effects of financial development on environmental indicators. The literature states that financial development facilitates the financing of renewable energy investments and encourages a shift toward environmentally friendly technologies [53,54]. In this regard, financial development is the main independent variable in the study, as it is a significant determinant of both renewable energy consumption and carbon emissions.

Foreign direct investment (FDI) is a significant source of capital that contributes to economic growth and technological renewal in countries. In this study, FDI was used to examine how foreign capital inflows to developed economies affect renewable energy investments and environmental sustainability. The use of FDI enables analysis of its potential to reduce carbon emissions by supporting not only economic growth but also the transfer of environmentally friendly technology and the transformation of the energy structure. The literature highlights the role of foreign direct investment in reducing carbon emissions by increasing renewable energy investments [55,56,57,58,59]. Therefore, the FDI variable was included in the model as a significant determinant of renewable energy consumption and carbon emissions in the study. Foreign direct investment (FDI) is measured as total net inflows expressed as a percentage of GDP, obtained from the World Development Indicators (WDI). It should be noted that this indicator captures aggregate FDI inflows rather than energy-sector-specific investments. Therefore, the estimated effects reflect the broader macroeconomic impact of foreign capital rather than targeted renewable energy investments. This distinction is important, particularly in developed economies where FDI may be directed toward both carbon-intensive and clean technology sectors.

CO₂ emissions are one of the key indicators that directly reflect the environmental impact of economic activities. In this study, CO₂ emissions were considered as the dependent variable to measure environmental sustainability and evaluate the impact of corporate and financial factors on the environment in developed economies. Revealing the role of determinants such as renewable energy consumption, financial development, and foreign direct investment in reducing carbon intensity is critically important for evaluating the effectiveness of climate policies. The literature emphasises that CO₂ emissions are closely related to economic growth and energy structure, making them an indispensable variable for measuring the results of environmental policies and financial reforms [54,55,60,61].

The study used the Regulatory Quality Index as a moderator variable. This index reflects perceptions of the government’s capacity to create and implement effective policies and regulations that support the development of the private sector and is considered one of the fundamental elements of governance quality. The literature emphasises that regulatory quality plays a significant role in environmental quality, particularly in shaping and implementing environmental policies [51]. Therefore, considering regulatory quality as a moderating variable is important for examining how the relationship between financial indicators and environmental outcomes varies across institutional contexts. Given that strong institutional structures in developed countries can enhance the effectiveness of environmental policies, the impact of renewable energy investments and environmental policies on carbon emissions may be more pronounced in environments with high regulatory quality.

GDP per capita is a fundamental indicator of economic development and can directly or indirectly affect environmental indicators. Although developed countries were examined, GDP per capita was used as a control variable to account for differences in development across countries. The literature indicates that economic growth has complex, often non-linear relationships with environmental degradation; therefore, including the growth variable in the model enables a more robust assessment of the effects of other independent variables. Indeed, various studies have shown that GDP per capita increases CO₂ emissions in both developing economies [62] and MENA countries [63]. Therefore, adding the variable in question as a control variable strengthens the model’s consistency and validity.

Energy intensity is the amount of energy consumed per unit of economic output and is an important indicator of energy efficiency. In this study, energy intensity was used to control its effects on renewable energy consumption and carbon emissions. High energy intensity indicates that economic activities consume more energy resources, which can lead to increased carbon emissions. Including energy intensity in the model allows for separating the economic structure from the impact of energy efficiency on environmental outcomes. The literature emphasises that energy intensity is a critical factor influencing the relationship between energy consumption and environmental impacts, and that increasing energy efficiency is a significant strategy for reducing carbon emissions [64].

3.3. Estimation Strategy

The study, which encompasses a panel data set from 22 developed countries (see Appendix A), has determined the econometric method to be followed within a phased, consistent, and theoretically grounded framework. First, given the presence of cross-sectional dependence and potential unit root problems in the panel data, the stationarity of the series was examined using CADF and CIPS panel unit root tests that account for cross-sectional dependence. The findings reveal that the variables have mixed integration orders (I (0) and I (1)). This situation is a common characteristic encountered in macroeconomic panel data studies, necessitating the testing of the existence of a long-term equilibrium relationship among the variables through cointegration analysis [50,65,66].

In this context, the refs.[67,68] the panel cointegration test was applied to examine the long-term relationship between the variables. After detecting the cointegration relationship, Machado and Silva developed the Moment Method Quantile Regression (MMQR) estimator [69]. They used to reveal how this relationship changes not only through the average effect but also across different points of the conditional distribution. The MMQR method allows for a more comprehensive analysis of the asymmetric and heterogeneous nature of relationships among variables, providing deeper, more robust inferences than traditional mean-based methods.

Additionally, the ref. [70] the slope homogeneity test was applied to assess parameter heterogeneity arising from differences in income levels and structural differences across countries. To test the reliability and robustness of the obtained key findings, various robustness analyses were conducted using OLS, GMM, and Fixed Effects–Driscoll-Kraay estimators. Finally, VIF statistics were examined to assess multicollinearity among the explanatory variables. This holistic econometric approach aims to enhance the statistical validity, consistency, and reliability of the obtained results.

3.3.1. CADF and CIPS Test

To assess the cointegration properties of the variables, the CADF and CIPS tests, second-generation unit root tests proposed by Pesaran, were applied. These tests are both more up-to-date and can produce more effective, accurate empirical results by accounting for cross-sectional dependence and heterogeneity within the sample. The equations for these tests are presented below in Equations (9) and (10).

$$∆Y_{it}={\mathrm{\alpha }}_i+{\mathrm{\beta }}_i{Y}_{i,t-1}+{\mathrm{\gamma }}_i\overline{Y_{t-1}}+{\sum }_{j=0}^q{\mathrm{\alpha }}_{ij}∆Y_{i,t-j}+{\sum }_{j=0}^q{\mathrm{\gamma }}_{ij}∆\overline{Y_{t-j}}+{\mathrm{\epsilon }}_{it}$$

(9)

$$CIPS\left(N,T\right)=N^{-1}{\sum }_{i=1}^N{t}_i\left(N,T\right)$$

(10)

In the equations above, a₁ represents the deterministic term q represents the lag order, and Y_t represents the cross-sectional average at time t

3.3.2. Slope Homogeneity Test

Another important issue to consider when analysing time series data is the assumption of slope heterogeneity. In this context, they conducted a pioneering study that tested whether the slope coefficients were homogeneous using the slope homogeneity test developed by [70]. The equation for this test is presented below in Equations (11) and (12)

$$∆=\sqrt{N}{\displaystyle \frac{\widehat{S}-k}{\sqrt{2k}}}$$

(11)

$${∆}_{\mathrm{a}\mathrm{d}\mathrm{j}}=\sqrt{\mathrm{N}}{\displaystyle \frac{\widehat{\mathrm{S}}-\mathrm{E}\left(\widehat{\mathrm{S}}\right)}{\sqrt{\mathrm{V}\mathrm{a}\mathrm{r}\left(\widehat{\mathrm{S}}\right)}}}$$

(12)

where $N$ denotes the number of cross-sectional units, $k$represents the number of independent variables, $\widehat{S}$ is the Swamy test statistic, $E(\widehat{S})$is its expected value, and $Var(\widehat{S})$ denotes its variance. If the homogeneity hypothesis is rejected, it is concluded that the slope coefficients are heterogeneous, meaning they differ across sections.

3.3.3. Pedroni Panel Cointegration Test

The panel cointegration test developed by [67,68] is one of the widely used methods for detecting long-term relationships (cointegration) between variables in panel datasets consisting of multiple cross-sections. The most significant advantage of this test is that it yields more robust results than unit root tests, thanks to the additional information provided by panel data. Pedroni presents different test statistics that account for heterogeneity (differences specific to sections) and for both cross-sectional dependence and independence assumptions. The test includes various statistics examining cointegration both within individual sections (within-dimension) and across the entire panel (between-dimension).

The model equation for this test is as presented in Equation (13).

$$Y_{it}={\mathrm{\alpha }}_i+{\mathrm{\delta }}_{i}t+{\mathrm{\beta }}_i{X}_{it}+{\mathrm{\epsilon }}_{it}$$

(13)

where ${\alpha }_i$ represents individual-specific intercepts, ${\delta }_{i}t$ captures deterministic time trends, ${\beta }_i$ denotes heterogeneous slope coefficients, and ${\epsilon }_{it}$ is the error term.

Every test evaluates whether the error terms of the variables are stationary. The null hypothesis is that the error terms have a unit root, meaning there is no cointegration. If the null hypothesis is rejected, the existence of panel cointegration between the variables is accepted.

3.3.4. Quantile Regression

Since the study’s variables are cointegrated, the relationship was examined using the Quantile Regression Based on the Method of Moments (MMQR), developed by [69]. It has been widely used recently. This method is used not only to reveal heterogeneous effects but also to show the distributional effects within each quantile being evaluated. Traditional quantitative regressions can perform poorly when there is unobserved heterogeneity in panel data. In contrast, the MMQR method accounts for covariance effects by considering conditional heterogeneity and provides robustness against cross-sectional dependence (CD). Additionally, the MMQR method, which is common in traditional quantitative regressions and can lead to invalid estimates, overcomes endogeneity and provides robust, reliable estimates even in its presence. The model for this test is presented below in Equation (14).

$$Y_{it}={\mathrm{\alpha }}_i+X_{it}^{\prime }\mathrm{\beta }+{\mathrm{\epsilon }}_{it}$$

(14)

where $i=\mathrm{1,2},\dots ,N$ denotes the cross-sectional units, $t=\mathrm{1,2},\dots ,T$ represents time, and $X_{it}$ is a $K$-dimensional vector of explanatory variables.

K-dimensional vector of unknown explanatory variables, as shown in Equation (15) below.

$$X_{it}={\left(X_{1it},X_{2it},\dots ,X_{Kit}\right)}^{\prime }$$

(15)

The MMQR equation proposed by Machado and Silva [67] can be calculated using Equation (16) below.

$$Y_{it}={\mathrm{\alpha }}_i+X_{it}^{\prime }\mathrm{\beta }+{\mathrm{\sigma }}_{i}q\left(\mathrm{\tau }\right)+u_{it}$$

(16)

The equation given above, where (X_i,t) and X′_I,t stand for the quantile distribution of the explained variable and vector of the explanatory variables (lnREC_i,t, lnRO_i,t) in the natural logarithms, respectively. Scalar coefficients stand for L_i,t − ϕi(τ) ≡ ϕ_i + λiq(τ), implying the fixed-effect quantile τ for the associated cross section i. However, it is important to emphasise that the individual effect does connote an intercept shift as obtained in the conventional least squares fixed effects. While the parameter coefficients do not change over time, the heterogeneous effects may vary with the dependent variable J in the conditional quantile distribution. q(τ) represents the τ-th sample quantile, which is obtained by optimising the function given in Equation (17).

$$\underset{\mathrm{\beta }}{\min }{\sum }_{i=1}^N{\sum }_{t=1}^T{\mathrm{\rho }}_{\mathrm{\tau }}\left(Y_{it}-X_{it}^{\prime }\mathrm{\beta }\right)$$

(17)

To test the robustness of the estimated parameters, in addition to the MMQR test, the OLS, GMM, and FE-Driscoll-Kraay methods were also applied. Figure 1 shows the methodological framework of the current study.

4. Results and Discussion

4.1. Descriptive Method

According to the descriptive statistics in

Table 2. Descriptives.

Variable	Obs	Mean	Std. Dev	Minimum	Maximum	Jarque-Bera
lnREC	440	2.378	1.142	−0.693	4.117	47.53 a
lnCO2	440	2.039	0.381	1.116	2.965	1.201
lnRQ	440	0.352	0.279	−0.717	0.811	111.5 a
lnFD	440	−0.324	0.204	−1.272	0	122.6 a
lnFDI	392	1.065	1.35	−6.398	4.454	139.3 a
lnEI	440	1.286	0.327	0.086	1.948	17.46 a
lnPGDP	440	10.571	0.485	8.941	11.453	167.6 a

Note: ^a = significant at the 1%.

, the variables exhibit substantial variation. Renewable energy consumption (lnREC) exhibits moderate dispersion, while carbon emissions (lnCO₂) display relatively low variability, suggesting more stability across developed economies. The Financial Development Index (lnFD) shows limited dispersion compared to foreign direct investment (lnFDI), which presents considerable volatility, as reflected by its wide minimum and maximum values. This indicates heterogeneous capital inflows across countries and years.

Regulatory quality (lnRQ) and GDP per capita (lnPGDP) demonstrate moderate variability, consistent with structural differences among developed economies. Energy intensity (lnEI) also shows noticeable dispersion, reflecting differences in energy efficiency levels.

The Jarque-Bera statistics indicate that all variables, except lnCO₂, reject the normality null at the 1% significance level. This non-normal distribution further justifies the use of quantile-based estimation methods, such as MMQR, which are robust to departures from normality.

4.2. Correlation Test Results

Table 3. Matrix of correlations.

Variable	lnREC	lnRQ	lnFD	lnFDI	lnEI	lnPGDP	lnCO2
lnREC	1.0000
lnRQ	0.065	1.000
lnFD	−0.293	0.164	1.000
lnFDI	−0.186	0.371	−0.112	1.000
lnEI	−0.066	−0.159	−0.117	−0.188	1.000
lnPGDP	0.050	0.599	0.598	0.113	−0.310	1.000
lnCO2	−0.430	0.134	0.274	−0.006	0.451	0.253	1.000

shows the correlation coefficients between the main variables used in the study. The findings reveal low to moderate correlations between the variables. This situation indicates that multicollinearity is not serious. Specifically, positive and significant relationships are observed between GDP per capita (lnPGDP) and the financial development (lnFD) and regulatory quality (lnRQ) variables (0.598 and 0.599, respectively). This situation may indicate that economic growth moves in tandem with the development of the financial system. In contrast, the correlation between renewable energy consumption (lnREC) and carbon emissions (lnCO₂) is negative and significant (−0.430). This finding suggests that increased use of renewable energy can support environmental sustainability.

4.3. Panel Unit Root Test Results

When examining the CADF test results provided in

Table 4. Panel data unit root test results.

Variables	Test Method	I (0)	I (1)	Level of Integration
		t-Stat.	t-Stat.
lnREC	CADF	−1.902	−3.066 a	I (1)
	CIPS	−2.507 a	−4.725 a	I (0)
lnRQ	CADF	−1.325	−2.844 a	I (1)
	CIPS	−1.957	−4.461 a	I (1)
lnFD	CADF	−2012	−4.059 a	I (1)
	CIPS	−2.272 a	−4.628 a	I (0)
lnFDI	CADF	−0.166	−3.239 a	I (1)
	CIPS	−3.746 a	−5.644 a	I (0)
lnEI	CADF	−2.246 a	−3.014 a	I (0)
	CIPS	−2.510 a	−4.433 a	I (0)
lnPGDP	CADF	−1.160	−2.969 a	I (1)
	CIPS	−1.054	−3.056 a	I (1)
lnCO2	CADF	−2.059	−3.256 a	I (1)
	CIPS	−2.331 a	−4.468 a	I (0)

Note: ^a = significant at the 1%.

, it is observed that only the lnEI variable is stationary at the level, while the other variables have a unit root but become stationary at the I (1) level after taking their first differences. When examining the CIPS test results, it is observed that the variables lnRQ and lnPGDP have a unit root at the level, but become stationary at the I (1) level when their first differences are taken, while the other variables are stationary at the level.

4.4. Homogeneity Test Results

Since the study involved countries with different income levels, this could lead to heterogeneity in the estimations. In this case, the slope homogeneity test developed by [70] was applied to determine the correct predictor, and the results are presented in

Table 5. Results of the slope homogeneity.

Models	Test	Value	p-Value
Model 1	% (Delta)	10.042	0.000
	adjusted	12.888	0.000
Model 2	% (Delta)	6.071	0.000
	adjusted	8.178	0.000
Model 3	% (Delta)	8.462	0.000
	adjusted	11.400	0.000
Model 4	% (Delta)	4.602	0.000
	adjusted	6.541	0.000

. According to the slope homogeneity test results in Table 5, the null hypothesis of slope homogeneity was rejected at the 1% significance level, as the p-values (0.000) for both the delta and adjusted delta tests were statistically significant for all four models. Consequently, it was concluded that the models were heterogeneous.

4.5. Cointegration Test

The results of Pedroni’s [67,68] The panel cointegration test indicates the existence of a long-term relationship between the variables. According to the findings in

Table 6. Cointegration test Results.

Test Stats.	Model 1		Model 2
	Panel	Group	Panel	Group
Panel v-Statistic	−0.267		−1.238
Panel rho-Statistic	1.937	3.545	3.361	5.35
Panel PP-Statistic	−4.286	−5.185	−3.104	−2.357
Panel ADF-Statistic	5.638	9.67	6.777	10.06

, both the panel t-statistics (−4.286; −3.104) and the group t-statistics (−5.185; −2.357) are significant and negative at the 1% level. These results strongly support the idea that the countries in the panel share a long-term equilibrium relationship. Although the panel rho and group rho statistics produced positive values, the highly significant negative t-statistics overshadow this and provide evidence in favour of cointegration. Overall, the findings confirm that the model’s variables move together in the long run and that a significant cointegration relationship exists.

4.6. MM-Quantile Regression Results

After cointegration was detected among the variables, the MMQR method was applied to reveal their relationships using percentile (quantile) values across all models, and the results are presented in

Table 7. Results from the method-of-moments quantile regression.

Models	Variables	Location	Scale	Quantiles
				0.10	0.30	0.50	0.70	0.90
Model 1	lnFD	−3.170 a	0.53012	−4.1761 a	−3.4042 a	−3.0711 a	−2.7846 a	−2.3910 a
	lnFDI	−0.27519 a	0.11607 b	−0.49550 a	−0.32647 a	−0.25354 a	−0.19082 b	−0.10463 b
	lnRQ	0.14604	0.04544	0.05978	0.12596	0.15451	0.17907	0.21281
	lnEI	−0.24328	−0.04536	−0.15719	−0.22324	−0.25174	−0.27626	−0.30994
	lnPGDP	0.89935 a	−0.25919	1.3913 a	1.0138 a	0.85101 a	0.71096 b	0.51851 b
	_cons	−7.6391 b	3.5878	−14.448 a	−9.2242 a	−6.9700 b	−5.0313	−2.3674
Model 2	lnREC	−0.15653 a	0.01118	−0.17542 a	−0.16323 a	−0.15666 a	−0.14772 a	−0.13964 a
	lnFD	−0.24247 a	0.14558 a	−0.48834 a	−0.32973 a	−0.24419 a	−0.12777 a	−0.11261 a
	lnFDI	−0.01440	0.00776	−0.02752	−0.01906	−0.01449	−0.00828	−0.00267
	lnRQ	−0.05759	0.10023 a	−0.22687 a	−0.11767 c	−0.05877	0.02138	0.09379
	lnEI	0.66971 a	0.10447 a	0.49326 a	0.60709 a	0.66848 a	0.75202 a	0.82750 a
	lnPGDP	0.43655 a	0.00691	0.42487 a	0.43241 a	0.43647 a	0.44200 a	0.44700 a
	_cons	−3.1041 a	-0.02000	−3.0703 a	−3.0921 a	−3.1038 a	−3.1198 a	−3.1342 a
Model 3	lnFD	−2.2681 a	0.32702	−2.8481 a	−2.4802 a	−2.2564 a	−2.0340 a	−1.7924 a
	lnFDI	0.12400	−0.09275	0.28850	0.18416	0.12068	0.05762	−0.01091
	lnRQ	−0.14718	−0.39411	0.55180	0.10844	−0.16128	−0.42926	−0.72050 c
	lnFDxlnRQ	−2.5016 a	−0.59016	−1.4549 c	−2.1188 b	−2.5227 a	−2.9240 a	−3.3601 a
	lnFDIxlnRQ	−1.0437 a	0.52685 c	−1.9781 b	−1.3855 a	−1.0249 a	−0.66670 b	−0.27737 c
	lnEI	−0.37586 c	0.06894	−0.49814	−0.42058	−0.37339 c	−0.32651 b	−0.27557
	lnPGDP	0.92903 a	−0.14128	1.1796 b	1.0206 a	0.92397 a	0.82791 a	0.72350 a
	_cons	−7.5375 a	2.1152	−11.289 b	−8.9094 a	−7.4618 a	−6.0236 a	−4.4605 b
Model 4	lnREC	−0.15973 a	0.02227 a	−0.19931 a	−0.17389 a	−0.15590 a	−0.14029 a	−0.13002 a
	lnFD	−0.37175 a	0.02599	−0.41793 b	−0.38827 a	−0.36728 a	−0.34907 a	−0.33707 a
	lnFDI	0.00075	0.00013	0.00050	0.00066	0.00077	0.00087	0.00093
	lnRQ	0.19626 b	0.30867 a	−0.35219 b	0.00002	0.24932 b	0.46563 a	0.60806 a
	lnFDxlnRQ	0.61981 a	0.62912 a	−0.49800	0.21985	0.72795 a	1.1688 a	1.4590 a
	lnFDIxlnRQ	−0.05958 c	0.00931	−0.07613 c	−0.06550 c	−0.05798 c	−0.05145 c	−0.04716 c
	lnEI	0.66117 a	0.07102 a	0.53497 a	0.61601 a	0.67337a	0.72314 a	0.75591 a
	lnPGDP	0.45240 a	0.01254	0.43010 a	0.44442 a	0.45455 a	0.46335 a	0.46914 a
	_cons	−3.3051 a	−0.10596	−3.1168 a	−3.2377 a	−3.3233 a	−3.3975 a	−3.4464 a

Note: (^a,b,c), respectively, significance at the 1%, 5% and 10% levels.

.

According to Model 1 results, RQ (Regulatory Quality) and EI (Energy Intensity) have no statistically significant effect on renewable energy consumption (REC) at any quantile level. The relationship between RQ and REC is positive but insignificant, while the relationship between EI and REC is negative and insignificant. Although these variables are theoretically expected to be important in developed countries, current infrastructure saturation and the dominance of other determinants may have limited their significance. The findings are consistent with Satrianto et al. [18]. Regarding the RQ-REC relationship and Yu et al. [71] regarding the EI-REC relationship. On the other hand, a statistically significant negative relationship between financial development (FD) and REC was found at the 1% significance level. These findings are consistent with studies by [72] on the economies of Vietnam and other ASEAN member countries, as well as with those by [73] for the Tunisian economy [10], and for selected South Asian countries [64] for the Chinese economy. A significant negative relationship was found between FDI and REC at low and medium quantile levels (1%) and at the 5% level at high quantile levels. These results are consistent with studies on Europe [14], the G-7 [74], BRICS [16], South Asia [75], and other selected countries [76].

The literature generally shows that increased FDI reduces renewable energy consumption. This situation can be explained by the fact that in developed countries, fossil fuel-based infrastructure and strong fossil fuel lobbies limit investments in renewable energy [77,78,79]. Additionally, the high cost and technological uncertainties in renewable energy investments also weaken FDI’s orientation toward this sector. According to Model 1’s other findings, there is a significant, positive relationship between PGDP and REC at the low and medium quantiles (1%) and at the high quantile (5%). These results are consistent with studies conducted in Indonesia [80], G-7 countries [81], and EU countries [82]. There is also extensive evidence in the literature that an increase in per capita income leads to an increase in renewable energy consumption [83,84,85].

When the results were evaluated for model 2, the findings showed that, as expected, REC statistically and negatively affected CO₂ emissions at all quantile levels with a significance level of 1%. These findings are consistent with studies conducted by ref. [86] for 27 OECD countries, ref. [87] for the US economy, ref. [88] for Belt and Road Initiative countries, ref. [89] for 53 countries, and ref. [90] for countries most dependent on natural resources. All these studies confirm that increased renewable energy consumption reduces CO2 emissions. This negative relationship can be attributed to factors such as advanced technological infrastructure, strict environmental regulations, and a high level of environmental awareness in developed economies. These elements facilitate the effective substitution of fossil fuels with cleaner renewable energy sources, thereby reducing greenhouse gas emissions. It has been observed that FD significantly and negatively affects CO₂ emissions at all quantile levels with a significance level of 1%. These results are consistent with studies conducted by ref. [25] on the USA, ref. [91] on Gulf Cooperation Council countries, ref. [29] on China, ref. [92] on Vietnam, ref. [55] on South Africa, and ref. [54] on BRIC countries. The literature generally supports the view that financial development reduces emissions. In developed economies, this situation can be explained by the efficiency of capital allocation, increased financing opportunities for renewable energy and green technologies, and the integration of sustainability criteria into financial systems. It was found that FDI has a negative but statistically insignificant impact on CO₂ emissions. These findings are consistent with studies by ref. [44] on ECOWAS, ref. [93] on China, ref. [94] on high and middle-income countries, ref. [95] on 188 countries, and ref. [54] on BRICS. While the emission-reducing effect of FDI is generally emphasised, the lack of a significant relationship in developed countries may be due to FDI being directed toward both environmentally friendly and carbon-intensive sectors, as well as the saturation of environmental regulations. The effect of regulatory quality on CO₂ emissions is significant and negative only at the low quantiles (0.10, 0.30); no significant relationship was found at other quantiles. It has been determined that energy intensity and GDP per capita positively affect CO₂ emissions at all quantile levels with a significance level of 1%. The direction of the EI-CO₂ relationship supports the findings of ref. [96] in China, ref. [97] in the USA, and ref. [98] in Turkey. The high energy demand in developed countries and the fossil fuel-dominated energy structure explain this situation. The positive relationship between PGDP and CO₂ emissions is consistent with the findings presented by [99,100,101,102]. The increase in energy consumption with rising per capita income, leading to more vehicle use, increased energy demand in households, and expansion of production activities, results in increased CO₂ emissions in developed economies.

The results of Model 3 show that regulatory quality (RQ) has a statistically significant and negative effect on the relationship between financial development (FD) and renewable energy consumption (REC) at all quantile levels. This situation reveals that regulatory quality weakens the impact of FD on REC and that financial development in advanced economies can work to reduce rather than promote renewable energy investments. Accordingly, the likelihood of financial resources being diverted to shorter-term, lower-risk, or fossil-fuel-based sectors rather than renewable energy under high regulatory quality conditions may increase. Therefore, high regulatory quality alone is not sufficient for financial development to translate into environmental benefits; the regulatory framework must also be aligned with green finance and sustainability goals. Similarly, in the relationship between foreign direct investment (FDI) and regional economic communities (RECs), the effect of regulatory quality (RQ) was found to be statistically significant and negative. This result indicates that the positive impact of FDI on renewable energy is weaker, or even negative, in high regulatory quality environments.

The results of Model 4 show that regulatory quality (RQ) has no significant effect on the relationship between financial development (FD) and carbon dioxide emissions (CO₂) at low quantile levels (0.10 and 0.30), but has a statistically significant and positive effect at medium (0.50) and high quantile levels (0.70 and 0.90). The interaction results indicate that, in higher emission quantiles, regulatory quality does not fully offset the emission-related effects of financial development, and may condition the magnitude and direction of this relationship. Therefore, in economies above a certain emission threshold, financial development can lead to higher carbon emissions despite high regulatory quality. These results provide indirect support for the pollution haven hypothesis and suggest that more targeted policy instruments are needed to reduce carbon emissions, beyond the overall quality of the regulatory framework’s governance. Additionally, it was found that regulatory quality (RQ) is significant and negative at all quantile levels in the relationship between foreign direct investment (FDI) and CO₂ emissions. This finding indicates that the environmental impacts of FDI are better controlled in developed countries with high regulatory quality and that strict environmental regulations help reduce carbon emissions.

4.7. Robustness and Additional Analysis

OLS, System-GMM, and FE-Driscoll-Kraay methods were used to check the robustness of the findings. The findings related to these methods are presented in

Table 8. Outcomes of OLS, GMM, and FE-Driscoll-Kraay.

	Variables	OLS			GMM			FE-Driscoll-Kraay
		Coeff.	t. Stats	Prob	Coeff.	t. Stats	Prob	Coeff.	t. Stats	Prob
Model 1	lnFD	−3.170	−9.90	0.000 a	−0.8365	−0.68	0.497	−0.2205	−1.47	0.157
	lnFDI	−0.2751	−6.55	0.000 a	−0.1803	−2.44	0.015 b	−0.0266	−2.82	0.011 b
	lnRQ	0.1460	0.58	0.563	0.2578	0.74	0.458	0.0542	0.76	0.456
	lnEI	−0.2432	−1.45	0.147	−0.8544	−1.01	0.311	−2.279	−11.65	0.000 a
	lnPGDP	0.8993	5.25	0.000 a	0.5321	3.40	0.000 a	0.3730	2.32	0.032 b
Model 2	lnREC	−0.1565	−11.84	0.000 a	−0.1775	−2.31	0.021 b	−0.1165	−3.66	0.002 a
	lnFD	−0.2424	−2.60	0.010 a	−0.0290	−0.14	0.885	−0.1206	−3.73	0.001 a
	lnFDI	−0.0144	−1.25	0.211	−0.0053	−0.69	0.490	−0.0025	−0.82	0.425
	lnRQ	−0.0575	−0.88	0.380	−0.1669	−0.84	0.402	0.0903	3.49	0.002 a
	lnEI	0.6697	15.35	0.000 a	0.9472	3.28	0.001 a	0.9947	7.95	0.000 a
	lnPGDP	0.4365	9.48	0.000 a	0.8904	3.03	0.002 a	0.8743	11.62	0.000 a
Model 3	lnFD	−2.268	−6.80	0.000 a	−0.6446	−0.63	0.530	−0.1978	−1.67	0.111
	lnFDI	0.1240	2.09	0.037 b	0.0779	0.41	0.685	−0.0300	−2.91	0.009 a
	lnRQ	−0.1471	−0.44	0.659	0.4169	0.40	0.686	−0.0365	−0.38	0.711
	lnFDxlnRQ	−2.501	−3.70	0.000 a	−1.033	−0.39	0.696	−0.2619	−0.75	0.461
	lnFDIxlnRQ	−1.043	−8.54	0.000 a	−0.8109	−1.62	0.105	0.0144	0.69	0.498
	lnEI	−0.3758	−2.45	0.015 b	−1.822	−2.84	0.005 a	−2.276	−11.09	0.000 a
	lnPGDP	0.9290	5.93	0.000 a	−0.1628	−2.23	0.037 b	−0.3893	−2.28	0.034 b
Model 4	lnREC	−0.1597	−11.17	0.000 a	−0.1742	−2.31	0.021 b	−0.1170	−3.56	0.002 a
	lnFD	−0.3717	−3.76	0.000 a	−0.4429	−1.79	0.074 c	−0.1120	−3.23	0.004 a
	lnFDI	0.0007	0.04	0.964	0.0153	0.38	0.704	0.0047	0.67	0.513
	lnRQ	0.1962	2.10	0.037 a	0.0869	0.27	0.787	0.0478	1.19	0.248
	lnFDxlnRQ	0.6198	3.21	0.001 a	0.8027	2.97	0.000 a	−0.1642	−2.10	0.049 b
	lnFDIxlnRQ	−0.0595	−1.60	0.111	−0.0225	−0.31	0.756	−0.0227	−1.30	0.208
	lnEI	0.6611	15.26	0.000 a	0.7138	3.27	0.001 a	0.9997	7.88	0.000 a
	lnPGDP	0.4524	9.86	0.000 a	0.4891	2.94	0.003 a	0.8750	11.38	0.000 a
Test						z			Pr > z
Arellano-Bond test for AR (1) in first differences						−1.44			0.151
Arellano-Bond test for AR (2) in first differences						1.10			0.270
Hansen test excluding group: χ2 (50)						17.41			1.000

Note: (^a,b,c), respectively, significance at the 1%, 5% and 10% levels.

. However, a series of preliminary tests must be conducted to validate the System-GMM estimates. In this context, the validity of the System-GMM estimates was tested using the Arellano–Bond AR (1) and AR (2) autocorrelation tests, as well as the Hansen over-identification test, and the results are presented at the bottom of Table 8. The findings indicate that the AR (2) test is insignificant (p > 0.05), and therefore, there is no second-order serial correlation problem. Additionally, the results of the Hansen test confirm that the instruments used are valid and exogenous. These findings indicate that the estimated model is statistically reliable and consistent.

In this study, the main empirical analysis was performed using the Panel Moment-Based Quantile Regression (MMQR) method. MMQR allows examining heterogeneity by estimating the effects of independent variables on the dependent variable at different quantiles of the distribution (0.10, 0.30, 0.50, 0.70, 0.90), rather than relying on traditional average effects. Thus, not only the average-level effects of the variables but also their low and high-level effects can be evaluated in detail. The results obtained show that the effects of the variables of financial development (lnFD), foreign direct investment (lnFDI), regulatory quality (lnRQ), energy intensity (lnEI), and per capita income (lnPGDP) differ significantly across quantiles, highlighting the non-homogeneous nature of these determinants and the need to consider this in policy design.

To test the robustness of the findings across all models, OLS, GMM, and FE models with Driscoll-Kraay standard errors were applied sequentially. While the OLS model assumes average effects, GMM (Generalised Method of Moments) provides more reliable estimates by controlling for potential issues such as endogeneity, simultaneity, and omitted-variable bias. The Driscoll-Kraay correction addresses issues such as cross-sectional dependence and serial correlation, which are commonly encountered in panel data, thereby improving the reliability of standard error estimates.

Comparative analyses reveal that variable significance levels and coefficient magnitudes can vary across models. In Model 1, the FDI and RQ variables yield similar results across all methods. For example, the effect of FDI on REC is significant and negative at the 1% level in OLS and at the 5% level in GMM and Driscoll-Kraay methods. These findings are consistent with all quantile levels in the MMQR method. The effect of financial development (FD) on REC was significant at the 1% level in OLS, whereas the GMM and Driscoll-Kraay results were statistically insignificant but negative. OLS results support all quantiles in MMQR, while other methods support the negative direction. The effect of energy intensity (EI) on REC is only significant and negative at the 1% level in the Driscoll-Kraay method. The OLS and GMM results, although insignificant, are negative and support MMQR’s findings. The positive effect of per capita GDP (PGDP) on REC is significant at the 1% level in OLS and GMM, and at the 5% level in the Driscoll-Kraay test. OLS and GMM validate the low and medium quantiles (0.10, 0.30, 0.50) in MMQR; Driscoll-Kraay, on the other hand, validates the high quantiles (0.70, 0.90). Overall, the MMQR model shows that, unlike mean-based approaches, policy effects exhibit heterogeneous structures across quantiles; the OLS, GMM, and Driscoll-Kraay results strengthen the methodological robustness of the findings. In this context, the validity of the findings has been increased in both theoretical and applied policy contexts.

In Model 2, the REC-CO₂ relationship yielded similar results across all methods. The impact of REC on reducing CO₂ emissions is significant and negative at the 1% level in OLS and Driscoll-Kraay, and at the 5% level in GMM. These findings are consistent with all quantile levels in the MMQR. The effect of FD on CO₂ is significant and negative at the 1% level in OLS and Driscoll-Kraay, but not significant in GMM. The OLS and Driscoll-Kraay results support all quantiles in the MMQR. The relationship between FDI and CO₂ is negative but insignificant across all methods, consistent with MMQR results. The effect of RQ on CO₂ was negative but insignificant in OLS and GMM, whereas it was significant at the 1% level and positive in the Driscoll-Kraay estimator. MMQR findings are significant and negative in the lower quantiles, insignificant in the middle quantile, and positive but insignificant in the higher quantiles. Overall, all methods are consistent with MMQR results. The effects of the EI and PGDP variables on CO₂ are significant and positive at the 1% level across all methods, confirming the MMQR results at all quantiles. In conclusion, while the MMQR model reveals the heterogeneous nature of policy effects across quantiles, the OLS, GMM, and Driscoll-Kraay robustness tests strengthen the methodological reliability of the findings.

The results of Model 3 show that the interaction between regulatory quality (RQ), financial development (FD), and renewable energy consumption (REC) significantly and negatively affects all quantiles. This suggests that this situation could weaken the guiding effect of RQ on financial development toward renewable energy, leading resources to shift toward shorter-term or fossil-fuel-based areas. The MMQR results were only confirmed by OLS; although the relationship was negative in the GMM and Driscoll-Kraay estimators, it was found to be statistically insignificant. Similarly, in the interaction between FDI and REC, RQ was found to play a significant and negative role at all quantile levels. MMQR findings were again only supported by OLS; GMM results were negative but insignificant, while Driscoll-Kraay results were insignificant and showed a positive effect.

The results of Model 4 show that regulatory quality (RQ) interacts with financial development (FD) and CO₂ emissions in a statistically insignificant and directionally variable manner (negative/positive) at low quantiles (0.10 and 0.30), and significantly and positively at medium and high quantiles (at the 1% level), according to the findings of the MMQR. The interaction results indicate that, in higher emission quantiles, regulatory quality does not fully offset the emission-related effects of financial development, and may condition the magnitude and direction of this relationship. Among the robustness tests, the results of OLS and GMM were particularly consistent with MMQR at high quantiles, whereas the Driscoll-Kraay estimator did not support these findings. Regarding the relationship between FDI and CO₂ emissions, the moderating effect of RQ is significant and negative at the 10% level across all quantiles, indicating that under high regulatory quality, FDI reduces emissions. While the OLS, GMM, and Driscoll-Kraay results in the robustness tests confirm that the moderator effect is negative, the effects are not statistically significant. Therefore, the robustness analysis partially supports the MMQR’s findings. Overall, regulatory quality emerges as a significant moderator, differentiating the environmental impacts of financial variables across quantiles.

There are some inconsistencies between MMQR and mean-based estimators like GMM. The insignificance of certain variables in GMM estimations may stem from the averaging nature of mean-based methods, which masks distributional heterogeneity. In contrast, MMQR captures heterogeneous slope effects across different conditional quantiles. Therefore, divergence does not necessarily imply model instability; rather, it reflects the presence of asymmetric, distribution-dependent relationships.

4.8. Marginal Effects Analysis (Heterogeneous Impact)

To interpret the moderation mechanism more intuitively, the conditional marginal effects of financial development at different levels of regulatory quality have been calculated. The findings in

Table 9. Conditional marginal effects of financial development at different levels of regulatory quality.

Level of ln_RQ	Marginal Effect of ln_FD	Std. Error	z-Stat	p-Value	95% Confidence Interval
25th percentile (0.213)	−0.141	0.063	−2.22	0.026	[−0.265, −0.017]
50th percentile (0.439)	−0.174	0.073	−2.38	0.017	[−0.318, −0.031]
75th percentile (0.567)	−0.193	0.082	−2.36	0.018	[−0.354, −0.033]

show that as regulatory quality increases, the marginal effect of financial development becomes larger in absolute value. Accordingly, the negative impact of financial development on CO₂ emissions is −0.141 at the 25th percentile of regulatory quality, rising to −0.193 at the 75th percentile. These results indicate that higher corporate quality significantly enhances the effectiveness of financial development in supporting environmental sustainability.

4.9. Hypothesis Results

The results of the hypothesis tests indicate that the effects of financial development and foreign direct investment on both renewable energy consumption and carbon emissions differ across models and variable contexts. The interaction terms (RQ moderation) mostly did not support the expected theoretical direction. This situation indicates that regulatory quality, rather than strengthening the impact of financial variables on environmental outcomes, can, in some cases, weaken or reverse it.

Examining the results in

Table 10. Summary of Hypothesis Test Results.

Hypothesis Code	Hypothesis	Model	Expected Effect	Observed Effect (β)	Statistical Significance	Result
H1	lnFD → lnREC	Model 1	+	$-$	*** p < 0.01	Rejected
H2	lnFDI → lnREC	Model 1	$-$	$-$	*** p < 0.01	Acceptance
H3	lnRQ → lnREC	Model 1	+	+	* p > 0.10	Partially Accepted.
H4	lnFD → lnCO2	Model 2	$-$	$-$	*** p < 0.01	Acceptance
H5	lnFDI → lnCO2	Model 2	$-$	$-$	* p > 0.10	Partially Accepted.
H6	lnRQ → lnCO2	Model 2	$-$	$-$	* p > 0.10	Partially Accepted.
H7	lnFDxlnRQ → lnREC	Model 3	+	$-$	*** p < 0.01	Rejected
H8	lnFDIxlnRQ → lnREC	Model 3	+	$-$	* p > 0.10	Rejected
H9	lnFDxlnRQ → lnCO2	Model 4	$-$	+	*** p < 0.01	Rejected
H10	lnFDIxlnRQ → lnCO2	Model 4	$-$	$-$	* p > 0.10	Acceptance

Note: “+” indicates a positive, while “−” indicates a negative

, it was found that, contrary to H1, financial development reduced renewable energy consumption across all quantilesThis unexpected result may be explained by the structural characteristics of developed economies, in which mature financial systems often allocate capital to established, lower-risk conventional energy sectors. This finding aligns with the “carbon lock-in” hypothesis, suggesting that financial deepening without a green orientation may reinforce existing fossil-fuel-based infrastructure.

The partial insignificance of FDI in the CO₂ model suggests that aggregate foreign capital inflows in developed economies may be sectorally diversified, limiting their net environmental impact. This nuanced outcome highlights the importance of distinguishing between green and brown FDI in future research.

5. Conclusions

Sustainable development and global climate change are interlinked with renewable energy consumption and carbon emissions. The use of renewable energy reduces environmental burdens; thus, financial development and FDI play important roles in financing this process. This study examines the effects of financial development and FDI on renewable energy consumption and carbon emissions across 22 developed countries (2002–2021), while accounting for the moderating role of regulatory quality.

Adopting the method-of-moments quantile (MMQR) estimation approach and OLS, GMM, and FE estimations to assess robustness, this paper contributes to the limited evidence on how regulatory quality shapes the relationship between financial drivers and environmental performance across different quantiles.

The MMQR results show that financial development and FDI decrease when renewable energy attracts developed countries, while per capita GDP increases significantly. Regulatory quality and energy intensity were not statistically significant in explaining renewable energy consumption. Consumption of renewable energy and financial development decrease carbon emissions, while energy intensity and per capita GDP increase them. There is a negative, statistically insignificant association between FDI and carbon emissions. The effect of regulatory quality on carbon emissions differs by quantile: it is significantly negative at low quantiles, weakens at medium to high quantiles, and shows a positive but statistically insignificant relationship at higher quantiles. Taken together, these findings imply that the impact of institutional mechanisms on environmental performance depends on context. This study contributes to the literature by demonstrating that the environmental implications of financial development cannot be evaluated independently of institutional context and distributional heterogeneity. By employing a quantile-based framework, the analysis reveals that regulatory quality operates as a conditional institutional mechanism rather than a uniformly corrective force. This nuanced evidence challenges linear interpretations of the finance–environment nexus and underscores the need for more targeted, sector-specific regulatory instruments in developed economies.

The results demonstrate that the interaction term between financial development and regulatory quality negatively affects renewable energy consumption, and that FDI and the interaction between regulatory quality & FDI have quantile-level-dependent negative effects. This implies that financial capital may become less attracted to green energy as regulatory quality rises, and other structural or investment barriers may hinder FDI in renewables. Financial development may flow less into renewable energy consumption, but also into environmentally friendly financial policies. The interaction results indicate that, in higher emission quantiles, regulatory quality does not fully offset the emission-related effects of financial development and may condition the magnitude and direction of this relationship.

Although the regulatory quality level in developed countries is generally high, contributing to a transparent and efficient investment environment, this does not guarantee that financial resources will be directed toward environmentally friendly investments on their own. Research findings indicate that financial depth and institutional structures are insufficient to translate financial development into environmental benefits. This situation underscores the necessity of green financing strategies, environmentally focused policy interventions, and guiding mechanisms, even in developed economies. In this context, based on the study’s findings, the following policy recommendations can be made.

Importantly, the interaction effects between financial development and regulatory quality are not uniform across the conditional distribution of emissions. While the coefficients become positive and statistically significant in higher quantiles, they are insignificant at lower quantiles. This suggests that regulatory quality does not systematically intensify the environmental impact of financial development, but rather shapes it in a non-linear and context-specific manner. Therefore, the findings should not be interpreted as evidence that better regulation increases emissions per se; instead, they indicate that institutional quality alone may be insufficient to counterbalance structural factors driving emissions in high-emission economies.

The study’s findings suggest that policymakers should direct financial development toward environmentally sustainable investments through instruments such as green bonds and environmental funds, while enhancing the environmental impact of foreign direct investment via sustainability-based incentives and approval processes. Strong regulatory policies, including carbon pricing and emissions trading, should complement institutional quality, and international collaboration should be strengthened to support the global green transition. Additionally, developing a National Banking Sustainability Index based on a “Green Finance Score” could help monitor and guide the financial system’s environmental performance.