The Role of Human Development Index, Technological Innovations and Environmental Taxes in Sustained Economic Growth—Evidence from MMQR Method

Abstract

The pursuit of sustained economic growth remains a fundamental objective for all nations, as it directly contributes to improving living standards and the overall quality of life for citizens. This research examines how human development, technological innovation and environmental taxation influence long-term economic performance across twenty-two European Union (EU) countries over the 1990 to 2022 period. Method of Moments Quantile Regression (MMQR) is employed for data analysis and the robustness check is achieved by employing the Pooled Mean Group (PMG) and Panel Corrected Standard Errors (PCSE) methods. Key findings reveal the importance of human development, research and development and investment by sector in raising the Gross Domestic Product (GDP) per capita. Moreover, the MMQR findings shows that environmental taxes exhibit positive relationships with GDP per capita in the lower and middle quantiles, while insignificant relationships prevail in the upper quantiles. Therefore, environmental taxes are subject to some upper limits on their influence on GDP per capita. Once the threshold is achieved, environmental taxes tend to harm production. The PCSE findings show that the relationship of environmental taxes and GDP per capita is a weak positive one, while the PMG results shows that these factors are negatively related. Renewable energy is observed to be negatively related with GDP per capita as supported by the MMQR, PMG and PCSE results. These findings offer valuable policy implications, reinforcing the importance of aligning economic strategies with the Sustainable Development Goals (SDGs) to foster inclusive and environmentally sustainable growth within the European context.

1. Introduction

Sustained economic growth remains a central macroeconomic priority for nations striving to enhance living standards, reduce inequality, and secure long-term prosperity. In the contemporary era, however, achieving such growth involves addressing a complex set of interrelated challenges, including climate change, rapid technological advancement, and evolving demographic trends. There is increasing global recognition of the need to align economic development efforts with the principles of inclusivity and sustainability, as emphasized in the United Nations’ Sustainable Development Goals (SDGs), notably SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 13 [1].

In this context, the European Union (EU) stands out as a region that actively integrates environmental regulation, innovation-driven policies, and human development into its broader economic strategy. This commitment was further underscored at the COP28 summit, where EU member states renewed their pledges to increase investment in clean technologies and to strengthen human capital in pursuit of their 2050 net-zero emissions goals [2]. Nonetheless, there remains a lack of comprehensive empirical analysis on how multidimensional human development, technological innovation, and environmental taxation interact to influence income growth across EU economies.

Many prior studies have approached human capital in a fragmented manner, often emphasizing individual components such as education or health, rather than utilizing an integrated framework like the Human Development Index (HDI). Additionally, relatively few investigations have applied advanced panel data techniques such as quantile regression that allow for a nuanced understanding of how these relationships may vary across different levels of income [3,4]. So, the HDI is used because it operationalizes human development in a standardized, multidimensional, and comparable way across countries and time. “Human development” as a concept is a broad concept but HDI provides a measurable, composite indicator that captures the theory quantitatively.

In this study, the concept of human development is theoretically discussed, but for empirical analysis, a consistent and measurable variable is required. Therefore, the Human Development Index (HDI) is used as an empirical proxy for human development, providing a quantifiable measure that captures its multidimensional nature. Moreover, human development, as represented by the HDI, contributes to economic growth by enhancing labor productivity, encouraging innovation and technology adoption, and improving the social and institutional foundations necessary for sustained economic performance. In Europe, the HDI–growth link appears weak because HDI levels are already high and similar across countries, limiting variation and returns, while other factors like innovation and R&D play a bigger role in explaining growth differences.

Classical and modern growth theories such as the Cobb–Douglas Production Function (CDPF), the Solow Growth Model, and various endogenous growth frameworks have long recognized the significance of capital accumulation, labor, and technological progress. However, early models often treated technological advancement as exogenous [5], while later endogenous models, such as those proposed by Romer and Lucas, placed innovation and human capital at the center of long-run growth dynamics [6,7,8].

Against this theoretical and policy backdrop, the present study analyses how human development (measured by HDI), research and development (R&D) expenditure (as a proxy for innovation), and environmental taxes affect economic growth across twenty-two EU member states (Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, The Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, and Sweden) during the period 1990–2022. These countries were selected based on the availability of consistent and comprehensive panel data, as well as their shared institutional characteristics.

The study addresses key research gaps by employing HDI as a comprehensive, multidimensional measure of human development, jointly examining the effects of HDI, R&D, and environmental taxes, and applying advanced quantile regression methods to capture heterogeneity and highlight income-based differences among EU countries that previous research has often neglected.

The study makes several contributions to the literature. First, it adopts a multidimensional measure of human development rather than relying on single-variable proxies. Second, it employs the Method of Moments Quantile Regression (MMQR) to assess the effects across income distribution. Third, it applies robustness checks using Feasible Generalized Least Squares (FGLS) and Panel Corrected Standard Errors (PCSE) to validate the findings. Collectively, these elements aim to fill a notable empirical gap and offer evidence-based insights to inform inclusive and sustainable growth policies consistent with EU strategic goals and the SDGs.

Building on this framework, this study contributes to the existing literature by integrating a more holistic measure of human development with innovation and environmental policy variables, while explicitly accounting for heterogeneity through quantile-based estimations. Moreover, by employing an EU-specific panel dataset, the study provides a comprehensive and context-sensitive analysis. This integrated approach offers deeper insights into the complex interactions between human development, innovation, and environmental dynamics within the European context, thereby advancing the empirical understanding of sustainable economic growth.

The paper is structured as follows. The next section reviews the relevant theoretical and empirical literature and develops the study’s hypotheses. Section 3 describes the data, variables, and methodological approach employed in the analysis. The empirical results are presented in Section 4, while Section 5 discusses the findings and their policy implications. Finally, Section 6 concludes the paper, highlights its limitations, and suggests directions for future research.

2. Literature Review

2.1. Theoretical Framework and Growth Models

Dynamics of economic growth requires engagement with multiple theoretical traditions. Among these, the neoclassical growth models, endogenous growth theories, and the ecological economics perspective offer foundational insights.

The neoclassical framework, typified by the Cobb–Douglas Production Function (CDPF), conceptualizes output as a function of labor and capital. The Solow–Swan model [5] extends this by introducing technological progress as an exogenous variable that drives long-term productivity gains. However, it operates under the assumption of diminishing returns to capital and offers limited insight into the sources of technological advancement.

In contrast, endogenous growth theories pioneered by Lucas [6] and Romer [7] address this shortcoming by integrating innovation directly into the growth mechanism. These models emphasize the role of human capital, research and development (R&D), and knowledge spillovers in sustaining economic expansion. They argue that targeted investments in education, skills, and technological capacity produce long-lasting effects that overcome diminishing returns.

From an ecological perspective, Pigouvian theory [9] contributes by introducing the role of environmental taxation in economic analysis. By taxing negative externalities, such as pollution, this framework promotes internalization of social costs, efficient resource use, and the pursuit of sustainability. This approach is increasingly integrated into modern growth models, especially in light of global climate commitments.

These theoretical perspectives can be viewed as complementary rather than competing approaches to understanding economic growth. While the Cobb–Douglas Production Function establishes the fundamental relationship between production inputs and output, the Solow model emphasizes the contribution of technological progress to long-term growth. Endogenous growth theory builds on this foundation by highlighting the roles of human capital, innovation, and knowledge accumulation as drivers of sustained economic performance. In addition, Pigouvian taxation theory [9] provides insights into how environmental policies can support sustainable development by correcting market failures associated with environmental externalities. Together, these theories offer a comprehensive framework for analyzing how human development, technological innovation, renewable energy use, investment activity, and environmental taxation influence economic growth.

Together, these frameworks provide a comprehensive lens through which to analyze the joint effects of human development (HDI), technological innovation, and environmental taxes on economic performance. They establish the conceptual basis for evaluating the multifaceted drivers of growth in both traditional and sustainable development contexts.

The central role of these factors has been extensively discussed in economic literature. Contributions by Barro [10], Romer [7], and Lucas [6] highlight human capital, R&D, and innovation as essential to growth. This section synthesizes key models including the CDPF, Solow model, and endogenous growth theories to elucidate how these drivers operate over the long term.

The Cobb–Douglas Production Function remains a foundational tool for linking output to capital and labor inputs. It assumes constant returns to scale and diminishing returns to individual factors and is typically expressed as:

Y = A × Kα × L1 − α

(1)

where Y represents total output, K is capital, L is labor, A denotes total factor productivity (TFP), and α reflects the share of output attributed to capital. Here, TFP measures how efficiently capital and labor are utilized [11,12].

The Solow Growth Model expands on this by incorporating savings-driven capital accumulation, labor force growth, and exogenous technological change. In this model, long-term per capita output growth is ultimately driven by technological progress. Yet, since innovation is considered external, the model cannot fully account for the origins or variability of technological change [7].

Endogenous models overcome this limitation by treating innovation and human capital formation as internal outcomes of economic decisions. Romer’s model places R&D and knowledge generation at the center of growth, arguing that such activities expand the production frontier through innovation. Meanwhile, Lucas emphasizes the accumulation of human capital through learning by education and experience, which stimulates productivity.

These frameworks collectively underline the importance of investment in education, health, and innovation. Moreover, they emphasize the critical role of policy in shaping long-term growth paths [13].

While the CDPF and Solow models offer insights into input–output relations and exogenous change, endogenous models provide a deeper understanding of the self-reinforcing mechanisms underlying growth [10]. Ultimately, a robust strategy for sustainable economic development must integrate not only capital accumulation but also advances in human capital, innovation, and environmental responsibility [14].

2.1.1. Drivers of Sustainable Economic Growth in the EU

In recent years, researchers have increasingly highlighted the central role of human development measured by the Human Development Index (HDI) in fostering long-term economic growth across the EU. Çetin [15] showed that in both EU13 and EU15 country clusters, improvements in human capital and patent activity closely correspond with higher HDI scores, suggesting that innovation and skill development strengthen human development. Looking at the EU as a whole, HDI has steadily improved over the past two decades, demonstrating how investments in education, health, and income contribute to economic growth and social cohesion. These improvements align closely with Sustainable Development Goals SDG 4 (Quality Education) and SDG 3 (Good Health and Well-Being) and reflect the EU’s broader strategy of building human capabilities as a foundation for inclusive and sustainable economic growth (SDG 8). By strengthening education and health systems, societies not only enhance labor productivity but also build the capacity to adopt and generate innovations, creating a more resilient path for sustainable growth rather than growth that depends solely on resources or capital accumulation.

Innovation is another key driver of growth and sustainability in the EU. Evidence shows that countries with higher innovation output and efficiency enjoy better competitiveness while achieving environmental gains. Manavgat and Demirci [16] highlight that EU member states that actively participated in framework programs, from the Lisbon Strategy to Horizon 2020, were more effective at turning research funding into tangible outcomes, such as patents and technological solutions, which in turn supported stronger economic growth. The European Commission [17] confirms that EU-level investments in research and innovation have directly boosted GDP and created employment opportunities across member states, underscoring the importance of innovation as an economic driver. Eco-innovation, in particular, has been shown to stimulate growth while reducing environmental impact. Avunduk and İçen [18] demonstrate that countries with higher eco-innovation performance tend to enjoy higher GDP growth and green technologies contribute to both competitiveness and ecological sustainability. García García [19] further reveal that the relationship between GDP per capita and eco-innovation is non-linear, meaning that innovation’s impact on growth can vary depending on the development stage of a country. Taken together, these studies indicate that innovation (especially when combined with strong policies and a skilled workforce) drives structural transformation, improves productivity, and promotes sustainable economic growth, directly supporting SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production).

Directly supporting innovation is research and development (R&D), which forms the backbone of technological progress. R&D investment not only fuels technological advancement but also enhances productivity and economic competitiveness. Kokko et al. [20] find that while R&D spending is positively associated with growth in EU countries, its impact is somewhat smaller than in other advanced economies due to lower private-sector engagement and weaker collaboration between public and private sectors. Szarowská [21] adds that economically advanced EU countries are more efficient at transforming R&D inputs into patents, publications, and applied research outputs that directly affect economic growth. Complementing these findings, the European Commission [17] reports that EU-level R&D investments, particularly through programs like Horizon 2020, have significantly strengthened innovation, created jobs, and boosted GDP across member states. These studies together underscore that a strong R&D ecosystem (supported by both public and private investment) is crucial for building innovation capacity and sustaining long-term economic growth, reinforcing SDG 9 across the EU context.

Environmental taxes provide a complementary approach to achieving sustainable economic growth. Țibulcă [22] indicates that such taxes help improve fiscal sustainability while encouraging cleaner production and responsible consumption. The study done by Francisco et al. [23] points out that environmental taxes are underutilized, despite their potential to guide economic behavior toward greener practices. Evidence also shows that combining environmental taxes with green financial instruments, such as green bonds, improves energy efficiency and supports cleaner production in EU member states [24]. These policies contribute directly to SDG 7 (Affordable and Clean Energy), SDG 13 (Climate Action), and SDG 8 (Decent Work and Economic Growth).

Taken together, human development, innovation, R&D and environmental taxation create a reinforcing cycle for sustainable growth. A well-educated and healthy workforce can drive innovation and effectively utilize R&D outputs. Innovation enhances productivity, creates jobs, and supports environmental objectives, while environmental taxes provide the economic incentives necessary to promote green growth. This interconnected system allows the EU to pursue economic growth that is robust, socially inclusive, and environmentally responsible, fully reflecting the objectives of the EU’s Sustainable Development Goals. By emphasizing the synergy between human development, technological advancement, and environmental responsibility, the EU can ensure that economic progress benefits society while remaining compatible with long-term ecological sustainability.

2.1.2. Human Development Index and Sustainable Economic Growth

The Human Development Index (HDI), developed by the United Nations Development Program (UNDP), offers a multidimensional measure of well-being that includes health, education, and standard of living. In the context of growth theory, human development plays a key role by enhancing labor productivity and fostering innovation capacity.

Within neoclassical models like the CDPF, improved education and health outcomes enhance labor quality, thereby increasing output efficiency. Empirical evidence by Barro [10] supports a positive correlation between HDI and GDP growth, supporting the value of human capital investment. Similarly, in the Solow model, human development facilitates the absorption and application of technological progress, making innovation more effective when used in a skilled, healthy population [6].

Endogenous growth theories further boost the role of human capital, viewing it as an internal mechanism for sustaining long-term growth. Romer [7] highlights the catalytic role of education and R&D in expanding the technological border, while Lucas [6] stresses the productivity gains from accumulated learning and skill development.

Empirical literature consistently supports the claim that enhancing human development accelerates economic growth. The World Bank’s Human Capital Index quantifies the returns to education and health, showing that these investments significantly improve productivity, particularly in developing economies [25,26,27].

Foundational studies by Schultz [28] and Uzawa [29] provided early theoretical grounding for the link between human capital and growth. Lucas [6] introduced the concept of internal and external spillovers, while Mankiw et al. [12] incorporated human capital explicitly into growth models. Musila and Belassi [30] also found strong direct and indirect effects of education on GDP growth, validating the multidimensional role of human capital in development processes.

2.2. Empirical Literature

Recent empirical research has increasingly emphasized the dynamic relationships among human development, technological innovation, environmental policy, and economic growth. Numerous studies confirm the critical role of human capital in promoting economic performance, particularly in emerging and developing economies. For example, Alfada [31] and Banerjee and Roy [32] report a positive association between human capital accumulation and economic output. In a similar manner, Xu and Li [4] employed panel data methodologies and highlight how education and technological progress enhance labor productivity and overall national output.

In the realm of sustainability and innovation, a growing body of literature has explored the intersection between environmental and economic outcomes. Al-Mulali et al. [33] analyze how patents, renewable energy utilization, and energy taxation in the transport sector contribute to carbon emission reductions in EU member states. Pata et al. [34] examine the role of ICT and R&D in advancing renewable and nuclear energy in Germany, while Khan and Khurshid [35] evaluate the impact of enhanced nonrenewable energy efficiency and increased renewable energy intensity on environmental performance in both the USA and EU. Khaffaf et al. [36] investigate the contribution of high-tech industries and green electricity to the United States’ transition toward carbon neutrality by 2050.

While these studies shed light on the individual contributions of the Human Development Index (HDI), innovation, and environmental policy, they often treat these components in isolation. Few have adopted an integrated empirical framework that considers their combined and interactive effects. This gap underlines the rationale for the present study, which employs the Method of Moments Quantile Regression (MMQR) to account for heterogeneous effects of HDI, R&D investment, and environmental taxes across the income distribution of EU economies.

Several empirical contributions support the human capital–growth nexus. Roopchund [37] finds a direct and positive link between human capital and economic expansion. Pomi et al. [3] demonstrate that, excluding the influence of population aging, human capital continues to exert both short- and long-term positive effects on growth. Similarly, Deng and Dong [38] suggest a long-run equilibrium among human capital, physical capital, and economic output. Gong et al. [39] and Garzarelli and Limam [40] identify the complementary role of physical capital in supporting economic performance.

However, the effectiveness of human capital is highly dependent on its quality and sustainability. While several studies emphasize the positive roles of education, training, and health in driving growth [41,42], others, such as Shah et al.’s [43], find negligible long-term effects in certain contexts. Nevertheless, the broader empirical consensus supported by Xu and Li [4], Alfada [31] and Banerjee and Roy [32] upholds human capital as a key determinant of economic development.

2.2.1. Technological Innovations and Sustainable Economic Growth

Technological innovation is widely recognized as a central engine of long-run growth and structural change, particularly in lower- and middle-income countries. The seminal work of Solow [5] identified technological progress as an exogenous determinant of growth. This framework was extended by Romer [44] and subsequent Schumpeterian models, which endogenized innovation and emphasized its pivotal role in productivity gains.

According to the Schumpeterian paradigm, competition and continuous innovation are fundamental to economic dynamism. Empirical and theoretical studies [44,45] underscore the importance of R&D in driving innovation-led growth. Westmore [46] provides evidence that tax incentives and public support for R&D significantly boost private sector innovation across 19 OECD countries. Petrariu et al. [47] highlight how the developmental stage of an economy conditions R&D effectiveness in Central and Eastern Europe. Czarnitzki and Toivanen [48] find that prior experience and past productivity shape the responsiveness of firms to public R&D funding.

Within the Cobb–Douglas Production Function framework, technological progress plays a critical role in enhancing output. Empirical contributions from Jones and Williams [49] and Mankiw, Romer, and Weil [12] establish a robust positive relationship between R&D investment and economic performance. While the Solow model views technology as an external input, it nonetheless emphasizes its transformative impact on productivity and long-run output.

Endogenous growth theories offer a more comprehensive understanding of innovation dynamics. These models propose that sustained investment in R&D, supported by human capital and institutional capacity, generates self-perpetuating cycles of innovation and productivity [13]. Romer [8] emphasizes how technological advancement, fueled by R&D, creates cumulative growth effects.

Technological change also enables economies to overcome diminishing returns to capital and labor. Acemoglu [50] stated that innovation helps structural transformation and sustained growth. Grossman and Helpman [51] support the view that technological change acts both as a cause and consequence of economic expansion.

Collectively, neoclassical and endogenous models agree on the indispensable role of technological innovation in driving sustainable and inclusive economic development.

2.2.2. Environmental Taxes and Sustainable Economic Growth

Environmental taxation is a key policy tool for internalizing the external costs of environmental degradation. These taxes target emissions, pollutants, waste, and resource extraction to promote more sustainable production and consumption behaviors.

The literature presents a nuanced view of the growth implications of environmental taxes. Some scholars argue that such taxes may increase production costs and discourage investment, while others maintain that they incentivize green innovation, resource efficiency, and employment in sustainable sectors. Historically, France pioneered the implementation of resource taxes in 1969, followed by carbon and aviation fuel taxes in the United States and Japan. The European Union’s Environmental Tax Reform (ETR) in the 1990s aimed to shift tax burdens away from labor and capital toward environmentally harmful activities [52].

Empirical studies yield mixed results. Oueslati [53] finds that environmental tax reforms can be conducive to economic growth in emerging economies. Bovenberg and Mooij [54] show that shifting taxes from labor to environmental externalities may enhance growth, particularly when labor is mobile. Karydas and Zhang [55] argue that under conditions such as low elasticity of substitution, higher energy taxes may have a positive impact on output.

Conversely, general equilibrium models [24] suggest that environmental taxes may exert negative effects on growth when broader socio-economic and environmental feedbacks are considered. Nonetheless, Liu et al. [56] demonstrate substantial environmental benefits, and Piciu and Tric [57] propose that eco-tax revenues can be effectively redistributed to mitigate adverse effects. Tamura et al. [58] emphasize that eco-taxes are most effective when supported by affordable pollution control technologies. Studies conducted in China [59,60] confirm that environmental taxes can significantly reduce CO₂ and other emissions.

Environmental taxation also influences resource use and innovation. Söderholm [61] finds a positive correlation between environmental taxes and resource recovery. Amundsen and Schub [62] suggest that eco-taxes may benefit resource-scarce nations by encouraging conservation and efficiency.

Within an endogenous growth framework, Bovenberg and Mooij [54] argue that well-designed environmental taxes can simultaneously improve environmental outcomes and economic performance. Ekins et al. [63] support the long-term development potential of eco-tax revenues, while Bosquet [64] concludes that, under appropriate institutional conditions, environmental taxes substantially contribute to sustainable economic growth and emissions reductions.

Environmental taxes and carbon pricing in the EU effectively promote green innovation and reduce carbon emissions, supporting SDGs 9 and 13. By internalizing environmental costs, these policies shift incentives toward cleaner production, resource efficiency, and investment in low-carbon technologies. Empirical evidence shows that well-designed environmental taxes and carbon pricing stimulate eco-innovation, renewable energy adoption, and long-term economic and ecological benefits, while policy design and institutional quality are crucial to avoid negative transitional effects. Overall, these mechanisms enable structural transformation and help the EU achieve its 2050 carbon-neutrality goals in line with the Paris Agreement and SDGs.

Pata et al. [34] and Khan and Khurshid [35] find that green innovation and renewable energy intensity, supported by environmental policies and carbon pricing, significantly reduce CO₂ emissions across EU member states. Furthermore, Al-Mulali et al. [33] demonstrate that carbon taxes and patents in the transport and energy sectors contribute to both emission reductions and technological transformation. These results suggest that environmental taxation, when complemented by investment in clean R&D, produces long-run economic and ecological benefits. In sum, the European evidence confirms that carbon pricing and environmental taxation are effective instruments for achieving sustainable development targets of carbon mitigation. They stimulate green innovation, promote structural transformation, and enhance the EU’s capacity to meet its 2050 carbon neutrality objectives, consistent with the Paris Agreement and UN SDGs.

2.3. Hypothesis Development

The relationship between human development, innovation, environmental policy, and economic growth has been widely discussed in the economic growth literature. Drawing on insights from the Cobb–Douglas Production Function, the Solow Growth Model, endogenous growth theory, and Pigouvian environmental taxation theory, this study examines how these factors contribute to economic performance across EU countries.

2.3.1. Human Development and Economic Growth

Human development is increasingly recognized as a key driver of long-term economic prosperity. Improvements in education, health, and living standards enhance the quality of the workforce, increase productivity, and strengthen a country’s capacity to adopt and generate new technologies. Since the Human Development Index (HDI) captures these dimensions in a comprehensive manner, it provides a useful measure for assessing the broader contribution of human development to economic growth. Previous studies generally suggest that countries with higher levels of human development tend to achieve stronger and more sustainable economic performance.

H1: 

Human Development Index (HDI) has a positive effect on GDP per capita.

2.3.2. Research and Development and Economic Growth

Innovation has become one of the most important sources of competitiveness and long-run growth in modern economies. Investments in research and development (R&D) contribute to the creation of new knowledge, technological progress, and productivity improvements. Endogenous growth theory argues that these innovation-related activities generate lasting economic benefits by continuously expanding production possibilities and enhancing efficiency. Therefore, higher levels of R&D expenditure are expected to support economic growth.

H2: 

Research and Development (R&D) expenditure has a positive effect on GDP per capita.

2.3.3. Renewable Energy and Economic Growth

The transition toward renewable energy has gained increasing attention as countries seek to balance economic development with environmental sustainability. Renewable energy investments can stimulate economic activity through infrastructure development, technological advancement, and the creation of new employment opportunities. In addition, greater reliance on renewable energy can improve energy security and reduce dependence on imported fossil fuels. For these reasons, renewable energy consumption is generally expected to contribute positively to economic growth.

H3: 

Renewable energy consumption has a positive effect on GDP per capita.

2.3.4. Investment and Economic Growth

Investment is one of the most widely accepted determinants of economic growth. Both neoclassical and endogenous growth theories emphasize the importance of capital accumulation in expanding productive capacity and improving economic performance. Higher levels of investment enable firms to modernize production processes, adopt new technologies, and increase productivity. Consequently, greater investment activity is expected to support economic growth.

H4: 

Investment by sector has a positive effect on GDP per capita.

2.3.5. Environmental Taxation and Economic Growth

Environmental taxes are designed to encourage more efficient use of resources and reduce environmentally harmful activities. According to Pigouvian taxation theory, properly designed environmental taxes can improve economic efficiency by internalizing environmental costs that would otherwise be borne by society. Beyond their environmental benefits, such taxes may stimulate innovation, encourage cleaner production methods, and support the transition to a more sustainable economy. Therefore, environmental taxation is expected to contribute positively to economic performance.

H5: 

Environmental taxation has a positive effect on GDP per capita.

Based on these theoretical arguments and previous empirical findings, this study examines the extent to which human development, innovation, renewable energy, investment, and environmental taxation contribute to economic growth across European Union countries.

3. Methodology and Data

3.1. Research Model and Data

The present research follows the CDPF in developing the research model of the study [65]. The simple CDPF is expressed in the following form, Y = f(K, L), where Y represents the output variable and K and L represent capital level and labor input variables. This model shows that capital and labor are the core input factors of output level. The importance of capital as a key input factor of output has also been supported in the endogenous growth models (Y = AK) that have shown capital accumulation as the only factor responsible for sustained economic growth and increasing living standards [44]. In the AK model presented above, A is the constant factor because endogenous growth models assume constant returns to scale. Endogenous growth models (EGM) also emphasized that human capital is fundamental in explaining sustained economic growth; hence, marginal product of capital (MPK) does not decline in these models [66]. The Solow Growth Model also emphasizes the importance of technological progress in ensuring sustained economic growth in a country, though it is assumed to be exogenous [5]. The endogenous growth models correct the problem of the Solow model by showing that technology is endogenous rather than exogenous; hence, emphasizing the importance of technological advancement in fostering economic growth [44]. Therefore, the simple model presented in this research, following the theories on economic growth, is shown in Equation (1).

Y = β + γHDI + δRD + αC + μ

(2)

Equation (1) presented above takes the Human Development Index (HDI) to represent human capital, an important input variable in the EGM. R&D is also taken to represent technological innovations, an important factor in the endogenous growth models. The definitions of HDI and R&D are presented below. C is taken to represent all the control factors that are fundamental in affecting the output, as shown in Equation (2). β is the constant in the model while γ, δ, and α are the parameters to be estimated. μ is the white noise error term.

logGDPC_ti = β₀ + β₁ HDI_ti + β₂ RD_ti + β₃ RE_ti + β₄ IBS_ti + β₅ ET_ti + μ_ti

(3)

Equation (2) presents the research model employed. We take the log of GDP per capita to represent the output variable. Economic growth as measured by GDP per capita is the more efficient than when the absolute value of GDP is used. In fact, the Solow model, when explaining the factors that define sustained growth of countries, uses output per worker as the measure of economic growth [5,66]. HDI and R&D are the explanatory factors as explained above. This research employs renewable energy (RE), investment by sector (IBS) and environmental tax (ET) as the control factors.

The data employed in this research is of the twenty-two EU nations (Lithuania, Latvia, Slovenia, Estonia, Sweden, Spain, Slovakia, Portugal, Poland, The Netherlands, Luxembourg, Italy, Ireland, Hungary, Greece, German, France, Finland, Denmark, Czech Republic, Belgium, and Austria), from 1990 to 2022. The other five EU countries (Bulgaria, Croatia, Cyprus, Malta and Romania) are not included due to data for the Investment by Sector (IBS) variable being unavailable. To verify the reliability of the results, a sensitivity analysis was carried out using the full sample of EU member states while excluding the IBS variable from the model. The findings were largely consistent with those obtained from the baseline model, suggesting that the main conclusions remain robust despite the data limitations. GDP per capita, RE, and R&D factors are retrieved from the World Bank (WB) databases. Investment by sector and environmental tax are retrieved from the Organization for Economic Co-operation and Development (OECD) databases. HDI is retrieved from the United Nations Development Program (UNDP) databases. GDP per capita is the total output (goods and services) produced in a country divided by the total population. It shows the output per person value that is fundamental in showing economic growth and improved living standards. HDI is an index of three dimensions, decent standard of living, knowledge and long and healthier life. Knowledge is a form of human capital that is essential for fostering economic growth of a country as presented in the endogenous growth models. Moreover, improved health life and standards of living are fundamental in improving the capacity of human capital; hence, they promote economic growth as explained in the endogenous growth models [66]. Since HDI is an index with low values that are rarely above one, this research employs the Min-Max Scaling procedure to normalize the data of this variable. The normalization procedure is important in ensuring that the regression will not yield very high coefficients due to mismatches in the variable measurements. RE is a source of energy that is safe to the environment and is essential in raising the growth of economies. Most contemporary studies have included RE in explaining growth patterns of economies [67]. Environmental tax is the charge on polluting or deteriorating the environment by humans and helps to improve environmental quality, but may cause a reduction in the output produced. Investment by sector is fundamental in this research model considering the postulations of the endogenous growth models [44,66]. The factors employed in the research model are summarized in Table 1.

Table 1. Summary of variables.

Variable	Measurement	Source
GDP per capita (GDPC)	Total income per person in US dollars current value	WB
Human Development Index (HDI)	Index of decent standard of living, knowledge and long and healthy life	UNDP
Renewable energy (RE)	Renewable energy consumption measured as the percentage of total final energy consumption, % of total energy use	WB
R&D (RD)	Research and development expenditure, % of GDP	WB
Investment by Sector (IBS)	Gross fixed capital formation by institutional sectors expressed as a percentage of GDP. % of GDP	OECD
Environmental tax (ET)	Total environmental tax revenues including energy taxes, transport taxes, pollution taxes and resource taxes, expressed as a percentage of GDP. % of GDP	OECD

Table 2 also presents the characteristics of the factors employed. The characteristics of the factors are summarized with the descriptive statistics showing the standard deviation, the mean, the maximum and the minimum values of each factor.

Table 2. Descriptive statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
GDPC	726	28,663.74	21,817.83	110.0295	140,091.2
HDI	726	0.8552	0.0633	0.69	0.9819
RE	726	15.8192	12.0997	0.9395	55.4505
RD	726	1.5636	0.8917	0.0763	3.8738
IBS	726	24.3847	8.9478	2.8	65.3365
ET	726	2.6461	0.7440	0.33	5.4008

Table 2 highlights the considerable diversity among EU countries in terms of their economic and development characteristics. GDP per capita ranges from USD 110 to USD 140,091, illustrating the wide differences in income levels across member states. Renewable energy consumption also shows substantial variation, ranging from 0.94% to 55.45%, which reflects differing stages of progress in the transition toward sustainable energy systems. Similar disparities are evident in R&D expenditure and investment levels. These differences across countries provide a strong justification for employing the Method of Moments Quantile Regression (MMQR), as it is particularly well suited to capturing how the effects of explanatory variables may vary across different points of the income distribution rather than assuming a uniform impact for all countries.

3.2. Methodology

This study applies the ‘Method of Moments Quantile Regression’ (MMQR), as introduced by Machado and Silva [68], to explore how human development, technological innovation, and environmental taxation influence sustainable economic growth across countries with differing development profiles. The adoption of MMQR is driven by both econometric robustness and the need for policy-relevant insights.

Conventional regression approaches such as ‘Ordinary Least Squares’ (OLS) or panel fixed-effects models are restricted to estimating average effects, potentially masking variations along economic growth distribution. In contrast, MMQR captures conditional quantile-specific effects, enabling an in-depth analysis of how the independent variables operate at various points (e.g., lower vs. upper quantiles) of the growth spectrum. This feature is particularly relevant when assessing economies characterized by structural divergence or income heterogeneity.

Additionally, MMQR proves effective in addressing issues such as skewness, outliers, and heteroskedasticity, common challenges in macroeconomic panel datasets [69]. Unlike mean-based methods that rely on restrictive distributional assumptions, MMQR produces reliable estimates even when classical assumptions are violated.

A distinctive advantage of MMQR over traditional quantile regression lies in its use of a location-scale modeling structure. This setup accounts for covariate effects on both the central tendency and dispersion of the dependent variable, allowing for richer parameter heterogeneity. Furthermore, the moment-based estimation approach enhances efficiency and accommodates unobserved heterogeneity within the panel [68,70].

The model’s ability to incorporate individual-specific effects is also noteworthy. This is critical in cross-country settings, where latent country-level factors such as institutional quality or demographic trends may be confused by the observed relationships. By controlling for such unobservable factors, MMQR mitigates omitted variable bias.

Lastly, MMQR facilitates a nuanced understanding of whether the impact of HDI, R&D, and environmental taxation varies across economies with different growth performances. This distributional lens offers policymakers evidence on whether interventions should be uniform or tailored based on a country’s position in the growth distribution.

In summary, MMQR presents a methodologically rigorous and policy-relevant framework to assess the differentiated effects of key development drivers on sustainable growth across diverse national contexts.

MMQR estimations were conducted using STATA version 15. The implementation available in this software does not directly provide post-estimation Wald tests for coefficient equality across quantiles. Therefore, the analysis focuses on the quantile-specific coefficient estimates and their statistical significance, while robustness is assessed through complementary estimators, namely PMG and PCSE.

Statistical inference for the MMQR estimates is based on asymptotic standard errors. Bootstrap replications were not employed because the sample size is sufficiently large (22 countries over the period 1990–2022), making asymptotic inference reliable. In large panel datasets, asymptotic standard errors are generally considered adequate, whereas bootstrap procedures are more commonly applied in small-sample settings where asymptotic approximations may be less reliable.

The methodology followed in this research is presented in three main stages, that is, panel factors preliminary testing, model preliminary testing and model analysis. The three stages of the methodology are explained below.

3.2.1. First Stage: Panel Factors Preliminary Testing

In the first stage, the panel factors are investigated for ‘cross-sectional dependence’ (CD) by adopting a tool developed by Pesaran [71] and the findings are presented in Table A1 in Appendix A. Investigating CD in the panel factors is required to depict which method to use in checking unit root [72]. Significant CD findings on the variables shown in Table A1 entail the importance of using second-generation (SG) methods in analyzing unit root [73]. Therefore, the SG method—the ‘Cross-sectional Augment-ed Dickey–Fuller’ (CADF) method—is used for checking UR [74]. The CADF results are presented in Table A2 in Appendix A and they indicate that the variables exhibit mixed ‘integration orders’—I(0) and I(1)—and dynamic methods can be employed under this condition.

3.2.2. Second Stage: Model Preliminary Testing

In conducting the model pre-tests, four methods—“Variance Inflation Factor” (VIF), “slope heterogeneity”, cointegration and the weak CD method—are used and their findings are presented in Appendix A. These preliminary tests are fundamental in determining the suitable method to use for the analysis of the research model employed. The VIF tool is used to investigate ‘multi-collinearity’ among the factors specified as explanatory variables and the findings presented in Table A3 show that the model has no “multi-collinearity” issues in the explanatory variables specified. The Westerlund [75] tool is used to investigate cointegration since the variables have mixed “integration orders”; hence, it can show the likelihood of a long run (LR) association in the model [75]. The Westerlund [75] cointegration results presented in Table A4 depict the absence of cointegration; hence, the LR connection is absent. The “slope het-erogeneity” test is fundamental in checking if the model has significant “heterogeneity” issues [76]. The heterogeneity results presented in Table A5 reveal the existence of strong “heterogeneity” issues in the model; hence, it is essential to adopt the SG methods in the analysis of the model. The model is also tested for CD using Friedman [77], Frees [78,79] and Pesaran [80] “scaled Lagrange multiplier” (LM) tests. The findings are presented in Table A1, showing that CD is strong in the model; hence, there is a need to employ SG methods in analyzing the model.

3.2.3. Third Stage: Model Analysis

The research model is analyzed with the MMQR with fixed effects because of the availability of weak CD in the model, heterogeneity and the presence of a LR association [68]. The MMQR method provides the heterogeneous findings in different quantiles; hence, making it easy to investigate the asymmetric effects of the explanatory factor of the explained one. It is an essential contemporary method of analyzing panel data. To check the robustness of the MMQR method results, the “Panel Correlated Standard Errors” (PCSE) method [81] and the “Pooled Mean Group” (PMG) method [82], commonly known as panel Autoregressive Distributive Lag (ARDL), are used. The PMG method is employed to overcome potential endogeneity in the model, due to reverse causality of some explanatory variables. The PMG method is the most appropriate method considering the nature of the panel data employed, that is, long panel data with time that is greater than the cross-sections.

Although MMQR accounts for heterogeneity and distributional effects, potential endogeneity and reverse causality cannot be fully ruled out. Economic growth may simultaneously influence human development, innovation activities, renewable energy adoption, and environmental taxation. Therefore, the results should be interpreted as conditional associations rather than definitive causal effects. Future studies may employ dynamic panel estimators or instrumental variable approaches to address these concerns.

4. Results

The analysis of this research presents the MMQR results in Table 3 and the PMG and PCSE results in Table 4. The PMG method that is employed to overcome endogeneity problems in long panel data utilizes the PMG (1,0,0,1,0,1) model that is automatically selected with the Akaike info criterion (AIC). The ECT of the PMG is −0.1865 and significant at 1%, confirming the long-run cointegrating relationship in the model, as well as showing that the speed of adjustment back to equilibrium is 18.65%, which is relatively low.

Table 3. MMQR results.

	Coefficient	Std. Err.	p-Value	Coefficient	Std. Err.	p-Value
	Location			Scale
HDI	0.0346 ***	0.0013	0.0000	−0.0030 ***	0.0008	0.0000
R&D	0.1764 ***	0.0276	0.0000	0.0506 ***	0.0183	0.0060
RE	−0.0050 ***	0.0016	0.0020	−0.0056 ***	0.0011	0.0000
IBS	0.0230 ***	0.0021	0.0000	−0.0074 ***	0.0014	0.0000
ET	0.0470 **	0.0220	0.0320	−0.0486 ***	0.0145	0.0010
	0.1 Quantile			0.5 Quantile
HDI	0.0396 ***	0.0020	0.0000	0.0346 ***	0.0013	0.0000
R&D	0.0916 **	0.0437	0.0360	0.1752 ***	0.0276	0.0000
RE	0.0045 *	0.0025	0.0740	−0.0048 ***	0.0016	0.0020
IBS	0.0354 ***	0.0032	0.0000	0.0232 ***	0.0020	0.0000
ET	0.1284 ***	0.0347	0.0000	0.0481 **	0.0219	0.0280
	0.9 Quantile
HDI	0.0298 ***	0.0018	0.0000
R&D	0.2550 ***	0.0376	0.0000
RE	−0.0137 ***	0.0022	0.0000
IBS	0.0115 ***	0.0029	0.0000
ET	−0.0286	0.0300	0.3420

Note: *, **, *** means p-value < 0.1, 0.05 and 0.01, respectively.

Table 4. Results of PCSE and PMG methods.

	Coefficient	Std. Err.	p-Value	Coefficient	Std. Err.	p-Value
	PMG			PCSE
	Long-run (Pooled)
HDI	0.0128 ***	0.0038	0.0007	0.0346 ***	0.0015	0.0000
R&D	0.0358 *	0.0527	0.4973	0.1764 ***	0.0451	0.0000
RE	−0.0311 ***	0.0060	0.0000	−0.0050 **	0.0020	0.0110
IBS	0.0311 ***	0.0054	0.0000	0.0230 ***	0.0017	0.0000
ET	−0.0259 **	0.0443	0.5598	0.0470 *	0.0263	0.0740
	Short-run (Mean Group)
ECT(−1)	−0.1865 ***	0.0224	0.0000
D(RE)	0.0021	0.0052	0.6853
D(ET)	−0.0542 **	0.0266	0.0418

Note: *, **, *** means p-value < 0.1, 0.05 and 0.01, respectively.

The findings of this analysis show that HDI, R&D and IBS are the main drivers of sustained economic growth and consistent rising standards of living in the EU region as they are responsible for driving GDP per capita. However, RE and environmental taxes provide a mixed influence on GDP per capita, emphasizing the need for the policy frameworks linked with these factors in the EU region.

According to Table 3, the MMQR findings show that HDI presents symmetric positive influence in the GDP per capita as it influences GDP per capita to rise in all quantiles (β = between 0.0396 and 0.0298 in quantile 0.1 to 0.9 respectively; p-value < 0.01). This shows that HDI is essential in driving GDP per capita in both high- and low-income economies of the EU. The PMG and PCSE results are in support of the significant positive influence of HDI on the GDP per capita in the EU region (β = 0.0128 and 0.0346 respectively; p-value < 0.01). The findings are strong and significant at the 1% level, revealing that this relationship is strong. Therefore, the EU can capitalize on advancing human development to ensure the achievement of economic growth and advanced living standards. Moreover, the MMQR results show that R&D is an important driver of GDP per capita in all quantiles (β = 0.0916 to 0.255 in quantile 0.1 to 0.9 respectively; p-value < 0.05). Thus, R&D provides consistent symmetric effects on GDP per capita in all EU economies despite their level of income. The PCSE results show that relationship of R&D is strongly positive (β = 0.1764; p-value < 0.01), while the PMG results show that the influence of R&D is weak and positive (β = 0.0358; p-value < 0.1). Overall, the findings of this analysis reveal the importance of R&D in driving economic growth and high standards of living in the EU. R&D is by far the main driver of technological advancements, ensuring the development of sophisticated technologies to boost output production with the least costly method that is sustainable for the environment. Furthermore, the MMQR findings reveal the importance of IBS in driving GDP per capita in all the quantiles (β = between 0.0354 and 0.0115 in quantile 0.1 to 0.9 respectively; p-value < 0.01). IBS influences GDP per capita to increase in the EU regions with high or low income, though the coefficients show that the influence is more prominent in low-income EU economies than high-income ones. Similarly, the PMG and PCSE results concur on the importance of IBS in driving GDP per capita in the EU (β = 0.0311 and 0.023 respectively; p-value < 0.01). This reveals the importance of promoting investment by sector in the EU economies for sustained economic growth, resulting in the improvements in the standards of living for all.

On the influence of RE on GDP per capita, the MMQR results show the existence of asymmetric effects. In the lower-quantile, RE presents weak positive influence on GDP per capita ($\beta$ = 0.0045; p-value < 0.1) and a strong negative relationship in the 0.5 and 0.9 quantiles ($\beta$ = −0.0048 and −0.0137 respectively; p-value < 0.01). This shows that RE may likely advance GDP per capita to rise in low-income EU countries and influences GDP per capita to decline in the middle- and high-income EU economies. The PMG and PCSE results also support that RE is associated with significant decreases in GDP per capita ($\beta$ = −0.0311 and −0.0050 respectively; p-value < 0.01). The detrimental influence of RE presented in this analysis is explained by the need for vast investments for RE development—which likely leads to a short-term tradeoff with economic growth as part of the national income will be channeled towards facilitating RE projects. Similarly, green transition initiatives mean the abandonment of fossil fuel use—yet fossil fuel strongly drives economic growth. There is a need to align the RE framework of the EU economies with sustained economic growth as it is associated with detrimental influences. Additionally, environmental taxes are observed to present a mixed influence on GDP per capita, revealing the importance of aligning the fiscal and environmental policies of the EU with economic growth policies. The MMQR reveals the existence of asymmetric influence of environmental tax on GDP per capita, where significant positive effects are observed in the lower and middle quantiles ($\beta$ = 0.1284 and 0.0481 respectively; p-value < 0.05) and insignificant effects are observed in the upper quantile. The MMQR findings show that environmental taxes advance economic growth in the low- and medium-income economies of the EU, and have no influence in high-income economies. Therefore, the insignificant effect observed in the upper quantiles can be extensive use of fossil fuel in the high-income-level economies, such that the enacting of environmental taxes may start to impede production.

As it can be seen from Table 4, the PCSE results show that the influence of environmental taxes is weakly positive ($\beta$ = 0.047; p-value < 0.1), while the PMG results reveal that environmental taxes strongly reduce GDP per capita ($\beta$ = −0.0259; p-value < 0.05). Similar to the insignificant negative effects of environmental taxes in the upper quantiles, the negative influence depicted by the PMG explains how environmental taxes, when adopted, can hinder production in economies that heavily rely on fossil energy.

The findings presented in this analysis are based on the data of the selected 22 EU countries, according to data availability of IBS. Thus, four robustness check sensitive analyses of all 27 EU countries, while excluding IBS, are performed. The sensitive analysis results obtained through employing the MMQR method are presented in Table A6 in Appendix A. The sensitive analysis results confirm the positive influence of HDI, R&D and environmental taxes on GDP per capita, as well as the significant negative influence of RE. Therefore, the findings presented in this analysis are robust for policy implication.

5. Discussion

This study provides valuable evidence for the contribution of human development to economic performance within the European Union. The consistent and positive impact of the Human Development Index (HDI) on GDP per capita across all quantiles suggests that investments in human development yield economic benefits regardless of a country’s position in the income distribution. This symmetric relationship emphasizes the universal role of HDI as a driver of growth. Importantly, the findings imply that EU member states can enhance both short-term and long-term economic outcomes by prioritizing improvements in education, healthcare, and overall living conditions.

As mentioned earlier, the Human Development Index used here is calculated through incorporating three dimensions, that is, knowledge endowed by people, healthier and longer life and a decent standard of living. Knowledge is a fundamental factor in raising the economic growth of nations, in the endogenous growth models [7,44,66].

Knowledge can also be referred to as the human capital and various studies have been employed to examine how human capital affects economic growth. A positive effect of human capital on the economic growth of nations has been presented in the literature, showing that this dimension of human development is vital in raising the GDP of countries [83,84,85]. The endogenous growth models do not only show that knowledge or human capital increases the economic growth of economies, but that it is also important for the improved living standards of the citizens in a country.

The other dimension of the Human Development Index, longer and healthier life, is very crucial in ensuring that the GDP per capita is improved. In the endogenous growth model, healthy workers are also taken as part of human capital that is responsible for the sustained economic growth of nations [66]. Workers who are healthy are capable of channeling their energy towards the production of more goods and services, hence increasing the GDP per capita. Improved living standards of workers is also strongly correlated with the rise in the economic growth of a country [66]. Therefore, it is observed that the three dimensions of human development are responsible for fostering the GDP per capita in a country. This explains the present study results that depict that human development significantly raises the GDP per capita of the EU countries. While there are few studies that explain how human development affects economic growth, it is crystal clear from its dimensions that a positive effect is expected. The few studies available on human development seek to investigate the factors affecting human development [86,87] and how human development affect other factors, such as oil production. Thus, this research is vital in covering this gap and provides essential policies for advancing human development for the purpose of raising the economic growth of nations. The results show the strong and consistent positive impact of HDI on economic growth, highlighting the importance of human capital development, education, and health investments in promoting inclusive and sustainable economic progress within the SDG 8 (Decent Work and Economic Growth) framework.

The importance of R&D in supporting economic growth is supported. R&D is important in promoting the invention of new technologies for use in the production industry; hence, it can lead to high growth rates. For this reason, R&D should continue to be promoted in order to raise economic growth. Recent empirical research has highlighted that the effectiveness of R&D on economic growth depends critically on factors such as institutional quality, absorptive capacity, and the sectoral composition of innovation activities [88]. Furthermore, the positive linkage between innovation outputs, particularly in clean technologies like renewable energy patents and economic performance, reinforces the long-term advantages of pursuing innovation-led sustainable development [89]. The results show that the positive contribution of R&D and technological innovation is linked to SDG 9 (Industry, Innovation and Infrastructure), emphasizing the role of innovation systems and technological capacity in supporting long-term productivity growth and economic resilience.

Investment by sector is presented as the major factor responsible for raising the GDP per capita. This shows promoting investment helps in promoting the economic growth of nations. This research also shows the importance of environmental taxes in supporting GDP per capita in the lower and middle quantiles, though the upper quantile exhibits an insignificant effect and the PMG shows that the relationship is negative. These findings show that the importance of environmental taxes for economic growth is subject to some upper limits and beyond such a threshold, it can have detrimental effects on economic growth. Therefore, developing environmental policies that are sensitive to these upper limits is essential for sustainable development. Environmental taxes appear to promote economic growth primarily in lower- and middle-income EU economies. The absence of significant effects at higher quantiles may reflect diminishing marginal returns from environmental taxation in countries where environmental tax systems are already mature and extensively implemented. Evidence presented in the literature underscores the important role of green fiscal policies in promoting sustainable economic progress [67]. Acting as Pigouvian instruments, environmental taxes internalize negative externalities and encourage cleaner production practices, thereby mitigating climate risks while stimulating green investments and employment opportunities [90].

Nonetheless, RE is observed to significantly reduce GDP per capita in the EU countries included in the sample. This finding contrasts with much of the existing empirical literature that has highlighted that RE is essential in raising the GDP per capita [67]. Previous studies have documented that greater reliance on renewable energy contributes not only to environmental sustainability but also to favorable economic outcomes within the EU context [91].

However, the negative coefficients observed at the upper quantiles indicate that the relationship between renewable energy consumption and economic growth may not be linear across different income levels. One possible explanation is the existence of threshold effects and diminishing marginal returns. In lower-income economies, investments in renewable energy can generate substantial economic benefits by supporting infrastructure development, creating jobs, and improving energy access. In contrast, in more advanced economies, the additional gains from further expanding renewable energy capacity may be relatively limited. These countries often face higher costs related to grid modernization, energy storage, intermittency management, and other infrastructure upgrades needed to integrate larger shares of renewable energy into the energy system. As a result, the positive contribution of renewable energy to economic growth may gradually weaken and, in some cases, become negative once renewable energy penetration reaches sufficiently high levels. Overall, the findings suggest that environmental policies can support SDG 13 (Climate Action), although their economic effects vary across different stages of economic development and therefore require context-specific implementation.

The study demonstrates that sustainable innovation, captured through R&D investments and renewable energy initiatives, plays a crucial role in advancing the Sustainable Development Goals (SDGs) in the EU by improving productivity, human development, and environmental sustainability. In particular, the combined effects of HDI, technological innovation, and environmental taxes foster inclusive and green economic growth, supporting SDG 4 (Quality Education), SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation, and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). The findings highlight that integrating R&D, environmental policies, and human capital investment generates a virtuous cycle of knowledge-driven, socially inclusive, and environmentally responsible growth, thereby reinforcing the EU’s sustainable development agenda.

This study’s findings directly contribute to the understanding and advancement of specific Sustainable Development Goals (SDGs) within the EU context. The positive and significant impact of the HDI on GDP per capita aligns with SDG 8 (Decent Work and Economic Growth) by demonstrating that investments in education, health, and living standards enhance labor productivity and inclusive growth across income levels. Furthermore, the role of R&D and innovation in promoting technological advancement and productivity gains corresponds with SDG 9 (Industry, Innovation, and Infrastructure), as these elements support the transition toward a more competitive, knowledge-based, and innovation-driven industrial sector.

The findings on environmental taxation and renewable energy intensity have direct implications for SDG 13 (Climate Action). Specifically, the evidence that environmental taxes and clean energy adoption positively affect GDP per capita shows that climate policies can generate economic as well as environmental benefits. Environmental taxes act as incentives for firms to reduce emissions and invest in cleaner technologies, thereby decoupling economic growth from carbon intensity. Similarly, the expansion of renewable energy contributes to both emission reduction and energy security, reinforcing sustainable industrial development.

The findings have important implications for the Sustainable Development Goals (SDGs). The consistently positive effect of HDI underscores the importance of investing in human development as a pathway to achieving SDG 8 (Decent Work and Economic Growth). Likewise, the positive role of R&D highlights the contribution of innovation and technological advancement to SDG 9 (Industry, Innovation and Infrastructure). In addition, the results related to renewable energy consumption and environmental taxation offer valuable insights for SDG 13 (Climate Action), suggesting that environmental and energy policies should be tailored to the specific economic conditions and development stages of individual countries to maximize their effectiveness.

By empirically linking human development, innovation, and green fiscal policies to economic performance, this research provides actionable insights for policymakers seeking to design integrated strategies that balance growth with sustainability. Thus, this study not only references but effectively translates SDGs 8, 9, and 13 into empirical evidence within the EU context, illustrating how coordinated efforts in social investment, technological innovation, and environmental policy can collectively advance sustainable and inclusive economic transformation.

In summary, this study highlights the interconnected and reinforcing roles of human development, technological innovation, and environmental taxation in advancing sustainable economic growth within the European Union. Future research should further investigate the complex interactions among these factors across diverse economic environments, leveraging advanced quantile regression techniques to better capture heterogeneity and inform targeted policy design.

6. Conclusions

This study makes significant contributions to both theoretical and empirical literature on the determinants of economic growth, with a particular focus on European Union (EU) countries. Firstly, it advances the understanding of the pivotal role human development, encompassing education, health, and living standards, plays in raising GDP per capita. Unlike much prior research that primarily conceptualizes human capital in terms of knowledge or skills, this study adopts a broader lens by incorporating the comprehensive Human Development Index (HDI). This approach aligns with recent findings emphasizing multidimensional human development as a fundamental driver of sustainable economic progress [33,86]. The results underscore that human development contributes not only to productivity enhancement but also to improved social well-being and resilience, collectively underpinning long-term economic advancement.

Secondly, the findings confirm that Research and Development (R&D) is a significant driver of GDP per capita growth. By stimulating innovation, technological progress, and productivity improvements, R&D contributes directly to economic performance across EU countries. These findings reinforce the central role of innovation-led growth and support policies aimed at strengthening research capacity, technological development, and knowledge creation.

Thirdly, the study highlights the increasingly important roles of renewable energy adoption and environmental taxation in shaping economic growth while supporting environmental sustainability. The positive impact of environmental taxation is primarily observed in lower- and middle-income economies, whereas its influence becomes insignificant at higher income levels, indicating the existence of threshold effects and diminishing economic returns. These findings suggest that the economic effects of renewable energy are heterogeneous and may vary according to countries’ levels of development and costs associated with energy transition. While the emerging literature demonstrates that investments in clean energy infrastructure, combined with effective environmental tax regimes, can decouple economic expansion from carbon emissions and ecological degradation [35], the present findings indicate that economic effects of renewable energy and environmental taxation may differ across income levels and stages of energy transition. The EU’s green transition exemplifies the potential synergy that can be achieved when environmental policies are integrated with economic objectives, offering a relevant model amid escalating global climate challenges [32,67].

Fourthly, the results confirm that strategic sectoral investment remains a vital driver of GDP per capita growth. The efficient allocation of capital towards high-productivity sectors accelerates output and income generation, resonating with prior empirical evidence advocating for targeted investment policies to optimize economic performance [31,87]. This underscores the need for governments to prioritize investments in sectors characterized by robust growth potential, underpinned by innovation and sustainability frameworks.

These findings are relevant to the twenty-two EU countries included in the analysis and are representative of the period from 1990 to 2022. Given the exclusion of some EU member states due to data limitations, the results should be interpreted with caution when extending them to the entire EU or to more recent economic and policy contexts. Employing the MMQR methodology, which accounts for cross-sectional dependence and heterogeneity, enhances the robustness and generalizability of the results. Consequently, policymakers in other regions may draw valuable insights to inform integrated strategies promoting inclusive and sustainable growth.

The findings demonstrate that human development contributes directly to SDG 8 (Decent Work and Economic Growth), while R&D investments support SDG 9 (Industry, Innovation and Infrastructure). Furthermore, the heterogeneous effects of renewable energy and environmental taxation provide important insights for SDG 13 (Climate Action), suggesting that environmental policies should be tailored to countries’ development levels in order to maximize both economic and environmental benefits.

6.1. Policy Recommendations

Based on the findings, several actionable policy measures emerge to promote sustainable economic growth within the EU and comparable economies. Foremost, advancing human development should remain a national priority through targeted investments in education, healthcare, and social welfare. Enhancing educational quality and access will equip the workforce with advanced skills essential for innovation and productivity, while improved public health initiatives can boost workforce vitality by extending healthy life expectancy.

Given the consistently strong and significant impact of HDI across all quantiles, we emphasize investments in education, skills development, healthcare systems, and human capital formation as the primary policy priority for sustaining long-term economic growth.

Governments should also substantially increase R&D funding, emphasizing long-term projects with high innovation potential. Incentives such as tax credits, subsidies, and public–private partnerships can stimulate R&D activities, recognizing the delayed but significant returns to economic growth. Since R&D effects become stronger at higher income quantiles, the study recommends differentiated innovation policies that strengthen research capacity, university–industry collaboration, and technology commercialization, particularly in lower- and middle-income economies where the returns to R&D may require complementary institutional support.

Given that sectoral investment emerges as one of the strongest determinants of GDP per capita, governments should prioritize capital allocation toward productive, innovation-intensive, and sustainable sectors capable of generating long-term economic returns. Investment policies should encourage the development of high-value-added industries, strengthen productive capacity, and support sectors with strong growth and employment potential. Targeted investment strategies can enhance economic resilience while promoting sustainable development objectives.

Such strategies should be complemented by investments in infrastructure, workforce skills development, and improved access to finance to enhance economic resilience and promote sustainable economic development objectives.

Concerning environmental taxation, the results indicate growth-enhancing effects primarily in lower- and middle-income economies. Therefore, environmental tax policies should be carefully designed according to the development level of each country rather than applying a unique approach across all EU economies.

Although renewable energy remains essential for environmental sustainability, policymakers should carefully manage the economic costs associated with energy transition. Investments in grid modernization, storage technologies, and energy integration infrastructure should accompany renewable energy expansion to mitigate potential adverse effects on economic growth.

Finally, harmonizing policies across EU member states can reduce regional disparities and enhance economic cohesion and resilience across the bloc.

6.2. Future Research Directions

Future research should extend the scope beyond developed economies to include emerging and developing countries, providing comparative insights into the interplay among human development, R&D, and environmental taxation in diverse economic contexts. Longitudinal studies examining the long-term dynamics of these factors will deepen understanding of the delayed effects of innovation and environmental policies on growth trajectories.

Incorporating additional variables such as digital transformation, institutional quality, and climate risk factors could further refine growth models to better reflect contemporary challenges. Detailed sectoral analyses are warranted to identify industries that optimally balance economic performance with environmental sustainability.

Moreover, employing alternative advanced econometric techniques beyond MMQR may validate the robustness of results and uncover heterogeneous effects across countries and income groups.