The Effects of Foreign Direct Investment and Technological Innovation on Renewable Energy Consumption Under Varying Market Conditions in the EU

Abstract

The transition to renewable energy is a critical priority for the European Union. However, the roles of foreign direct investment and technological innovation in shaping renewable energy consumption remain unclear. This study examines their joint influence across 20 European Union countries from 2013 to 2023, employing Method of Moments Quantile Regression to capture varying effects under different market conditions. The findings reveal that technological innovation consistently enhances renewable energy consumption, strengthening its impact from 0.298 in the 10th to 0.488 in the 90th quantile, particularly in economies with a robust renewable energy infrastructure. However, FDI negatively affects renewable energy consumption across all quantiles, with coefficients ranging from −0.00000228 to −0.00000324, suggesting that foreign investments may not always align with clean energy goals. Additionally, inflation positively influences renewable energy consumption, implying that rising energy prices drive a shift toward renewables, while economic growth initially increases fossil fuel reliance before transitioning to cleaner sources. The study’s results emphasise the need for strong policy interventions to ensure that FDI aligns with renewable energy goals and that technological innovation continues to drive clean energy adoption.

1. Introduction

Energy use is fundamental to economic efficiency, financial stability, and sustainable development. Across modern economies, energy is indispensable in stimulating capital investment, labour productivity, and industrial expansion [1,2,3,4]. It is also a crucial input in producing goods and services, making it an essential pillar of economic growth [4,5]. However, the increasing demand for energy and concerns over environmental sustainability have brought energy security and the transition to greener alternatives to the forefront of global policy agendas. The European Union (EU), in particular, prioritises clean energy transitions, aligning its policies with Sustainable Development Goals (SDGs) 7, 9, and 13, emphasising sustainable energy access, technological innovation, and climate action [1]. Achieving these objectives requires a significant shift toward renewable energy sources that are reliable, sustainable, and capable of supporting long-term economic growth while mitigating environmental risks.

The EU has made significant progress in renewable energy adoption, with its share of total energy consumption rising from 15% in 2013 to over 22% in 2023 [6]. However, fossil fuels still account for about 71% of total energy use, highlighting persistent challenges in transitioning to a cleaner energy mix. Foreign direct investment (FDI) plays a crucial role in accelerating this transition by providing capital for renewable energy projects. Despite annual FDI inflows exceeding $80 billion in the EU energy sector [7], concerns remain about whether these investments sufficiently support renewable energy adoption.

In this context, FDI promotes renewable energy consumption by providing capital, expertise, and technology for expanding green energy infrastructure [8,9]. FDI inflows into the renewable energy sector contribute to deploying cleaner technologies, developing large-scale renewable projects, and integrating sustainable energy solutions into national grids. With its strong regulatory frameworks and commitment to climate neutrality, the EU has emerged as a leading destination for renewable energy investment. European countries actively attract FDI by offering policy incentives, such as subsidies, tax exemptions, and feed-in tariffs, which lower the risks associated with renewable energy projects [10]. These investments accelerate the transition to clean energy and foster economic growth by creating jobs, enhancing industrial competitiveness, and improving energy security. However, the effectiveness of FDI in driving renewable energy adoption is contingent on the region’s technological innovation level [11].

Foreign direct investment is essential in shaping a country’s energy landscape, particularly in economies where capital inflows drive industrialisation and infrastructure expansion [12]. As industries grow, the demand for energy surges, often leading to increased renewable and non-renewable energy consumption [13]. However, the impact of FDI on renewable energy adoption depends on the nature of the investments and the adsorptive capacity of the host country. The absorptive capacity hypothesis propounded by Cohen and Levinthal [14] suggests that FDI can facilitate technology transfer, knowledge diffusion and skill development, fostering a shift towards renewable energy sources. When foreign investors bring advanced clean technologies, they create positive spillover effects that enhance the integration of renewables into the energy mix [15,16].

Technology innovation is a critical moderating factor in the relationship between FDI and renewable energy consumption. Advanced technologies facilitate renewable energy’s cost-effective generation, storage, and distribution, making it more attractive to investors and consumers [17]. In the EU, ongoing research and development (R&D) efforts in smart grids, battery storage, and hydrogen energy have significantly improved the efficiency and scalability of renewable energy systems [18]. Additionally, Tuballa and Abundo [19] highlighted that the EU has been actively promoting smart technologies, setting a goal for member states to install smart meters in 80% of households by 2020. This initiative aimed to improve energy efficiency and make grid management more effective. The rapid advancement of digital technologies, including artificial intelligence (AI) and blockchain, has further enhanced energy management and market integration, enabling better forecasting, optimisation, and grid stability [20,21,22]. Moreover, the EU’s commitment to fostering innovation through programmes like Horizon Europe and the European Green Deal has positioned the region as a global leader in sustainable energy transitions.

The interplay between FDI and renewable energy consumption is, therefore, highly dependent on the technological landscape of the host economy. Countries with strong innovation ecosystems, supported by a robust R&D infrastructure, intellectual property protections, and industry–academic collaborations, are better positioned to attract green FDI and accelerate the adoption of renewable energy solutions. In contrast, regions with a limited technological capacity may struggle to maximise the benefits of foreign investment in the renewable sector. As the EU champions clean energy transitions, integrating cutting-edge technologies will ensure that FDI contributes meaningfully to sustainable development goals. By leveraging innovation as a moderating factor, the EU can enhance renewable energy efficiency, affordability, and reliability, fostering a resilient and climate-friendly economy while maintaining its global leadership in sustainability.

Unlike previous studies that assumed linear and uniform effects and independently explored the impact of FDI on renewable energy consumption [23,24,25,26] or the effects of technology innovation on renewable energy consumption [27,28,29,30] in isolation using traditional estimation approaches that assume a uniform impact across all observations, our study aims to comprehensively analyse the joint influence of foreign direct investment and technological innovation on renewable energy consumption across different market conditions using the method of moment quantile regression (MMQR) approach to uniquely capture their impact at varying levels of renewable energy consumption. Our study contributes to the body of knowledge by revealing that technological innovation consistently boosts the adoption of renewable energy, especially in markets that rely heavily on clean energy, while FDI negatively impacts renewable energy consumption across quantiles. Additionally, our study incorporates inflation and economic growth as control variables to explain how these macroeconomic factors influence the interactions between FDI, technological innovation, and renewable energy consumption. The remainder of this study is structured as follows: Section 2 is the literature review, Section 3 covers the methodology, Section 4 shows the data analysis and interpretation of the results, and Section 5 discusses the conclusion recommendations, policy implications, limitations, and suggestions for further studies.

2. Literature Review

Based on the objective of our study, which primarily focuses on understanding the role of FDI and technological innovation in shaping renewable energy consumption, we examine two key relationships in this section, the FDI–renewable energy consumption nexus and the technological innovation–renewable energy consumption nexus as seen in Section 2.2 and Section 2.3, respectively, to explore how these factors drive or hinder renewable energy transition.

2.1. Theoretical Perspective

The relationship between FDI, technological innovation, and renewable energy consumption is rooted in several economic and environmental theories. The pollution Hypothesis suggests that FDI can enhance or hinder renewable energy adoption, depending on the host country’s regulatory framework [31]. Similarly, the Absorptive Capacity Theory emphasises that economies with strong innovation capabilities benefit more from FDI-induced technology transfers [14]. The Environmental Kuznets Curve posits that as economies grow, initial reliance on fossil fuels gives way to cleaner energy adoption, reinforcing the role of innovation in sustainable energy transitions [32]. These theories highlight how FDI and technological innovation interact to shape renewable energy adoption, providing a foundation for examining empirical evidence in the following literature review.

2.2. Foreign Direct Investment and Renewable Energy Consumption Nexus

As renewable energy becomes increasingly essential, several researchers have examined how FDI influences consumption. However, the findings have been mixed, with some studies showing a positive impact while others suggest a negative and/or unclear relationship. For instance, Sbia et al. [33] examined the relationship between FDI, trade openness, economic growth, CO₂ emissions, and clean energy in the United Arab Emirates (UAE) from the first quarter of 1975 to the last quarter of 2011. Their study adopted the ARDL bounds testing approach and confirmed a long-run cointegration relationship among the variables. The findings revealed that FDI, CO₂ emissions, and trade openness reduced energy demand, while clean energy and economic growth positively influenced energy consumption. Marton and Hagert [23] observed that while FDI initially lowers renewable energy consumption in the short run, it ultimately contributes to an increase in the share of renewable energy consumption in the long run. Joshua et al. [34] noted that FDI has a long-term negative impact on environmental pollution, primarily through the facilitation of clean technology transfer and sustainable investments. This supports the argument that FDI can catalyse renewable energy expansion if directed towards environmentally friendly projects.

Doytch and Narayan [16] analysed the impact of sectoral FDI on renewable and non-renewable energy consumption. The study found that FDI discourages using unclean energy, while economic growth encourages non-renewable energy consumption. Paramati et al. [35] examined the impact of FDI and stock market growth on clean energy consumption across 20 emerging economies from 1991 to 2012. The study found that FDI inflows, economic output, and stock market development significantly enhance clean energy use. Additionally, FDI has a unidirectional short-run causal effect on clean energy consumption. Tiwari et al. [36] also examined how the development of equity markets influences renewable energy consumption, focussing on the role of FDI in Asian economies. Their study found that FDI encourages renewable energy consumption by promoting investment activities in lower Asian countries where capital is relatively scarce. Kilicarslan [37] explored the relationship between FDI and renewable energy production in BRICS countries and Turkey from 1996 to 2015. The study applied the Pedroni cointegration and panel ARDL approach and found a long-term relationship between FDI inflows and renewable energy production.

Deng et al. [38] explored how renewable energy consumption, green technology innovation, and FDI influence the intensity of carbon emissions across 42 developing and 33 developed countries from 2000 to 2019. Their findings reveal that renewable energy plays a more significant role in reducing the intensity of carbon emissions in developing nations, where emissions are higher. Green technology innovation has also proved to be effective in both developing and developed countries but has substantially impacted developing economies more. Interestingly, while FDI contributes to higher emissions in developing countries, it does not bring the expected environmental benefits in wealthier nations. However, in developing economies, FDI helps offset some of the negative environmental impacts through green innovation. Shinwari, Wang, Gozgor, and Mousavi [1] also examined the relationship between FDI and energy consumption, emphasising the role of green technologies across 29 countries involved in the Belt and Road Initiative from 2000 to 2021. The study’s findings revealed that FDI positively impacts energy consumption and that adopting green technologies also increases energy demand. Mielnik and Goldemberg [39] suggested that FDI significantly impacts energy consumption in developing countries. They further argued that FDI tends to encourage the adoption of more efficient technologies, which can reduce energy use. Their findings highlight how modern technologies brought in through FDI allow countries to leapfrog over older, more energy-intensive systems, ultimately contributing to lower energy intensity. This supports that FDI can drive a more sustainable energy future in developing economies.

Foreign direct investments can also drive policy reforms, incentivising governments to create regulatory frameworks that support renewable energy adoption [40]. Conversely, FDI may hinder renewable energy expansion if it primarily flows into fossil fuel-intensive industries. Foreign direct investments, prioritising non-renewable energy investments, can slow the transition to clean energy by reinforcing dependence on conventional energy sources and discouraging sustainable energy initiatives [41]. Moreover, inadequate infrastructure in developing countries may prevent the effective utilisation of renewable energy technologies, reducing the impact of FDI on clean energy development [42]. To fully harness the benefits of FDI for renewable energy growth, host economies must attract investments prioritising sustainable energy projects and green technologies. Policymakers should implement incentives, such as tax breaks and subsidies, to encourage foreign firms to invest in renewables. By strategically channelling FDI into clean energy sectors, developing economies can accelerate their renewable energy transition and reduce reliance on fossil fuels [43,44]. Based on this review, hypothesis one is proposed as follows:

H1. 

The impact of FDI on renewable energy consumption varies under different market conditions.

2.3. Technological Innovation and Renewable Energy Consumption Nexus

Although numerous studies have explored how renewable energy consumption affects various economic variables, only a few have considered the role of technological innovation in shaping these relationships. For instance, Qayyum et al. [45] examined the relationship between financial development, renewable energy consumption, CO₂ emissions, and technological innovation in India from 1980 to 2019. The study used the ARDL model to estimate the long-run dynamics and the Vector Error Correlation Model (VECM) for causality. The study revealed that financial development increases CO₂ emissions, while renewable energy consumption and technology innovation reduce short- and long-term emissions. Economic growth and urbanisation also negatively impact environmental quality by increasing CO₂ emissions. Irandoust [46] explored the relationship between renewable energy consumption, technological innovation, economic growth, and CO₂ emissions in four Nordic countries using a vector Autoregression (VAR) model. The study found that technological innovation drives renewable energy consumption across all four of the studied countries. Additionally, renewable energy reduces CO₂ emissions in Denmark and Finland, while a bidirectional link exists in Sweden and Norway. However, no evidence was found that renewable energy directly influences economic growth.

Raihan et al. [47] explored how economic growth, renewable energy use, and technological innovation influenced CO₂ emissions in Bangladesh from 1980 to 2019. Their study adopted ARDL and Dynamic Ordinary Least Square (DOLS) estimation techniques. Their research findings revealed that economic growth significantly increases CO₂ emissions, while renewable energy helps to reduce CO₂ emissions. Technological innovation also lowers emissions, though its impact is less pronounced. Their study highlights the need for policies encouraging renewable energy adoption and technological advancements to promote environmental sustainability. Ahmed and Ozturk [48] and Shahbaz et al. [49] noted that technological innovation in China strongly affects environmental sustainability. Ang [50] pointed out that CO₂ emissions in China are negatively related to research intensity, technology transfer, and the country’s ability to absorb foreign technology. Rahman et al. [51] found that when international businesses adopt clean technologies, they can help improve environmental quality by reducing carbon emissions in Pakistan. Likewise, Ahmed et al. [52] showed that technological innovation contributes to better environmental outcomes by lowering CO₂ emissions across 24 European nations. Appiah et al. [53] argue that capital formation is essential for energy transition and must be coupled with technological innovation to drive renewable energy adoption.

Wang et al. [54] examined the dynamics of carbon emissions in 11 countries from 1990 to 2017. Their study found that carbon emissions positively correlate with financial development and GDP. However, technological innovation and renewable energy consumption are negatively associated with carbon emissions. Their findings suggest promoting technological innovation and renewable energy to help meet COP21 goals. Han et al. [55] highlight the importance of policy measures to encourage technological innovation, which can boost the adoption of renewable energy and reduce environmental pollution. Their research shows that well-designed policy frameworks promoting clean energy technologies are crucial in meeting long-term sustainability goals. Based on this review, hypothesis two is proposed as follows:

H2. 

Technological innovation positively influences renewable energy consumption across EU countries.

Despite extensive research on FDI, technological innovation, and renewable energy consumption, several gaps persist. Prior studies often assume uniform effects, overlooking market-specific conditions. Few analyses use quantile regression to capture distributional impacts. Additionally, limited research explores how the absorptive capacity moderates FDI’s influence on renewable adoption. Therefore, this study addresses these gaps to enhance policy relevance and effectiveness.

3. Methodology

3.1. Data Selection and Variable Description

In achieving the objective of this research, our study used annual panel data from 2013 to 2023 across 20 EU countries. The selection of 20 EU countries was based on their regional representation, economic diversity, and policy frameworks supporting renewable energy. This ensures balanced insights into FDI, technological innovation, and energy transitions (see Appendix A,

Table A1. Regional classification of selected EU countries.

No.	Country	Region	Code	No.	Country	Region	Code
1	Ireland	Western Europe	IRL	11	Italy	Southern Europe	ITA
2	The Netherlands	Western Europe	NLD	12	Greece	Southern Europe	GRC
3	Belgium	Western Europe	BEL	13	Spain	Southern Europe	ESP
4	Luxembourg	Western Europe	LUX	14	Portugal	Southern Europe	PRT
5	France	Western Europe	FRA	15	Demark	Northern Europe	DNK
6	Germany	Western Europe	DEU	16	Estonia	Northern Europe	EST
7	Austria	Western Europe	AUT	17	Latvia	Northern Europe	LVA
8	Hungary	Eastern Europe	HUN	18	Lithuania	Northern Europe	LTU
9	Czech Republic	Eastern Europe	CZE	19	Finland	Northern Europe	FIN
10	Slovenia	Central Europe	SVN	20	Sweden	Northern Europe	SWE

). As seen in

Table 1. Variable description.

Variable	Identifier	Description	Measurement	Source
Foreign direct investment	FDI	Quarterly Inward Foreign Direct Investment Flows.	US dollars, exchange rate converted, US dollars, Millions	OECD Data Explorer
Real GDP	GROWTH	Quarterly real GDP growth in OECD countries.	Percentage (%)	OECD Data Explorer
Inflation (CPI)	DINFLA	Quarterly Consumer Price Index for energy-related categories (electricity, gas, fuels), measured as a composite index using Classification of Individual Consumption According to Purpose (COICOP) 1999 classification, and base year normalised.	Index	OECD Data Explorer
Technology innovation	LTI	Government domestic expenditure in R&D.	Percentage of GDP	OECD Data Explorer
Renewable energy consumption	REC	Quarterly energy consumed from renewable sources (e.g., solar, wind, hydro), measured in Gigawatt-hours (GWh) or as a percentage of total energy consumption.	% of total final energy consumption	WDI and OECD Data Explorer

Source: Authors’ Synthesis (2025).

, which presents the description of the variables adopted in this study, renewable energy consumption is the dependent variable because the study focuses on understanding its determinants. Foreign direct investment and technological innovation are the independent variables, with FDI facilitating financial resources for renewable energy projects, while technological innovation drives energy efficiency and growth. Real GDP and inflation serve as control variables to account for broader economic activity and price fluctuations, which could independently impact renewable energy consumption, as adopted in the studies of [56,57].

3.2. Model Specification

This paper assesses the effects of foreign direct investment and technical innovation on renewable energy consumption under varying market conditions, as defined by the distribution (or quantiles) of renewable energy consumption. Our study uses the method of moment quantile regression (MMQR), introduced by Machado and Silva [58], which applies to moment estimation with constraints from quantiles and conditional moments to estimate quantile regression coefficients in panel data models with fixed effects. Unlike the traditional quantile regression method pioneered by Koenker and Bassett [59], which estimates parameters based on minimising the loss function, the MMQR relies on a conditional location-scale model that allows the estimation of quantiles through moment conditions. With the conditional location-scale modelling process, the conditional distribution of a response variable $Y$ given covariates $X$ for a panel data with a fixed effect can be expressed as:

$$Y_{it}={\alpha }_i+X_{it}^{\prime }\beta +\left({\delta }_i+Z_{it}^{\prime }\gamma \right)U_{it}$$

(1)

where

• ${\alpha }_i$ and ${\delta }_i$ capture individual-specific fixed effects.
• $X_{it}$ represents strictly exogenous regressors.
• $U_{it}$ is an i.i.d. error term that is independent of $X_{it}$.
• $Z_{it}$ is a transformation of $X_{it}$ affecting the scale function.

The conditional quantile function is given by:

$$Q_Y\left(\tau \mid X_{it}\right)=\left({\alpha }_i+{\delta }_{i}q\left(\tau \right)\right)+X_{it}^{\prime }\beta +Z_{it}^{\prime }\gamma q\left(\tau \right)$$

(2)

where $q\left(\tau \right)$ represents the quantile function of the error term.

In the context of our paper, $Y$ represents renewable energy consumption and $X$ is the set of independent variables that include foreign direct investment (FDI), technical innovation (LTI), inflation (DINFLA) and real GDP growth (GROWTH).

The estimation of Equation (2) relies on a set of moment conditions for location and scale to identify the parameters, such as the following:

(a)

The first-order conditions ensuring independence between errors and regressors are given by:

$$\begin{array}{c}E\left[UX\right]=0\\ \end{array}$$

(b)

Moment conditions capturing the structure of the fixed effects model:

$$\begin{array}{rr}& \\ & E\left[\left(\right|U|-1)D_{\sigma \delta }\right]=0\end{array}$$

where $D_{\sigma \gamma }$ and $D_{\sigma \delta }$ denote derivatives of the scale function.

(c)

Moment conditions ensuring the quantile function holds, such as:

$$E\left[I\right(U<q\left(\tau \right))-\tau ]=0$$

where $I(\cdot )$ is the indicator function.

The MMQR estimator follows a sequential estimation process, where the estimate $\beta$ is obtained by regressing $Y_{it}-{\bar{Y}}_i$ on $X_{it}-{\bar{X}}_i$ using least squares:

$$\widehat{\beta }=\mathrm{a}\mathrm{r}\mathrm{g} \mathrm{m}\mathrm{i}\mathrm{n}\sum _{i,t} {\rho }_{\tau }\left(Y_{it}-{\alpha }_i-X_{it}^{\prime }\beta \right)$$

The individual effect ${\alpha }_i$ is obtained as

$${\widehat{\alpha }}_i={\displaystyle \frac{1}{T}}\sum _t \left(Y_{it}-X_{it}^{\prime }\widehat{\beta }\right)$$

With the residuals obtained as $R_{it}=Y_{it}-{\widehat{\alpha }}_i-X_{it}^{\prime }\widehat{\beta }$, the estimate $\gamma$ is obtained by regressing $\left|R_{it}\right|-\left|{\bar{R}}_i\right|$ on $Z_{it}-{\bar{Z}}_i$, such as

$$\widehat{\gamma }=\mathrm{a}\mathrm{r}\mathrm{g} \mathrm{m}\mathrm{i}\mathrm{n}\sum _{i,t} \left(\left|R_{it}\right|-{\delta }_i-Z_{it}^{\prime }\gamma \right)$$

The estimate ${\delta }_i$ is obtained as:

$${\widehat{\delta }}_i={\displaystyle \frac{1}{T}}\sum _t \left(\left|R_{it}\right|-Z_{it}^{\prime }\widehat{\gamma }\right)$$

(d)

Estimate quantile function $q(\tau )$ is the solution to:

$$\widehat{q}(\tau )=\mathrm{a}\mathrm{r}\mathrm{g} \underset{q}{\mathrm{m}\mathrm{i}\mathrm{n}} \sum _{i,t} {\rho }_{\tau }\left(R_{it}-\left({\delta }_i+Z_{it}^{\prime }\widehat{\gamma }\right)q\right)$$

where ${\rho }_{\tau }\left(A\right)=(\tau -1)AI(A\le 0)+\tau AI(A>0)$ is the quantile check function.

The advantage of the MMQR is that the estimator accounts for fixed effects that influence the entire distribution (not just location shifts). Moreover, the MMQR avoids the incidental parameter problem by structuring the moment conditions appropriately, which renders the estimator computationally efficient and applicable to large panel datasets.

This derivation provides a robust framework for estimating quantile regression models in panel settings while mitigating the limitations of traditional fixed effects estimators.

The moment conditions $E\left(RX\right)=0$ for the location moments, where $R=Y-\left(\alpha +X^{\prime }\beta \right)$.

For the scale parameters $\delta$ and $\gamma$, the scale moment condition is given by:

$$\begin{array}{rr}& \\ & E\left[\left(\left|R\right|-\sigma \left(\delta +Z^{\prime }\gamma \right)\right){\displaystyle \frac{\partial \sigma \left(\delta +Z^{\prime }\gamma \right)}{\partial \delta }}\right]=0\end{array}$$

To target the specific quantile $\tau$, a check function moment condition is included, such as:

$$E\left[I\left(R\le q\left(\tau \right)\sigma \left(\delta +Z^{\prime }\gamma \right)\right)-\tau \right]=0$$

These conditions allow the estimation of the parameters $\alpha ,\beta ,\delta ,\gamma$, and $q\left(\tau \right)$ using the method of moments.

4. Data Analysis and Result Interpretation

4.1. Descriptive Statistics

Table 2. Descriptive statistics and matrix correlation.

Descriptive Statistics						Correlation Matrix
Variable	Obs	Mean	Std. Dev.	Min	Max	(1)	(2)	(3)	(4)	(5)
REC	880	24.077	12.985	3.494	66.393	1.000
FDI	871	3540.016	21,373.893	−310,278.69	161,812.53	−0.095	1.000
DLINFLA	880	113.788	23.119	87.14	236.74	0.132	−0.101	1.000
LTI	800	1.944	0.828	0.435	3.49	0.296	0.043	0.103	1.000
GROWTH	836	0.468	2.4	−17.64	16.244	−0.005	0.021	−0.017	−0.029	1.000

Source: Authors’ Synthesis (2025).

presents the descriptive statistics for the various key variables in the study. Renewable energy consumption has a mean of 24.08%, with values ranging from 3.49% to 66.39%, indicating that it is substantial. Foreign direct investment exhibits high dispersion with a mean of USD 3540 million but a large standard deviation, reflecting significant fluctuations in FDI inflows. Technological innovation has a moderate mean of 1.94% of the GDP, while GROWTH varies widely, ranging from 17.64% to 16.2%, highlighting economic volatility. Also, in Table 2, none of the variables were strongly correlated, which does not threaten multicollinearity. Therefore, addressing multicollinearity among the independent variables is essential, as its presence can produce misleading or unreliable outcomes [60]. Therefore, to further check for multicollinearity in the models, the variance inflation factor (VIF) test was conducted, as shown in

Table 3. Variance inflation factor.

Variables	VIF	1/VIF
DLINFLA	1.023	0.978
LTI	1.015	0.986
FDI	1.014	0.986
GROWTH	1.001	0.999
Mean VIF	1.013

. Generally, a VIF value above 10 suggests a serious multicollinearity issue [61]. The result of the VIF test in Table 3 shows that none of the variables above exhibit any sign of multicollinearity, as the variables were within the acceptable threshold.

4.2. The Role of Technical Innovation and FDI in Promoting Renewable Energy Consumption

The MMQR results in

Table 4. Method of moment quantile regression (REC).

	Lower Quantile			Median Quantile			Upper Quantile
Variables	(0.1)	(0.2)	(0.3)	(0.4)	(0.5)	(0.6)	(0.7)	(0.8)	(0.9)
FDI	−0.00000228	−0.00000242	−0.00000252	−0.00000260	−0.00000268	−0.00000280	−0.00000292	−0.00000308	−0.00000324
	(0.00000161)	(0.00000129)	(0.00000111)	(0.00000098)	(0.00000091)	(0.00000090)	(0.00000100)	(0.00000128)	(0.00000163)
LTI	0.2984223 ***	0.3272094 ***	0.3456584 ***	0.3628167 ***	0.3789394 ***	0.4012572 ***	0.4256161 ***	0.4573061 ***	0.4889797 ***
	(0.1013474)	(0.0810388)	(0.06995)	(0.0619151)	(0.0572312)	(0.0564684)	(0.063316)	(0.0804823)	(0.102776)
DLINFLA	0.0118235 ***	0.0114459 ***	0.0112039 ***	0.0109788 ***	0.0107674 ***	0.0104746 ***	0.0101551 ***	0.0097394 ***	0.0093239 **
	(0.0026876)	(0.002149)	(0.0018552)	(0.0016416)	(0.0015158)	(0.0014942)	(0.0016749)	(0.0021332)	(0.002726)
GROWTH	−0.006773	−0.0064309	−0.0062116 **	−0.0060077 **	−0.0058161 **	−0.0055509 **	−0.0052614 *	−0.0048848	−0.0045083
	(0.0043485)	(0.003477)	(0.0030017)	(0.0026558)	(0.0024516)	(0.002416)	(0.002708)	(0.0034508)	(0.0044108)

Standard errors are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

provide valuable insight into the differential effects of FDI and technology innovation on renewable energy consumption (REC) across varying levels of REC distribution. This approach allows for a more comprehensive understanding of how these factors influence renewable energy adoption under different market conditions rather than assuming a uniform effect across all contexts [58]. The result in Table 4 indicates a consistently positive and statistically significant impact of technological innovation on REC across all quantiles. Furthermore, the magnitude of this effect increases progressively as we move from lower to upper quantiles, ranging from 0.298 in the 10th to 0.488 in the 90th quantile. This suggests that the impact of technological innovation on renewable energy adoption is more substantial in markets where the amount of REC is already high. These findings align with previous studies that opined that technological advancement is crucial in facilitating the transition to renewable energy, particularly in regions with a well-established clean energy infrastructure [62,63,64,65]. Also, investments in research and development (R&D), smart grid technology, and energy-efficient storage solutions create a reinforcing effect, enhancing the ability of high-REC economies to expand their clean energy adoption faster [17]. The EU’s success in renewable energy integration can be attributed to government-backed R&D initiatives and robust policy frameworks supporting technological innovation [18].

Contrary to expectations, Table 4 shows that FDI has a negative impact on REC across all quantiles, with its effect becoming more pronounced at higher levels of REC. The coefficient ranges from −0.00000228 in the 10th to −0.00000324 in the 90th quantile, suggesting that FDI does not contribute positively to renewable energy adoption, irrespective of the market conditions. This finding may indicate that FDI in host economies tends to favour non-renewable energy investments or traditional industries with high carbon footprints rather than clean energy projects [16,66]. Foreign investors may sometimes prioritise short-term profitability and infrastructure stability, leading to continued reliance on fossil fuels instead of renewable alternatives [35]. Additionally, the absorptive capacity of host countries plays a critical role in regions with weaker regulatory frameworks or underdeveloped technological capabilities that may struggle to direct FDI toward sustainable energy projects [14,15].

Inflation is positive and statistically significant on REC across all quantiles. This suggests that rising energy prices encourage higher renewable energy adoption. As traditional energy sources become more expensive, consumers and industries seek cost-effective renewable alternatives, reinforcing the importance of energy price policies in promoting green energy transitions [41]. On the other hand, GROWTH exhibits a weakly negative effect on REC, with statistical significance at the median quantile. This could indicate that economic expansion may initially drive higher energy consumption, including fossil fuel, before transitioning towards sustainable energy sources.

5. Conclusions and Recommendations

This study explored the impact of FDI and technological innovation on renewable energy consumption across 20 EU countries from 2013 to 2023 using the MMQR approach. The study’s findings reveal that technological innovation enhances REC across all quantiles, with its impact increasing from 0.298 in the 10th to 0.488 in the 90th quantile, particularly in economies with a strong renewable energy infrastructure. In contrast, FDI was found to have a negative effect on REC across all quantiles, suggesting that under current conditions, foreign investments may not be effectively driving renewable energy expansion. This could be due to FDI being directed towards the traditional energy sector or host countries lacking the regulatory frameworks to channel these investments into sustainable energy projects. Moreover, inflation positively influences REC across all quantiles, with coefficients ranging from 0.0118 to 0.0093 in the 10th to 90th quantile, respectively, indicating that higher energy prices push economies toward adopting renewable alternatives. However, economic growth exhibited a weak negative effect on REC with a statistically significant impact at the median quantile (−0.0062 in the 30th and −0.0055 in the 60th quantile), implying that as economies expand, energy consumption rises, often leading to a temporary increase in fossil fuel reliance before transitioning to cleaner sources.

Therefore, to enhance the role of FDI in renewable energy development, this study recommends that the government focus on restructuring investment policies to direct foreign capital toward sustainable projects. This can be achieved by offering targeted incentives, such as tax reductions and green finance initiatives, that make renewable energy investments more attractive than fossil fuel-based alternatives. Also, based on the findings of this study, which noted that investing in technology innovation is crucial for sustaining the growth of renewable energy consumption. Our study further recommends that governments and businesses should increase funding for R&D, focusing on smart grid systems, energy storage solutions, and AI-driven energy management tools. Energy pricing policies should also be aligned with sustainability goals. Carbon pricing and subsidy reforms can help shift consumption patterns toward renewables, making fossil fuels less economically attractive [67]. Furthermore, as economies grow, they should integrate long-term sustainability policies to ensure that rising energy demand is met with clean, renewable sources rather than increased dependence on non-renewable energy.

The interaction between foreign direct investment and technological innovation is crucial for accelerating renewable energy adoption. While FDI provides capital and infrastructure, technological innovation enhances efficiency and sustainability. For instance, Germany’s Energiewende policy leverages foreign investments in wind and solar energy alongside domestic R&D, fostering a robust clean energy transition [68,69].

5.1. Policy Implication

Based on the findings of this study, policymakers must first develop comprehensive strategies to attract green investments while fostering domestic innovation to accelerate the transition toward sustainable energy systems. Governments should create favourable investment climates by offering targeted incentives such as tax credits, subsidies, and streamlined regulatory frameworks to attract FDI in renewable energy projects. Policies that promote public–private partnerships (PPPs) can further enhance the deployment of renewable energy infrastructure by leveraging foreign expertise and capital. Additionally, policymakers should prioritise green finance mechanisms, including green bonds and carbon pricing, to align financial incentives with clean energy objectives.

Secondly, it is critical to enhance the absorptive capacity of host economies to maximise the benefits of FDI. Strengthening research and development (R&D) initiatives, investing in higher education, and promoting industry–academic collaborations will enable technology spillovers and ensure that foreign investments contribute to long-term renewable energy development. Governments should also enforce intellectual property rights to encourage multinational corporations to share cutting-edge clean energy technologies without concerns over misappropriation. Moreover, integrating digital and innovative technologies into the renewable energy sector can improve efficiency and grid stability. Policymakers should support the adoption of artificial intelligence, blockchain, and smart grid technologies to enhance energy distribution and forecasting, thereby making renewable energy more reliable and cost-effective. Finally, developing nations must ensure that FDI does not reinforce dependence on non-renewable energy sources. Clear sustainability criteria should be embedded in foreign investment policies to prioritise renewable energy projects.

5.2. Limitations and Suggestions for Further Studies

While this study provides valuable insights into how FDI and technological innovation affect renewable energy consumption, there are a few limitations that future research could look into. First, our study focussed on 20 countries in the EU, which has a well-developed infrastructure and strong policy support for clean energy. This means the findings of our study might not fully apply to developing economies, where energy access, governance, technology, and investment conditions differ significantly. Future studies could explore how these relationships play out in countries with varying economic development and technological readiness levels.

Second, while our study highlights the role of technological innovation, we did not break it down into specific types. Different innovations in renewable energy generation, storage, or grid management likely have unique effects. Therefore, future research could dive deeper into how various technological advancements contribute to the renewable energy transition. Examining individual renewable energy sources, like wind, solar, and hydrogen, could also offer a clearer picture of how FDI supports specific clean energy technologies. Lastly, it is essential to recognise that FDI does not have the same impact across all industries. While some investments support renewable energy, others may strengthen reliance on fossil fuels. A closer look at sector-specific FDI could help clarify whether green investments are significant enough to drive the transition away from traditional energy sources.