Achieving sustainable development goals through renewable energy production: The role of mineral resources and green innovation among emerging economies

Abstract

There is an urgent global need to shift toward renewable energy sources to address climate change and foster sustainable development. Understanding the factors crucial for enhancing renewable energy production is essential to prevent potential disruptions and ensure the future availability of these energy sources. This study investigates the potential role of mineral resources, green innovations, and education on renewable energy production in emerging economies from 1990 to 2019. The recently developed advanced panel data econometric method, a bias-corrected method of moments regression, is utilized for empirical estimation. The findings indicate that a 1% increase in mineral resources correlates with a 0.109% rise in renewable energy production (p < .01). Additionally, a 1% increase in green innovation and education can significantly enhance renewable energy production by 0.451% (p < .01) and 0.753% (p < .09), respectively. The results provide practical guidance for policymakers to advance progress toward sustainable development goal (SDG)-7 (Affordable and Clean Energy) by focusing on the sustainable use of mineral resources and promoting green innovation and education. This strategy helps reduce environmental vulnerability and aligns with SDG-13 (Climate Action).

Graphical abstract

This is a visual representation of the abstract.

Keywords

Mineral resources green innovation education renewable energy production energy transition

Introduction

For 50 years, the global energy system has faced growing challenges and uncertainties driven by environmental sustainability issues. The increasing shortages and rising costs of energy underscore the enduring importance of “low carbon,” as well as “security” and “sustainability,” in addressing the energy crisis.¹ The International Energy Agency (IEA) highlights a persistent demand for energy amidst limited oil resources, a situation further intensified by extreme weather events and global warming. Consequently, nations worldwide are investing heavily in clean energy alternatives to meet their sustainable development goals (SDGs). Renewable energy production (REP) has become a key way to reach these goals. REP is also critical for sustainable growth and for meeting the Paris Agreement's target for large-scale deployment of low-carbon energy.² However, the continuous and sustainable supply of renewable energy depends on multiple interlinked factors, particularly the availability and efficiency of mineral resources. Essential minerals—such as copper, silicon, lithium, nickel, and aluminum—are required for manufacturing electric grids, solar panels, wind turbines, and energy-storage batteries.³ Although global mineral production exceeds three billion tons annually, the supply remains insufficient to meet the surging demand driven by REP expansion. The mineral intensity of renewable technologies is significantly higher than that of conventional energy, with the material requirement per unit of renewable energy increasing by 50% in the past two decades.^4,5

While prior studies have examined the deployment of renewable energy and the associated policy incentives, there is still limited understanding of how mineral resources, green innovation, and education interact to support the growth of REP.⁶ Most existing research addresses these factors individually, either focusing on technological innovation or resource management, without integrating them into a cohesive sustainability framework. However, the transition to renewable energy relies not only on technological advancements but also on the educational and institutional capacities that facilitate the effective use of critical minerals.⁷ This transition from non-renewable to renewable energy sources, supported by education and green innovation, is inherently complex. Despite significant progress in education and technological advancements over the past 30 years, their effects on improving resource efficiency in REP remain largely unexplored. Therefore, it is essential to examine factors such as education and green innovation to enhance REP through the efficient utilization of mineral resources, ultimately promoting ecological sustainability.⁸

Given their high relevance to REP, this study selected emerging countries (Argentina, Brazil, Bulgaria, Chile, China, Colombia, Hungary, India, Indonesia, Mexico, Peru, Poland, Romania, Russian Federation, South Africa, Turkey, and Ukraine) as the sampled economies to empirically examine the impact of mineral resources, green innovation, and education on REP. This choice is driven by the increasingly influential global standing of emerging economies, which hold significant mineral resources and account for more than 50% of the world's energy wealth. Furthermore, environmental pollution is escalating in these nations, with India and China ranking among the top polluters globally.⁹ This intensifies the demand for renewable energy in emerging economies. Most emerging countries have limited access to minerals due to a lack of advanced technology and education, which hampers the use of mineral resources in REP. Luthra et al.¹⁰ have pointed out that the existing environmental policies in emerging economies do not facilitate the efficient utilization of mineral resources within the energy sector. This scenario draws attention to the need to incorporate emerging economies into case studies. Therefore, this study aims to fill this gap by exploring how education and green innovation enhance REP through efficient utilization of mineral resources, contributing to both environmental and economic sustainability.

This study makes significant contributions to the literature in several ways. First, it offers an integrated empirical analysis of the effects of mineral resources, green innovation, and education on REP in emerging countries—an area that has been largely overlooked in recent research. By simultaneously examining these critical components, the study enhances our understanding of the factors driving REP in emerging economies. Second, the study utilized the latest bias-corrected methods of moments (BCMM) and moment quantile regression (MMQR), which effectively address potential biases arising from cross-sectional dependence (CSD), endogeneity, and unobserved heterogeneity. This approach guarantees consistent and robust estimates, fostering a deeper comprehension of the relationships under investigation. Finally, the study contributes to the global discussion on sustainability by explicitly linking its findings to SDG-7 (Affordable and Clean Energy) and SDG-13 (Climate Action). The empirical findings indicate that the sustainable use of mineral resources, combined with education and green innovation, can accelerate the adoption of renewable energy (SDG-7) and address the impacts of climate change (SDG-13).

The remainder of this paper is structured as follows: Section 2 provides a literature review and identifies the research gap. Section 3 outlines the detailed conceptual framework. Section 4 discusses the econometric strategy and data sources. Section 5 offers the Results and Discussion, while Section 6 concludes with the implications for policy based on the empirical findings.

Literature review

Related studies on mineral resources and REP

Studies focusing on the nexus between mineral resources and REP are not readily available in the current literature. However, several closely connected research studies provide empirical evidence supporting the relationship between mineral resources and REP, thereby reinforcing the nexus. For instance, Raihan et al.¹¹ examined China's environmental degradation, including aspects such as mineral resources and energy efficiency. These findings showed a negative correlation between environmental pollution and the extraction of mineral resources and energy efficiency. Similarly, Amin et al.¹² tested the influence of mineral resource rents and technological innovation on environmental pollution in China. Their results revealed that, in China, carbon emissions were exacerbated by technological innovation and mineral rents and alleviated by renewable energy and green growth. Chang et al.¹³ examined China's mineral trade network with a complex network methodology and revealed that mineral trade patterns influence renewable energy development. Further, Xu et al.¹⁴ analyzed the variations in China's environmental sustainability pattern by accounting for aspects such as mineral resource extraction and renewable energy. These empirical data indicated that mineral resource extraction, renewable energy, and carbon emissions were negatively correlated. Islam et al.¹⁵ analyzed the import-demand function for minerals associated with solar energy generation in China, revealing a significant long-term relationship between the demand for mineral imports and the country's installed solar capacity. Several studies utilizing a panel data set, such as the work of Liu and Lu,¹⁶ emphasized the influence of mineral resource rents and energy efficiency on renewable electricity production in Brazil, Russia, India, China, South Africa (BRICS) nations. Their quantile regression estimations indicated that mineral rents adversely impact renewable electricity production. Li et al.¹⁷ considered the impact of natural resources on the reduction of emissions from production in 21 Organisation for Economic Co-operation and Development (OECD) economies. They found that production-based emissions were predominantly influenced by natural resources. However, Ahmadov and Borg¹⁸ also noted that although natural resources promote REP, certain natural resources, such as petroleum, can have detrimental effects on the generation of renewable energy within a country. Li et al.³ investigated how natural resources affect renewable energy deployments in the Middle East and North Africa (MENA) region. Their findings indicate that natural resources play a significant role in the deployment of renewable energy. Viglioni et al.¹⁹ conducted an analysis of data from 77 developing nations to explore the impact of metallic minerals on renewable energy. Their findings reveal an inverse relationship between the availability of mineral resources and the utilization of renewable energy. Song et al.²⁰ indicate that mineral exploration has a lot of potential to help the green transition by providing important materials for renewable technologies.

Related studies on green innovation and REP

Countries worldwide are investigating approaches to address the energy gap. Notably, innovation can accelerate the renewable energy supply and optimize energy structures to meet the future need for renewable energy. The nexus of green innovation and REP has received reasonable attention, and several studies have documented its impact on REP. For instance, Awijen et al.²¹ examined the determinants influencing REP in the MENA region. This empirical study indicated that the performance of innovations is likely to affect REP under favorable governmental policies. Ruifang He et al.²² asserted that technological innovation is a crucial hub in REP to ensure that the increased demand for mineral resources is satisfied. Similarly, IEA 2013²³ reported that innovation is one of the influential factors in the deployment and production of renewable energy. Huang et al.²⁴ conducted a closely related study that thoroughly examined green marketing and innovation strategies. Their results indicated that sustainable packaging, product labeling, and collaborations are the most effective techniques for green innovation. Solangi et al.²⁵ sought to identify the drivers of green innovation that influence the adoption of renewable energy technologies in China. The results from this work revealed that governmental green innovation activities are the most critical methods for implementing renewable energy technologies. Zheng et al.²⁶ investigated the influence of technological innovation on renewable power generation in Chinese provinces. The findings from this study suggested that technological innovation promotes renewable power generation. Meanwhile, Vural²⁷ examined technological innovation and trade as predictors of REP in selected Latin American nations. This empirical research indicated that technological innovation and trade exert a positive and statistically significant influence on REP. Behera et al.²⁸ aim to assess the specific roles of green technology innovation in enhancing renewable energy utilization in 10 selected European Union (EU) countries. The findings of the study suggest that green innovation significantly impedes the use of renewable energy in these EU nations. Jiang et al.²⁹ investigates environmental advancements in China across 30 provinces. The findings indicate that integrating green technology innovations into policy frameworks will improve energy efficiency and foster environmental sustainability. Alfalih³⁰ examines how green technology innovation affects the supply of renewable energy in the world's leading green nations. The results suggest that green innovation has a positive impact on the development of renewable energy. Several other studies have highlighted the central role of critical minerals and technology in REP, ranging from identifying strategically important elements (i.e., Calvo and Valero,³¹ who found technology to be the most influential), as well as optimal extraction timing Fabre et al.³² and locations Sonter et al.³³ to examining trade dynamics Zhu et al.⁵ and seabed mining Toro et al.³⁴ as emerging opportunities.

While there is a growing body of literature examining the relationship between green innovation and REP, many existing studies provide fragmented insights. They often fail to deliver a comprehensive analysis that integrates mineral resources and green innovation within emerging economic contexts. Most previous research either emphasizes the technological efficiency of innovations or their environmental impacts, neglecting a thorough investigation of how green innovation progressively enhances the scaling of REP over time.

Studies related to education and REP

Education has emerged as a crucial factor in the development of renewable energy. The growth in renewable energy requires intensive and quality education to disseminate and prompt the acceptability of renewable energy sources in the near future.³⁵ Consequently, recent studies have increasingly investigated the role of education in promoting REP. While most findings have pointed to a positive relationship, particularly in emerging and developed economies (e.g., MENA, G11, and OECD), results vary by region and education level. Taghvaee et al.³⁶ investigated the impact of economic complexity, a measure of expert capability, on renewable energy adoption, and their findings indicated a positive correlation between economic complexity and renewable energy in MENA economies. Fang et al.³⁷ analyzed the impact of education on the demand for renewable energy in emerging economies. The empirical outcome of this work indicated that educational attainment encourages the adoption of renewable energy sources. Comparatively, Mehmood³⁸ examined the effect of education on environmental sustainability for G11 economies. The empirical findings from this study indicated that education improves environmental sustainability. In contrast, when Wang et al.³⁹ considered the effect of education on the use of renewable energy in Next Eleven (N-11) countries, their empirical findings indicated that education did not yield a beneficial impact on renewable energy adoption. Meanwhile, Zaman et al.⁴⁰ examined the relationship between educational expenditure and environmental pollution in China. Their estimates showed a negative correlation between environmental pollution and education expenditure. However, when Ozbay and Duyar⁴¹ investigated the influence of different levels of education on renewable energy for 20 OECD nations, their findings again suggested that higher education was the most significant factor influencing renewable energy adoption. Zheng et al.⁴² examines the connection between higher education and renewable energy generation in countries with high pollution levels. The findings suggest that institutional factors and higher education have a positive effect on REP.

The role of education in promoting environmental sustainability has been thoroughly explored in academic literature, with many studies focusing on its impact on reducing CO₂ emissions and increasing environmental awareness. However, there remains a significant gap in the literature concerning the direct relationship between education and REP, particularly in emerging economies.

The examined literature together suggests that mineral resources, green innovation, and education are crucial in influencing REP; nevertheless, the degree of their combined impact remains inadequately investigated in the context of emerging economies. Prior research has primarily examined these factors separately, overlooking the potential synergies that may exist among them. Based on the insights gathered, this study proposes that mineral resources positively impact REP (H1), that green innovation has a favorable effect on REP (H2), and that education enhances REP by fostering human capital (H3). Table 1 presents the comparative literature review.

Table 1.

Comparative literature review.

Author/year	Objective	Estimation method	Countries/ regions	Key variables
Raihan et al.¹¹	Analyze drivers of CO₂ emissions (1990–2021)	ARDL, DOLS, cointegration tests	China	Mineral extraction, GDP growth, renewable energy, energy efficiency
Islam et al. ¹⁵	Import-demand of minerals on solar energy generation	ARDL and dynamic ARDL simulation methods	China	Mineral import demand, solar energy, oil prices, exchange rates
Liu and Lu¹⁶	Nexus natural resource rents, GDP, and energy efficiency on renewable electricity output (1988–2021)	Quantile Regression	BRICS	Natural resource rents (coal, mineral, oil, forest), GDP, energy efficiency
Amin et al.¹²	Assess energy transition, green growth, and mineral rents on CO₂ (1990–2018)	Spectral causality	China	Non-renewable/renewable energy, green growth, mineral rents, technological innovation, GDP
Li et al.¹⁷	Natural resources and environmental regulations in reducing production-based emissions (1990–2020)	MMQR	OECD economies	Natural resources, environmental regulations, renewable electricity, economic growth
Chang et al.¹³	Mineral trade's impact on REP (2000–2019)	dynamic econometric modeling	China	Mineral trade patterns, RE technology, export strength, reprocessing rates
Xu et al.¹⁴	Assess COP26-aligned decarbonization drivers (1986–2021)	FMOLS/DOLS/CCR, quantile regression	China	Mineral extraction, GDP, energy efficiency, renewable energy
Awijen et al.²¹	Identify SDG-7-aligned RE drivers	Panel smooth transition model (PSTR)	MENA economies	Governance quality, innovation, political stability, financial development
Ruifang He et al.²²	Analyze threshold effects of industrial upgrading on tech-metal nexus in electricity (2000–16)	Dynamic panel threshold model	41 countries	Industrial upgradation, R&D, foreign tech spillovers, metal types
Huang et al.²⁴	Assess environmental labeling's impact on sustainable consumer behavior	Fuzzy AHP and Fuzzy WASPAS methods		Product attributes, sub-factors, strategies
Solangi et al.²⁵	Identify green innovation drivers for RET adoption	AHP method	China	Economic, Environmental, and social drivers
Zheng et al.²⁶	Quantify spatial spillovers of renewable tech innovation (2005–17)	Spatial Durbin model	China	Renewable energy patents, technology decay/diffusion rates
Vural²⁷	Determinants of REP	FMOLS method	Latin American countries	GDP, technology innovation, CO₂, REP
Calvo and Valero³¹	Assess material bottlenecks in global 2°C energy transition (2018–50)	Supply-demand modeling		Cobalt, lithium, tellurium, nickel; PV/wind/EV technologies
Fabre et al.³²	Model optimal RE-fossil transition under mineral scarcity	Intertemporal optimization		Extraction costs, RE productivity, substitution elasticity
Sonter et al.³³	Assess spatial overlap between RE mineral demand and conservation areas	Global spatial analysis		Protected areas, key biodiversity areas, wilderness, mine density
Taghvaee et al.³⁶	Economic complexity and structural transformation effect on energy security (1990–2017	Panel-corrected standard errors, DK, and spatial correlation consistent method, and GLS	MENA countries	Economic complexity, structural transformation, energy security
Fang et al.³⁷	Analyze urbanization/education impacts on energy demand (1990–2015)	FMOLS/DOLS	BICS countries	Urbanization, education, globalization, industrialization
Mehmood³⁸	Assess RE-education-FDI nexus on CO₂ (1990–2019)	FMOLS	G11 countries	Renewable energy, education, FDI, natural resources
Wang et al.³⁹	Influence of financial development and education on renewable energy (1990–2016)	Dynamic seemingly unrelated cointegrating regression	N-11 countries	Financial development, education, renewable energy

Research gap

The critical analysis of the existing empirical literature reveals several shortcomings in understanding the primary determinants of REP, particularly in emerging nations. Although prior studies have examined the influence of various economic and environmental factors, there is still a lack of comprehensive analysis regarding the combined effects of mineral resources, green innovation, and education on REP. Most current research predominantly focuses on the roles of education and green innovation in reducing CO₂ emissions, rather than exploring their direct relationship with REP. Additionally, empirical studies rarely integrate these three components into a cohesive analytical framework. This gap is especially pronounced in emerging nations, where advancements in education, green innovation, and mineral resources are crucial for promoting sustainable energy transitions. Thus far, the connections among mineral resources, green innovation, and education in influencing REP indicators have largely gone unexamined in empirical, data-driven research—highlighting the need for further investigation.

Conceptual framework

This study draws on the theoretical framework established by Hartwick⁴³ and posits that mineral resources significantly influence REP. On one hand, an abundance of mineral resources may lead to a resource curse, causing nations to become overly dependent on them and diverting investment away from clean energy transitions. Conversely, if mineral resources are utilized effectively, they can generate funding for renewable energy infrastructure and clean energy sources. This perspective is reinforced by endogenous growth theory, which suggests that income generated from mineral resources, when invested in human capital and innovation, can promote sustainable development. Additionally, the resource-based view (RBV) indicates that mineral wealth can serve as a strategic asset that, when managed effectively, provides the financial and technological resources needed for REP. Therefore, access to critical mineral resources can significantly impact both the cost and scale of REP.

Green innovation is recognized as a crucial element in promoting REP. For example, it helps eliminate barriers to grid integration, fosters advanced technologies, and enhances the technical skills necessary for the installation and maintenance of renewable energy power plants. Additionally, innovation contributes to the development of effective management practices for products and processes, leading to the efficient utilization of mineral resources and ensuring a balance between renewable supply and demand.⁷ The significance of green innovation in REP is further supported by endogenous growth theory, which suggests that innovation directly stimulates sustainable economic growth through knowledge spillovers that improve renewable energy technology. From the perspective of RBV, green innovation represents an intangible strategic asset that provides a competitive edge in the transition to renewable energy. By investing in environmentally sustainable research and development, companies and governments enhance their capacity to absorb renewable technologies, thus facilitating the acceptance and growth of REP. This strategy helps reduce environmental degradation while promoting energy security and long-term sustainability. Green innovation may also affect REP by maximizing the utility of mineral resources required for production and stimulating low-carbon transformation.²² Innovation encourages the adaptation of the latest machinery and advanced methods of production, leading to reduced mineral consumption and power losses in renewable energy plants and accelerating energy production.⁴⁴ Consequently, innovation improves the use of mineral resources and increases the renewable energy supply by expanding application areas and providing alternative production methodologies.⁴⁵

This study considers education an important factor in encouraging REP. Education is a crucial element and a fundamental pillar in national development; it fosters scientific ambition, enhances income, and cultivates a trained workforce to meet contemporary employment demands. It also assists nations in preparing for unforeseen issues and responding efficiently to dire circumstances. Education imparts essential knowledge and promotes social responsibility, enhancing public awareness of the severity of environmental issues, particularly those associated with REP. Zyadin et al.⁴⁶ asserted that education is essential for the successful implementation of renewable energy policy. It increases public awareness of the environment, increases the local involvement of citizens, and bridges the gap between human behavior and technological advancement through knowledge dissemination. Hess and Collins⁴⁷ contended that elevated educational attainment is a robust indicator of environmental consciousness and a proficient method for identifying renewable energy sources. According to the RBV, human capital gained through education serves as an intangible strategic asset that enhances a nation's or organization's ability to manage complex renewable energy technologies, foster innovation potential, and achieve a competitive advantage in sustainable energy markets. Education equips the workforce with essential knowledge and skills needed to create, adopt, and sustain advancements in renewable energy, ultimately contributing to a sustainable energy supply.⁴⁸ The significance of education in REP is supported by endogenous growth theory, which argues that investing in education enhances knowledge accumulation and technical progress, thereby facilitating the transition to clean energy sources. The OECD⁴⁹ argues that countries with low education initiatives and low willingness to engage in research-based innovation are falling far short of their clean energy targets. Consequently, education significantly influences the development of renewable energy, as evidenced by climate change. Consequently, education serves as both a means of disseminating information and a driving force for behavioral change in enhancing REP.

This study also includes labor productivity and urbanization in the proposed model because labor productivity in the energy sector may increase the efficiency of operations, encourage innovation, and ensure the effective utilization of resources, all of which positively influence REP. While urbanization increases energy demand, which stimulates renewable energy projects to fulfill energy demands with clean energy, it can also drive demand for energy infrastructure to promote projects, such as solar and wind. The visual representation of theoretical framework of the study is shown in Figure 1.

Figure 1.

Theoretical framework connecting resource-based view and endogenous growth theory in renewable energy production.

Drawing from the aforementioned arguments, the production function of renewable energy is as follows:

R E P_{t} = f (M R S_{t}, G D P_{t}, G R I_{t}, E D U_{t}, U R B_{t}, L O P_{t})

(1)

where REP_t, MRS_t, GDP_t, GRI_t, EDU_t, URB_t, and LPO are REP, mineral resources, economic growth, green innovation, education, urbanization, and labor productivity, respectively. All variables have been transformed into natural logarithms to prevent any disproportionate size effects in long-term relationships. The log function of the studied variables is as follows:

L n R E P_{t} = α_{\circ} + α_{1} L n M R S_{t} + L n α_{2} G D P_{t} + L n α_{3} G R I_{t} + L n α_{4} E D U_{t} + L n α_{5} U R B_{t} + L n α_{6} L O P_{t} + ε_{t}

(2)

where ln represents the natural logarithm and ε_t indicates the error term. It is assumed that if α₁ > 0, mineral resources have a positive impact on REP, and if not, it assumes that α₁ < 0. If α₂ > 0, economic growth increases REP; otherwise, α₂ < 0. Green innovation stimulates REP if α₃ > 0; otherwise, REP is hampered due to the higher rate of green innovation (α₃ < 0). The nexus between education and REP is positive if α₄ > 0; otherwise, education impedes REP α₄ < 0. Whereas, if α₅ > 0 and α₆ > 0, this indicates that urbanization and labor productivity, respectively, will improve energy production. The outcome variable was chosen because the production of one commodity inherently limits the production of another. In addition, the costs and benefits of implementing renewable energy power plants can vary significantly across different regions worldwide. REP serves as a strong indicator of a country's commitment to transitioning to clean energy. This study contends that a measure of production more accurately reflects a country's willingness to undertake the substantial investment necessary for a clean energy transition.¹⁸

Econometric strategy and data sources

The section outlines the methodology employed in this study. It emphasizes the econometric strategy, process, and underlying assumptions to improve the reproducibility and transparency of the methods used. The analysis assumes that the relationship between REP and its potential determinants remains stable throughout the examined period, with any cross-country variability addressed through country-specific factors.

Cross-sectional independence and slope homogeneity tests

In panel data analysis, local spillovers, global economic trends, and unknown shocks can raise concerns regarding CSD. Neglecting to address the CSD issue can result in biased coefficient estimates and forecasting errors, particularly in multi-country analyses that involve significant economic, environmental, and policy interconnections.⁵⁰ This study employed the CSD test developed by Pesaran et al.⁵⁰ to investigate how CSD is related to mineral resources, green innovation, education, GDP per capita, urbanization, labor productivity, and REP. The mathematical expression of the CSD tests is as follows:

C S D = \sqrt{\frac{2 T}{N (N - 1)} (\sum_{i = 1}^{N - 1} \sum_{k = i + 1}^{N} \hat{β_{i k}})} \sim N (0, 1) i, k

(3)

C S D = (1, 2 \dots \dots \dots 40 \dots \dots \dots N)

M = \sqrt{\frac{2 T}{N (N - 1)}} (\sum_{i = 1}^{N - 1} \sum_{k = 1 + 1}^{N} \hat{β_{i k}}) [\frac{(T - K) \hat{β_{i k}^{2}} - (T - K) \hat{β_{i k}^{2}}}{V a r (T - K) \hat{β_{i k}^{2}}}]

(4)

Here, N and T represent the cross-section (panel size) and time period (sample size), respectively.

Moreover, this study applied the Pesaran and Yamagata⁵¹ test to determine slope homogeneity. The null hypothesis of this test assumes that the slopes throughout the cross-section are not heterogeneous. The Pesaran and Yamagata slope test has the advantage of estimating both slope coefficient homogeneity and adjusted slope coefficient homogeneity. The mathematical formulations for both slopes are shown in Eq. (5) and Eq. (6).

Δ = \sqrt{\frac{N}{2 k}} (\frac{\overset{\hat{'}}{S} - K}{N})

(5)

Δ_{a d j} = \sqrt{N} . \sqrt{\frac{T + 1}{2 K (T - K - 1)}} (N^{- 1} \overset{\hat{'}}{S} - 2 K)

(6)

where Δ and Δ_adj denote the slope coefficient heterogeneity and adjusted slope coefficient heterogeneity, respectively.

Panel unit root tests

This study utilized the Cross-sectionally Augmented Dickey–Fuller (CADF) and Cross-sectionally Augmented IPS (CIPS) unit root tests to obtain robust estimates.⁵² The application processes for both tests were similar, and they did not necessitate (N/∞). The mathematical formulation for the CADF test is as shown below.

Δ Y_{i t} = β_{i} + δ_{i} y_{i, t - 1} + λ_{i} {\bar{y}}_{t - 1} + Ψ_{i} Δ {\bar{y}}_{t} + μ_{i t}

(7)

After adding the lag term (t-1) in Eq. (7), Eq. (8) is obtained as:

Δ Y_{i t} = β_{i} + δ_{i} y_{i, t - 1} + λ_{i} {\bar{y}}_{t - 1} + \sum_{j = 1}^{p} Ψ_{i j} Δ {\bar{y}}_{t - j} + \sum_{j = 1}^{p} θ_{i j} Δ y_{i, t - j} + μ_{i t}

(8)

where

{\bar{y}}_{t - 1}

and

Δ y_{i, t - j}

indicate the lagged averages and first difference operator of each cross-section, respectively.

The expression for the CIPS unit root test is shown in Eq. (9)

C I P S = N^{- 1} \sum_{i = 1}^{N} δ_{i} (N, T)

(9)

Here, the

δ_{i} (N, T)

represents the CADF, which can also be expressed as:

C I P S = N^{- 1} \sum_{i = 1}^{N} C A D F_{i}

(10)

Panel cointegration test

Prior to performing a long-run analysis, it is important to apply a cointegration test to evaluate the viability of a long-run relationship among variables. This study utilized the Durbin-Hausman panel cointegration test⁵³ to address the challenges posed by the series’ differentiation process. It provided robust estimates by overcoming the problems of heterogeneity and CSD. Additionally, it did not heavily rely on historical data regarding the integration orders, which allowed for variations in the solidity ranks of the regressors. A further advantage of the Durbin-Hausman cointegration test is its simultaneous execution of both the panel (DH_P) and group (DH_G) tests. Both DH_P and DH_G can be expressed as shown in the following:

D H_{P} = {\hat{S}}_{n} (\tilde{ϑ} - \hat{ϑ})^{2} \sum_{i = 1}^{N} \sum_{t = 2}^{T} {\hat{e}}_{i, t - 1}

(11)

D H_{G} = \sum_{i = 1}^{N} {\hat{S}}_{i} ({\tilde{ϑ}}_{i} - {\tilde{ϑ}}_{i})^{2} \sum_{t = 2}^{T} {\hat{e}}_{i, t - 1}

(12)

Panel long-run estimation method

This study employed a recent “bias-corrected method of moments,” as suggested by Breitung et al.,⁵⁴ to tackle challenges such as endogeneity, small-sample bias, and unobserved differences. BCMM is particularly suitable for dynamic panel data models, as traditional methods like pooled ordinary least squares or fixed effects often produce inaccurate and unreliable results when incorporating past dependent variables and related factors. The BCMM is specifically designed to mitigate known biases by directly adjusting moment conditions at their source while maintaining effectiveness. Additionally, it retains the small variance associated with fixed and random effects and is capable of accommodating higher-order autoregressive models. The BCMM estimation model demonstrates characteristics of an asymptotic distribution, facilitating the computation and adjustment of standard errors. It improves the ability to address cross-sectional correlation and slope heterogeneity, resulting in consistent and efficient estimates in such scenarios. Furthermore, a preliminary consistent estimator is not a prerequisite for BCMM, unlike the bias approximation of Bruno.⁵⁵ The fixed-effect version of BCMM complements that of Dhaene and Jochmans,⁵⁶ which can adjust the profile likelihood estimator. By adopting these methods, we ensure that our results remain reliable despite the potential fluctuations among countries. The mathematical composition of the BCMM estimator is as follows:

y_{i t} = \sum_{j = 1}^{p} λ_{j} y_{i, t - j} + x_{i t}^{'} β + \underset{= e_{i t}}{\underset{⏟}{α_{i} + μ_{i t}}}, i = 1, 2, \dots ., N, t = 1, 2, \dots ., T

(13)

With moment function

m_{i} (θ) = \frac{1}{T} \sum_{t = 1}^{T} [(\begin{matrix} y_{i, t - 1} - \bar{y} - 1, i \\ x_{i t} - \bar{x} i \end{matrix}) - (\frac{T}{T - 1} b (λ_{1}) (e_{i t} - {\bar{e}}_{i})] e_{i t}

(14)

where,

\bar{x} i = \frac{1}{T} \sum_{t = 1}^{T} x_{i t} a n d b (λ_{1}) = - \frac{1}{T^{2}} \sum_{t = 0}^{T - 2} \sum_{s = 0}^{t} λ_{1}^{s}, s u c h t h a t E [m_{i} (θ)] = 0

(15)

Robustness check: Alternative estimation methods

This study also used the Driscoll and Kraay⁵⁷ (DK) standard error, generalized least squares (GLS), and Method of Moments Quantile Regression (MMQR) regression to evaluate the stability of the empirical results from the BCMM estimator. These models are similar to those used in recent studies by Baloch et al.,⁵⁸ Danish,⁵⁹ and Anwar et al.⁶⁰ The DK⁵⁷ standard error method is equally effective and efficient for balanced and unbalanced data. It is a robust estimation method that can eliminate the problems associated with CSD, autocorrelation, and heteroscedasticity. In a case-regression model with a certain degree of correlation between regressors and error terms, the GLS model is the most suitable method. Ultimately, the study employed the MMQR model introduced by Machado and Silva.⁶¹ The econometric strategy used in this study is shown in Figure 2.

Figure 2.

Framework for panel data econometric strategy. Source: Authors’ calculations.

Data sources and description of variables

The sample consists of 17 emerging economies chosen for their increasing importance in global renewable energy investment and their initiatives to tackle climate impacts. These countries exhibit strong economic growth, improved industrial capabilities, and rising energy demand, making them crucial for the achievement of the SDGs.⁹ The provision of consistent and comparable annual data from these countries is essential for maintaining methodological rigor and ensuring comparability. It is worth articulating that the adopted sample was contingent on data available at the time of the study. Although every effort was made to utilize the most comprehensive data available within the study period, data remained limited for some emerging economies. Therefore, interpolation and extrapolation approaches were utilized to provide estimates for missing data. STATA 16 was used to conduct this analysis and derive econometric estimations. This study measured mineral resources as mineral rents (% of GDP) by estimating the difference between the production value of a mineral's stock at world prices and its total production. GDP per capita was considered a significant element in advancing the production of renewable energy and served as an indicator of economic performance. GDP was calculated per capita in 2015 US dollars at constant prices. The gross enrollment in secondary education is used as a gauge of education, taking into account the need for higher education to address environmental issues. The data extracted for mineral resources, GDP per capita, and education from the World Bank's World Development Index database (World Development Indicators, 2018; World Bank Open Data. Available at https://data.worldbank.org/indicator). Patents on environmental technologies estimate green innovation and offer several attractive properties compared to other alternative metrics of innovation. The model incorporates green innovation because of its critical role in improving economic performance and renewable energy development. Green innovation refers to the application of innovation and technological advancement in the energy sector to ensure the optimized use of mineral resources and increase energy efficiency. The OECD database provided the data related to green innovation (OECD (2022), Patents on Environmental Technologies (Indicator). https://data.oecd.org/). Finally, data for REP were collected from the IEA database and derived the total energy production from renewable sources, including wind, solar, geothermal, hydro, tide, biomass, and biofuels. Table 2 summarizes the data sources and describes the variables in this work. Figures 3 and 4 illustrate the trends in the production of mineral resources and renewable energy in emerging economies, respectively.

Figure 3.

Trends in mineral resources across emerging economies. X-axis represents years, and the Y-axis shows the share of mineral resources as a percentage of GDP.

Figure 4.

Trends across renewable energy production among emerging economies. X-axis represents years, and the Y-axis shows the share of total renewable generation.

Table 2.

Description of variables and their associated data sources.

Variable	Symbol	Measurement	Data source
Renewable energy production	REP	Sum of energy production from renewable sources, for example, wind, geothermal, hydroelectric, solar, tides, biomass, and biofuels	IEA
Gross domestic product	GDP	Per capita GDP (Constant 2015 US $)	WDI
Mineral resources	MRS	Mineral rents (% of GDP)	WDI
Green innovation	GRI	Patents on environmental technologies	OECD database
Education	EDU	Secondary education enrollment gross	WDI
Urbanization	URB	Urban population (% of total population)	WDI
Labor productivity	LOP	Labor force participation rate, total (% of total population ages 15–64) (modeled ILO estimate)	WDI

Note: IEA, International Energy Agency; WDI, World Development Index.

The variable measurement table provides detailed information about the study structures; however, many data points require interpolation because of missing values. The employed interpolation approaches align with previous research.^62,63 Possible limitations of the data set involve inadequate reporting for certain emerging economies and the frequent presence of minor measurement errors in secondary data sources. The use of MMQR, DK and GLS estimators as robustness tests is intended to verify the reliability of the estimates. These measures enhance the clarity of the empirical analysis and facilitate its repeatability.

Results and discussion

This section presents the results, with the descriptive statistics of the variables used in this study shown in Table 3. The estimates reveal a diverse distribution of the variables used in the data set. Notably, the coefficient of variation of lnMRS appears to be negative due to a negative mean, indicating high variability. The standard deviation (0.886) further supports this, reflecting a wide spread of values relative to the mean for lnMRS. The boxplot shown in Figure 5 illustrates the distribution of the study's variables. Each box represents the median, interquartile range, and potential outliers for the respective variables, making it possible to understand their central tendencies and variability. The boxplot analysis revealed some moderate outliers, which were retained since they represent valid extreme cases rather than data errors.

Figure 5.

Distribution of the key variables of the study. The boxes show the interquartile range with the median marked, whiskers indicate data variability, and dots represent outliers. Source: Authors’ calculations.

Table 3.

Descriptive statistical results.

Variable	lnMRS	lnGDP	lnGRI	lnEDU	lnURB	lnLOP	lnREP
Observations	510	510	510	510	510	510	510
Mean	−.0552	3.720	0.996	1.111	4.150	4.191	1.577
Std. dev.	0.885	0.310	0.213	0.071	0.296	0.104	0.595
Minimum	−4.559	2.722	0.161	0.880	0.151	3.240	3.881
Maximum	1.224	4.177	1.664	1.247	4.521	4.6052	3.352
CV (%)	−160	8.34	21.38	6.46	7.14	2.49	37.72

Note: CV, coefficient of variation; Std. dev., standard deviation.

The results are described in the following sections.

Cross-sectional dependence and slope homogeneity results

Cross-country heterogeneity, CSD, and transnational pollution can all have an impact on emerging economies.⁶⁴ Therefore, the distinct characteristics of nations necessitate the application of CSD tests. Table 4 displays the estimates derived from the Pesaran CSD, which reveals that all the studied variables (lnMRS, lnGDP, lnGRI, lnEDU, lnURB, lnLOP, and lnREP) are significant at the 1% level, disproving the null hypothesis of no CSD. In other words, CSD exists among countries, and shocks that occur in one country may influence others in the data set. In addition, Table 4 summarizes the findings of the slope homogeneity test. The Δ and Δadj (the slope coefficient heterogeneity and adjusted slope coefficient heterogeneity, respectively) are significant at the 1% level of significance, rejecting the null hypothesis of homogeneity. In other words, heterogeneity exists among the studied variables. Consequently, the application of a second-generation panel unit root test that accounts for both CSD and heterogeneity is appropriate.

Table 4.

Cross-sectional dependence and slope homogeneity tests.

Cross-sectional dependence test
Variable	CSD test	p-value
lnMRS	24.05*	.000
lnGDP	54.69*	.000
lnGRI	06.40*	.000
lnEDU	55.39*	.000
lnURB	03.57*	.000
lnLOP	42.60*	.000
lnREP	48.42*	.000

Slope homogeneity test
Test	Statistic	p-value
Δ	15.937*	.000
Δadj.	17.818*	.000

Note: * denotes level of significance at 1%

Panel unit root test results

This study recognized that the diverse characteristics of the countries involved and the complexities related to CSD in the panel data could result in spurious and misleading estimates. Consequently, this study employed second-generation CADF and CIPS panel unit root tests, which effectively counteract the uncertainty of CSD. As shown in Table 5, the results indicate that all variables are non-stationary at level but become stationary after first differencing. Hence, the null hypothesis is valid, and the variables are suitable to test the long-run equilibrium.

Table 5.

Results from panel unit root tests.

	CADF		CIPS
	At level	Δ	At level	Δ
lnMRS	−2.690	−4.441*	−3.159	−5.397*
lnGDP	−2.236	−2.857*	−1.683	−3.866*
lnGRI	−4.095	−5.815*	−4.939	−6.283*
lnEDU	−2.153	−3.313*	−2.197	−4.527*
lnURB	−2.630	−2.880*	−0.648	−2.464*
lnLOP	−2.563	−3.275*	−1.905	−4.018*
lnREP	−2.644	−4.593*	−2.896	−5.622*

Note: Δ means the first difference term. * indicates significance level at 1%.

Cointegration test results

This study used the Durbin-Hausman cointegration test to assess the long-term viability of the studied variables. At a 1% level of significance, Table 6 shows that both the group (DH_G) and panel (DH_P) tests are significant. This means that the variables that were studied are strongly linked. A long-run equilibrium relation can be estimated based on the cointegration of mineral resources, GDP per capita, education, green innovation, urbanization, labor productivity, and REP.

Table 6.

Results of the Durbin-Hausman cointegration test.

Durbin-Hausman cointegration test	DH panel	DH group
	−5.0712*	−5.8270*

Note: * indicates significance level at 1%.

Long-run results and discussion

The BCCM estimator for regression estimates was used in this analysis, and Table 7 reports these results. Economic performance in emerging economies is positively associated with REP. A 1% increase in GDP per capita leads to a 0.249% increase in REP, which suggests that improved economic performance of emerging countries will stimulate the production of renewable energy. This may be attributed to the rapid economic growth that has been experienced by emerging economies in the last few decades, leading to a rising trend in energy demand, which is a sign of rising energy supply. These findings are also consistent with the notion that economic growth significantly influences energy production. Specifically, the demand for renewable energy has increased because emerging economies have pledged to control environmental pollution by shifting their energy mix from traditional fossil fuels to clean energy. These results are in line with previous studies in the literature.²⁷

Table 7.

Panel data mean group estimation results.

	BCCM estimator			GLS regression estimator			DK errors regression estimator
Regressor	Coef.	Std. err.	p	Coef.	Std. err.	p	Coef.	Std. err.	p	VIF
Ln MRS	0.1085*	0.0185	.000	0.0568*	0.0112	.000	0.1085*	0.0294	.001	1.28
Ln GDP	0.2497*	0.0731	.001	0.5587*	0.0702	.000	0.2497*	0.0891	.009	3.35
Ln GRI	0.4512*	0.1156	.000	0.1106**	0.0482	.022	0.4512*	0.1279	.001	1.13
Ln EDU	0.753***	0.4272	.078	1.4623*	0.1951	.000	0.7530**	0.7458	.032	2.40
Ln URB	−0.0544*	0.1201	.050	−0.2905*	0.0930	.002	−0.0544*	0.2206	.007	2.83
Ln LOP	1.9053*	0.1669	.000	1.1421*	0.1193	.000	1.9053*	0.1424	.000	1.05
Constant	−5.5813*	0.4852	.000	−3.9010*	0.4812	.000	−5.5813*	0.7555	.000
R-squared	0.1762						0.1762
Root MSE							0.5433
Wald chi-squared	1051.22			426.01
p > chi-squared	0.000			0.000
Number of observations	0.000			510			510
Number of groups	510			17			17

Note: VIF, variance inflation factor. *, **, and *** represent the level of significance at 1%, 5%, and 10%, respectively;

The findings show that if mineral resources increase by 1%, REP in developing countries could go up by 0.108%, highlighting a strong positive impact on REP. Given the rising demand for renewable energy and the decreasing supply of nonrenewable power in the sample countries, it is not surprising that mineral resources are pivotal in the energy sector of these emerging economies.

These findings may reflect the ongoing energy transition in these nations, where mineral resources are crucial for producing renewable technologies such as wind turbines, solar panels, and batteries. Such development directly supports SDG-7 (Affordable and Clean Energy), which aims to significantly enhance the share of renewable energy in the global energy mix. Conversely, effectively utilizing these resources reduces reliance on fossil fuels, thereby advancing SDG-13 (Climate Action) by lowering CO₂ emissions and strengthening the resilience of energy systems against climate-related risks.

Additionally, this positive relationship indicates that the effective use of profits from mineral resources establishes a solid financial base for diversifying and expanding the energy portfolio. In developing economies, mineral wealth can promote investments in renewable energy infrastructure, facilitate knowledge transfer, and enhance institutional performance, thereby accelerating the transition to clean energy sources.

These findings resonate with endogenous growth theory, which advocates for reinvesting resource rents in innovation and human capital to foster sustained growth. These findings are also consistent with those of Berthet et al.⁶⁵ Zhu et al.,⁵ and Xu et al.¹⁴ They support the idea that resource-abundant nations can utilize profits earned from mineral resources to develop renewable energy projects, provided there is a sound governance structure in place. This highlights the importance of avoiding the “resource curse hypothesis,” which suggests that poor management of mineral resources can hinder progress toward renewable energy development.

This study identifies a statistically significant and positive relationship between green innovation and REP. A 1% increase in the coefficient of green innovation corresponds to a 0.45% increase in REP. Additionally, the coefficient of education shows a positive impact on REP, particularly enhancing growth rates in emerging economies. Specifically, a 1% increase in education is associated with a 0.753% rise in REP.

The empirical results highlight the essential and synergistic roles of green innovation and education in advancing REP. Green innovation enhances the technological landscape by improving the financial feasibility, reliability, and efficiency of renewable energy systems. It encourages the development of cleaner production methods, storage solutions, and smart grid integration, thereby reducing barriers to widespread renewable implementation.

Education, on the other side, plays a crucial role in developing the educational and scientific capabilities of nations, particularly within the renewable energy sector. It acts as an essential facilitator by enhancing human capital, fostering technical proficiency, and raising environmental consciousness. Together, these elements strengthen the capacity of societies to adopt, sustain, and utilize renewable technologies.

The positive roles of education and innovation in REP complement each other, enhancing overall efficiency. A well-educated labor force drives innovation in the energy sector, significantly boosting the effectiveness of renewable energy initiatives. This educated workforce improves the absorptive capacity of organizations, enabling them to better comprehend, adopt, and utilize technological innovations. Meanwhile, advancements in technology within the energy sector create a heightened demand for skilled labor, establishing a virtuous cycle between education and innovation. This relationship corroborates with the theoretical framework of endogenous growth theory, which suggests that sustainable development is rooted in human capital, knowledge, and innovation.

The positive impact of green innovation and education in REP reinforces global sustainability efforts. Green innovation boosts the efficiency and scalability of clean technologies, thereby advancing progress toward SDG-7. Similarly, a well-educated population increases technical proficiency and knowledge, facilitating the adoption of renewable energy solutions and fostering essential human capital for sustaining innovation and effective policy implementation. These dynamics improve access to renewable energy and reduce ongoing emissions, thereby supporting SDG-13 and accelerating the transition to low-carbon energy systems.

In the context of emerging economies, the positive association suggests that these countries may have increased their educational expenditures and research and development (R&D) spending. This investment can lead to enhanced technical education and greater environmental awareness among the public, ultimately promoting the supply of renewable energy. It is likely that the initiatives taken by emerging countries to advance education and green innovation have improved access to skilled workers and renewable energy technologies, resulting in improved REP. Furthermore, education may have enhanced production processes in energy-powered plants within these economies by introducing new knowledge, skills, and innovations that boost power plant efficiency.

These results for green innovation are consistent with those described by Zheng et al.,²⁶ Vural,²⁷ and Solarin et al.,⁶⁶ while the results for education are in line with findings by Fang et al.,³⁷ Ozbay and Duyar,⁴¹ and Zaman et al.;⁴⁰ however, they contradict the findings by Wang et al.³⁹ This discrepancy may be explained by the differences in the econometric methods used.

The remaining variables in this study indicate that labor productivity plays a role in the production of renewable energy. In contrast, urbanization hinders the production of renewable energy. This negative impact may arise from increasing land prices in urban areas, which make it less feasible to develop large-scale renewable energy infrastructure. Another possible explanation involves the imbalance between energy production and demand; as urbanization escalates energy consumption, the existing renewable energy resources may fall short of meeting this growing demand. The urbanization dynamics in emerging economies may contribute to this negative effect, as unregulated urban expansion often leads to increased energy demand that is primarily satisfied by traditional fossil fuels. Additionally, elevated emissions from transportation and industry, coupled with a lack of integration of renewable infrastructure in urban planning, may obstruct the advancement of renewable energy.

Robustness and diagnostic tests

The study employed various methods, including MMQR (Table 8), GLS regression, and DK errors regression, along with the variance inflation factor (VIF) test (Table 7), to assess the reliability of the model estimates. The results from the MMQR, GLS, and DK regressions support the estimates derived from BCCM, confirming their reliability. Furthermore, the VIF values for all variables ranged from 1.2 to 3.5, well below the threshold of 10, indicating that multicollinearity is not a concern. Consequently, the diagnostic and alternative estimation tests affirm the robustness of the model estimates. Figure 6 displays the key long-run findings.

Figure 6.

Key findings of the study. Source: Authors’ calculations.

Table 8.

Method of moment regression results.

Variable	Ln MRS	Ln GDP	Ln GRI	Ln EDU	Ln URB	Ln LOP	Constant
Location	0.1086* [0.009]	−0.2497 [0.253]	−0.4512** [0.025]	0.7534 [0.275]	−0.0544 [0.827]	1.9053* [0.000]	−5.5813* [0.002]
Scale	0.1394* [0.000]	0.2031 [0.246]	−0.3173** [0.048]	1.0129*** [0.067]	−0.5939* [0.003]	0.8290** [0.025]	−2.0928** [0.015]
Q0.05	−0.1217 [0.173]	−0.5853 [0.188]	0.0730 [0.860]	−0.9200 [0.516]	0.9267*** [0.075]	0.5356 [0.574]	−2.1236 [0.571]
Q0.10	−0.0779 [0.253]	−0.5213 [0.180]	−0.0269 [0.939]	−0.6010 [0.625]	0.7397*** [0.080]	0.7967 [0.321]	−2.7827 [0.393]
Q0.20	−0.0205 [0.723]	−0.4377 [0.182]	−0.1575 [0.598]	−0.1840 [0.859]	0.4951 [0.166]	1.1380*** [0.094]	−3.6445 [0.185]
Q0.30	0.0141 [0.785]	0.3874 [0.188]	−0.2362 [0.377]	0.0669 [0.943]	0.3481 [0.276]	1.3434** [0.027]	−4.1627 [0.091]
Q0.40	0.0578 [0.200]	0.3236 [0.203]	−0.3357 [0.148]	0.3848 [0.632]	0.1616 [0.561]	1.6036* [0.002]	−4.8197** [0.024]
Q0.50	0.1006** [0.012]	0.0261** [0.024]	0.4332** [0.033]	0.6960 [0.322]	−0.0207 [0.933]	1.8583* [0.000]	−5.4626* [0.003]
Q0.60	0.1446* [0.000]	0.1971** [0.032]	0.5334** [0.003]	1.0158 [0.105]	−0.2083 [0.346]	2.1200* [0.000]	−6.1233* [0.000]
Q0.70	0.1965* [0.000]	0.1216** [0.042]	0.6514* [0.000]	1.3926** [0.019]	−0.4292* [0.047]	2.4285* [0.000]	−6.9019* [0.000]
Q0.80	0.2753* [0.000]	0.0068** [0.047]	0.8307* [0.000]	1.9649* [0.004]	−0.7647* [0.002]	2.8968* [0.000]	−8.0842* [0.000]
Q0.90	0.3739* [0.000]	0.1367** [0.037]	1.0551* [0.000]	2.6814* [0.003]	−1.1848* [0.000]	3.4832* [0.000]	−9.5646* [0.000]
Q0. 95	0.4287* [0.001]	0.2166** [0.028]	1.1799* [0.000]	3.0796* [0.005]	−1.4183* [0.000]	3.8092* [0.000]	−10.3874* [0.000]
Observation	510	510	510	510	510	510	510
Countries	17	17	17	17	17	17	17

Note: *, **, and *** represent the level of significance at 1%, 5%, and 10% respectively. Values in square brackets denote p-values.

Limitations and policy implications

Policy implications

This study offers several important policy implications for governments and policymakers in emerging economies to promote REP based on the empirical findings. The implications are divided into short-term initiatives aimed at achieving quick outcomes and long-term strategies designed for lasting results.

Short-term policy implications

Governments can quickly establish dedicated funds to implement technology and develop clean energy infrastructure by utilizing revenues generated from mineral resources. Botswana serves as a recent example of reallocating resource earnings toward the renewable energy sector, potentially offering a template for sustainable energy initiatives. Additionally, Chile created a copper income fund that co-financed solar energy projects in the Atacama region.

Governments of emerging countries should encourage R&D activities in the power sector by allocating funds for renewable energy projects, including tax breaks and subsidies for patent applications and innovative renewable energy projects. A notable example is China's Renewable Energy Program, which demonstrates how short-term innovation subsidies have accelerated indigenous solar production.

Governments should initiate capacity-building training workshops to equip the workforce with essential skills for renewable energy technologies. This will ensure the availability of a skilled workforce capable of operating and maintaining advanced systems.

Long-term policy implications

Governments of emerging economies should strictly enforce environmentally responsible approaches for mineral extraction and utilization for REP to safeguard the long-term availability of these critical resources.

Instead of relying on mineral exports, policymakers in emerging economies should create a better environment for local production and refining industries to add more value and support a local supply chain for renewable energy. Governments should create a dedicated fund for renewable energy to guarantee ongoing financing for renewable projects, independent of commodity cycles. Mineral rents could be integrated into a green investment fund, similar to Norway's Oil Fund.

Governments of emerging economies should strengthen intellectual property rights and establish agreements with developed countries regarding technology transfer to boost the renewable energy sector. Further still, the governments of the investigated countries considered in this study should take the initiative to establish technology parks and incubator centers to encourage innovation for REP.

Governments should make education a key part of the process of integrated energy planning. Universities and research institutions in emerging economies should include renewable energy studies in their curricula to deepen understanding and build local expertise. This will foster environmental awareness and develop a skilled workforce for the energy sector. Governments should additionally initiate vocational training at the local level to equip operational workers with the practical skills required for installing, upgrading, and maintaining renewable energy infrastructure. South Africa's Renewable Energy Training Centre serves as a model for institutionalized, long-term skill development. It is also advisable that emerging countries offer scholarship programs specifically focused on mining and innovation for REP.

Practical implications of this study

This study also provides practical implications for policymakers in emerging economies, based on the empirical findings. The beneficial role of mineral resources indicates that emerging economies with abundant resources can strategically utilize their mineral profits to invest in renewable energy projects and advanced technologies. This approach can transform potential “resource dependence” into a catalyst for clean energy advancements. By establishing a renewable energy investment fund derived from mineral profits, these economies could finance renewable energy sources, thereby decreasing their reliance on fossil fuels.

The synergistic impact of green innovation and education emphasizes that without the essential human capital, innovation cannot be effectively assimilated, adapted, or maintained. Policymakers should align funding for green innovation with educational initiatives to ensure that technical expertise supports the dissemination of renewable technologies.

Limitations and suggestions for future studies

Although this study has addressed an important issue and has been carried out with considerable rigor, certain limitations remain that should be addressed in future research. This study is based on 29 years of data for a selection of emerging nations, which was constrained by data availability. Future studies could extend the analysis timeframe and consider individual countries, cross-country assessments, and other panel settings by employing new models to reveal generalizable findings. Another limitation of this study is the absence of a techno-economic and environmental analysis of REP and its associated mineral resources. This study recognizes the immense significance of these factors in driving the feasibility and sustainability of REP. However, a comprehensive analysis was beyond the scope of this study; therefore, future research extends this work by considering critical factors, such as life cycle environmental impacts, cost structures, and policy impacts in various contexts. This study found a negative relationship between urbanization and REP but could not analyze the potential moderating factors that may affect the nexus between REP and urbanization. It would be beneficial for future studies to identify and examine other factors that could potentially mitigate the negative impact of urbanization on REP. Despite using various econometric methods to confirm the reliability of the results, this study was unable to utilize AI-based experiments to verify the robustness of the estimates. Therefore, future research is encouraged to incorporate AI-based methods, such as deep learning or machine-based learning approaches, to complement the methods used in this study.

Conclusion

This research study emphasizes the urgent need to understand the factors that promote REP in emerging countries, where renewable energy is vital for both economic and environmental resilience. This study offers a new perspective on the role of mineral resources, green innovation, and education in enhancing REP, which is an important topic that has been overlooked in previous studies. Recent BCMM methods based on CSD and heterogeneity checks were used in this analysis. The empirical findings urge policymakers to effectively use mineral resources, enhance education, and promote green innovation to accelerate REP. This approach connects national energy policies with SDG-7 (Affordable and Clean Energy) and SDG-13 (Climate Action).

The application of these methods led to the following findings:

Mineral resources can positively influence REP if managed effectively and backed by financial investments in clean energy. Green innovation and education are essential factors in boosting REP in emerging economies. Green innovation fosters technological progress and reduces costs in energy systems. In contrast, education enhances the energy system's ability to absorb, implement, and maintain these advancements. Green innovation accelerates technological advancement and cost reductions in renewable systems, while education enhances the absorptive capacity needed to adopt and sustain such innovations. Together, these variables create a synergistic pathway for scaling renewable energy in contexts that are often constrained by institutional and financial barriers.

The present study contributes to the existing literature by synthesizing resource-based and endogenous growth frameworks specifically within emerging economies. Additionally, it offers practical implications for policymakers on how to convert funds generated from mineral resources into clean energy, foster green innovation, and implement educational reforms to achieve long-term clean energy goals.

A way forward for this research could involve exploring a series of policies that integrate the allocation of mineral income, green innovation, and educational reforms to achieve optimal outcomes. Conducting comparative studies between resource-rich and resource-poor economies may provide deeper insights. This study emphasizes that successful transitions to renewable energy require a cohesive integration of resource management, innovation, and enhanced human capital.

Highlights of the study

Analyzes the impact of mineral resources, green innovation, and education on renewable energy production in emerging economies.

Advanced panel data estimation tools, such as the “bias-corrected method of moments” and the “method of moment regression,” are employed.

Results reveal that mineral resources positively impact renewable energy production.

Education and green innovation are key factors in renewable energy production.

Education and approaches to using mineral resources should be the primary policy goals for renewable energy production.

Footnotes

Abbreviations/Nomenclature

Acknowledgments

The authors would like to express their gratitude to the editor and the anonymous reviewers for their constructive comments and suggestions, which have significantly enhanced the clarity, quality, and utility of this manuscript.

ORCID iD

Muhammad Awais Baloch

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Jiaxin Yang is an Associate Professor at the School of Economics and Management, Baoji University of Arts and Sciences, China. He holds a Master's degree in Technological Economics and Management from Xidian University. His research focuses on technological innovation, entrepreneurship management, and regional innovation ecosystems. Prof. Yang has led multiple provincial research projects and has published his work in several peer-reviewed journals.

Muhammad Awais Baloch. is an Associate Professor of Business Management at Baoji University of Arts and Sciences, China. He earned his Ph.D. in Business Administration from Beijing Institute of Technology. His research interests lie in environmental economics, energy transition, and the digital economy. Dr. Baloch has contributed widely to peer-reviewed international journals in the fields of sustainability, energy innovation, and climate policy.

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