Green Energy, Economic Growth, and Innovation for Sustainable Development in OECD Countries

Abstract

This study explores the interrelationship between green energy adoption, economic growth, and innovation in promoting sustainable development within OECD countries. Using a random forest regression model, the research analyzes secondary data from 2013 to 2022 to identify the most significant contributors to sustainable development. The random forest model was selected for its ability to handle non-linear relationships and feature importance ranking, providing a comprehensive understanding of the variables’ impacts. The analysis reveals that green energy adoption has the strongest influence on the human development index (HDI), with an importance score of 0.43, followed by gross domestic product (GDP) and the global innovation index (GII). These findings underscore the pivotal role of green energy adoption, amplified by economic growth and technological innovation, in advancing sustainable development. While the study focuses on OECD countries, the insights offer valuable implications for global sustainability initiatives. The evidence supports the argument that prioritizing green energy, supported by economic and innovative drivers, is crucial for achieving broader sustainable development goals. This research provides a methodological contribution by demonstrating the effectiveness of machine learning models in analyzing complex sustainability data and offers empirical evidence that informs policy and future research in a broader context.

1. Introduction

The global transition towards sustainable development has gained critical importance due to escalating environmental challenges, such as climate change, resource depletion, and biodiversity loss. These challenges necessitate a fundamental transformation of current socio-economic models, requiring shifts across economic, social, and political dimensions. The rapid pace of industrialization and economic growth has exacerbated environmental degradation, highlighting the urgency for adopting sustainable pathways. Among these pathways, the transition to green energy has emerged as a key solution not only for addressing environmental concerns but also as a driver of economic growth and innovation [1].

Integrating renewable energy sources into national energy systems is essential for reducing carbon emissions while fostering sustainable development. Green energy adoption serves as both a sustainable alternative to fossil fuels and a catalyst for economic and technological advancement [2]. Particularly in the context of advanced economies, economic growth, when driven by sustainable innovations, contributes to long-term development outcomes. The traditional dichotomy between economic growth and environmental sustainability has evolved into a more comprehensive understanding of their interdependence as green innovations increasingly drive economic performance [3]. Technological advancements, especially in renewable energy technologies, enhance the efficiency and cost-effectiveness of green energy solutions, promoting their widespread adoption. Moreover, innovations improve productivity and economic efficiency, thereby contributing to sustainable development.

Despite a significant body of research examining green energy adoption, economic growth, and innovation as individual factors [4,5], their combined and synergistic effects on sustainable development have been underexplored. To address this gap, the present study investigates the interactions among these factors in the context of OECD countries, focusing on their collective impact on the human development index (HDI), a widely recognized measure of sustainable development [6]. The primary objectives of this study are as follows: (1) to evaluate the relationships among green energy adoption, economic growth, and innovation in OECD countries; (2) to quantify their combined impact on sustainable development outcomes; (3) to provide actionable insights for policymakers. The research questions guiding this study include the following: How does green energy adoption contribute to sustainable development in OECD countries? What is the role of economic growth in enhancing development outcomes? How does innovation facilitate green energy utilization, and what are its effects on sustainability? Furthermore, are there significant differences in development outcomes between countries with advanced practices in green energy and innovation?

This research utilizes a random forest regression model, chosen for its ability to capture non-linear relationships and its robustness against overfitting. Compared to alternative regression models, random forest is particularly effective in analyzing complex interactions among variables. The analysis draws on data from 2013 to 2022, using green energy adoption (GEA), gross domestic product (GDP), the global innovation index (GII), and HDI as key variables. The inclusion of green energy adoption, economic growth, and innovation as primary variables in this study is informed by their intrinsic and interconnected roles in driving sustainable development. These variables encapsulate the broader influence of underlying policy measures and social dynamics, enabling a focused and robust analysis of their combined impact on sustainable development. By focusing on OECD countries, the study captures long-term trends in sustainable development among advanced economies, offering a comprehensive perspective on how green energy, innovation, and economic growth contribute to sustainable outcomes. This study makes a novel contribution by examining the synergistic effects of green energy, economic growth, and innovation on sustainable development in OECD countries. The findings will provide evidence-based insights for policymakers, enabling the formulation of strategies that support sustainability at the intersection of energy policy, economic growth, and innovation. These insights are expected to have significant implications for advancing sustainable development agendas globally.

The remainder of this paper is organized as follows: Section 2 presents a comprehensive literature review that contextualizes the key variables, e.g., green energy adoption, economic growth, and innovation, and their impacts on sustainable development. Section 3 describes the methodology employed, including data sources and the random forest regression model used for analysis. The results are detailed in Section 4, highlighting the significance of each variable in predicting sustainable development outcomes. Section 5 discusses the implications of the findings, integrating them with existing literature and theoretical frameworks. Finally, Section 6 concludes the study by summarizing the main insights and suggesting future research directions.

2. Literature Review

The interactions between green energy adoption, economic growth, innovation, and sustainable development have been the subject of extensive research, reflecting growing interest in understanding how these factors contribute to sustainability. This literature review provides an overview of key studies, offering theoretical background and context for the current study.

Green energy adoption is widely regarded as a pivotal element of sustainable development, with various studies demonstrating its environmental and socio-economic benefits. Research by [7] highlights how transitioning to renewable energy sources like wind, solar, and hydroelectric power can significantly reduce environmental degradation while promoting social well-being and economic resilience. Mentes [8] emphasized the impact of green energy on environmental sustainability within the European Union, mirroring findings by [9], who demonstrated that nations heavily reliant on renewable energy showed significant progress in achieving sustainable development. Additionally, early adopters of green energy, according to [10], exhibited superior sustainability outcomes compared to late adopters, further underscoring the critical role of renewable energy in fostering long-term sustainability.

Research into the link between economic growth and sustainable development presents a complex picture. Coscieme et al. [11] provide evidence of long-term co-integration between GDP and sustainable development, reinforcing the idea that economic growth if coupled with sustainable practices, can enhance societal welfare without jeopardizing environmental integrity. Can et al. [12] argue that the traditional economic model, which depends heavily on fossil fuels, must evolve to include renewable energy and sustainability-driven innovations for continued prosperity and ecological balance. Vezzoni [13] similarly advocates for “green growth”, contending that economic growth and environmental conservation can coexist through the adoption of green technologies and sustainable policies. Becker [14] adds that sustainable economic growth strategies bolster resilience to global environmental risks, providing further evidence of the intricate connections between economic development and sustainability.

Innovation is another critical driver of sustainable development. Technological advancements, in particular, have been shown to facilitate green growth and promote sustainable practices [15]. Singh et al. [16] highlighted the dual role of innovation in boosting economic productivity and encouraging more sustainable consumption patterns. Similarly, Satrovic et al. [17] demonstrated that innovation can significantly reframe how resources are utilized, aligning economic growth with environmental sustainability. Moreover, Mehmood et al. [18] show that innovation allows countries to bypass traditional, unsustainable industrialization processes in favor of greener alternatives. Awan [19] extends this argument by highlighting the importance of social and institutional innovation alongside technological advances, arguing that such innovations are essential for steering societies towards sustainable development. Cheng et al. [20] focus on “green innovation”, stressing the importance of policy incentives in promoting technological developments that directly reduce environmental impacts. In addition to driving sustainable development, innovation is a key enabler of green energy adoption. Jahanger et al. [21] provide evidence that advances in renewable energy technologies improve efficiency and lower costs, thereby facilitating widespread adoption. Wei et al. [22] similarly find that innovations in renewable energy significantly bolster green energy utilization, a conclusion echoed by Stevens [23], who argues that both technological and policy innovations are essential in accelerating the shift towards renewable energy.

While the existing literature provides substantial evidence on the individual impacts of green energy, economic growth, and innovation on sustainable development, less attention has been given to their synergistic effects. This presents a significant research gap that this study seeks to address. By applying a random forest regression model to OECD countries’ data, this research contributes to the literature by offering new empirical insights into how these factors collectively influence sustainable development. The model’s ability to capture non-linear interactions and rank the importance of variables offers a comprehensive understanding of the relationships between green energy adoption, economic growth, innovation, and sustainable development. This study, therefore, not only builds upon previous research but also provides a robust methodological contribution to the field.

Theoretical Framework

To support the interpretation of the interactions investigated in this study, we draw on the sustainable development theory (SDT) and the environmental Kuznets curve (EKC) hypothesis. The SDT posits that economic growth, environmental protection, and social equity must be pursued simultaneously to achieve sustainable outcomes [24]. This theory underscores the interconnectedness of green energy adoption, economic growth, and innovation as essential elements of sustainable development. According to SDT, the transition to renewable energy not only mitigates environmental impacts but also fosters social well-being and economic resilience, aligning with findings from the existing literature [7,9]. The EKC hypothesis further complements this framework by suggesting that economic growth initially exacerbates environmental degradation, but after reaching a certain income threshold, it facilitates the adoption of cleaner technologies [25]. This model supports the idea that innovation and investments in green technologies can reverse the adverse effects of early-stage industrialization, leading to sustainable economic growth [11]. Integrating these theoretical perspectives provides a comprehensive framework for understanding how the combined effects of green energy adoption, economic growth, and innovation contribute to sustainable development. This theoretical underpinning enriches the discussion of results by linking empirical findings with established theories, thereby enhancing the interpretative depth of the analysis.

3. Materials and Methods

3.1. Data and Sources

This study uses the HDI as a proxy for sustainable development. HDI, a composite measure of life expectancy, education, and per capita income, extends beyond economic growth by capturing broader human development outcomes. Data for HDI were sourced from the United Nations Development Programme (UNDP), reflecting each country’s development progress. The first independent variable, GEA, represents the transition towards sustainable energy by quantifying the percentage of total energy produced from renewable sources such as wind, solar, and hydroelectric power. These data were obtained from the International Energy Agency (IEA). The second independent variable, GDP, measures economic output and is sourced from the World Bank. GDP reflects economic growth, an essential pillar of sustainable development, particularly when linked to green innovation. Finally, the GII, sourced from INSEAD, represents a country’s innovation capacity and success. GII data highlight the role of technological innovation in promoting sustainable practices and supporting economic growth. The impact of the COVID-19 pandemic on economic growth is acknowledged due to its significant influence on GDP trends from 2013 to 2022. Pandemic-induced economic disruptions, particularly during 2020 and 2021, introduced variability that could affect the interpretation of sustainable development metrics. Although no additional analysis was conducted, the economic contraction and subsequent recovery during this period are likely captured in the data trends presented. This context should be considered when interpreting the economic results.

3.2. Modeling

To analyze the relationships between GEA, GDP, GII, and sustainable development, this study employs the random forest regressor, a machine-learning algorithm known for its robustness and versatility in handling complex datasets. Random forest is particularly suitable for regression tasks involving continuous data, such as HDI, due to its resistance to overfitting and ability to quantify feature importance [26]. The model was chosen for its ability to handle non-linear relationships between predictors and response variables and for its interpretability, particularly in contexts with multidimensional data.

The random forest regressor constructs multiple decision trees during training, each based on a bootstrap sample of the dataset. The model outputs the average of the predictions from all trees, reducing variance and increasing accuracy. For a new observation $x^{*}$, the prediction is computed as follows:

$$\widehat{Y}\left(x^{*}\right)={\displaystyle \frac{1}{m}}\sum _{i=1}^m{f}_i\left(x^{*}\right),$$

(1)

where $f_i\left(x^{*}\right)$ is the prediction from the $i^{th}$ tree. The optimal number of trees, along with other hyperparameters, is determined through grid search cross-validation. This process enhances predictive performance by fine-tuning the model to minimize errors while avoiding overfitting.

Dataset D comprises ${(GEA,GDP,GII,HDI)}_i$ for each of the i-th OECD countries. The feature vector ${(GEA,GDP,GII)}_i$, denoted as $X_i\in {\mathbb{R}}^3$, corresponds to the input variables, while $HD{I}_i$ represents the response variable Y. The random forest model generates B decision trees, each of which selects m variables at random from the total set of predictors. The best split is chosen by minimizing the mean squared error (MSE):

$$MSE={\displaystyle \frac{1}{N}}\sum _{i=1}^n{(Y_i-{\widehat{Y}}_i)}^2,$$

(2)

where $Y_i$ is the actual value, ${\widehat{Y}}_i$ is the predicted value, and N is the number of observations.

To find the optimal split, the algorithm selects the split $s^{*}$ that minimizes the sum of the MSE for the left and right child nodes:

$$s^{*}=\underset{s\in S}{min}[MS{E}_{\mathrm{left}}\left(s\right)+MS{E}_{\mathrm{right}}\left(s\right)],$$

(3)

where S is the set of all possible splits. The average prediction from the ensemble of B trees is then computed as follows:

$$\widehat{y}\left(x\right)={\displaystyle \frac{1}{B}}\sum _{b=1}^B{T}_b\left(x\right),$$

(4)

where $T_b\left(x\right)$ is the prediction from the $b^{th}$ tree.

The performance of the random forest model is evaluated using two metrics: mean squared error (MSE) and R-squared statistic. MSE measures the average squared difference between actual and predicted HDI values, providing a straightforward indicator of prediction accuracy. The R-squared value indicates the proportion of variance in HDI that is explained by the predictors, calculated as follows:

$$R^2=1-{\displaystyle \frac{S{S}_{\mathrm{res}}}{S{S}_{\mathrm{tot}}}},$$

(5)

where $S{S}_{\mathrm{res}}={\sum }_{i=1}^n{(y_i-{\widehat{Y}}_i)}^2$ represents the residual sum of squares, and $S{S}_{\mathrm{tot}}={\sum }_{i=1}^n{(y_i-\overline{y})}^2$ is the total sum of squares. $\overline{y}$ is the mean of the observed data.

Validation is conducted using a hold-out test set comprising 20% of the original data, offering an unbiased estimate of the model’s performance on new, unseen data.

Feature importance analysis provides insights into the contribution of GEA, GDP, and GII in predicting HDI. In a random forest model, feature importance is calculated based on the average decrease in MSE resulting from splits over a particular variable, averaged over all trees:

$$I\left(v\right)={\displaystyle \frac{1}{B}}\sum _{b=1}^B{I}_b\left(v\right),$$

(6)

where $I_b\left(v\right)$ is the importance score of variable v for the $b^{th}$ tree, computed as the total decrease in MSE:

$$I_b\left(v\right)=\sum _{t\in T_b:v\left(t\right)=v}\mathrm{\Delta }MS{E}_b\left(t\right).$$

(7)

This analysis highlights the significance of each variable in the model, offering valuable insights into how green energy adoption, economic growth, and innovation contribute to sustainable development outcomes.

4. Results

Table 1. Descriptive statistics of key variables (2013–2022).

Variable	N	Mean	Median	SD	Min	Max	Skew	Kurtosis
GEA	380	59.2%	59.1%	14.5%	30.0%	85.0%	0.10	−0.20
GDP	380	$46,072	$45,753	$8565	$30,438	$67,425	0.15	−0.25
GII	380	51.7	51.8	8.2	33.0	70.0	−0.05	−0.15
HDI	380	0.87	0.88	0.06	0.72	0.98	−0.10	0.05

provides an overview of the descriptive statistics for the primary variables across 38 OECD countries from 2013 to 2022. GEA exhibits a mean value of 59.2%, reflecting moderate progress in renewable energy integration. The near-symmetry between the mean (59.2%) and median (59.1%) suggests a relatively balanced distribution of GEA across countries, while a standard deviation of 14.5% indicates moderate cross-country variation. This distribution is further characterized by minimal skewness (0.10) and kurtosis (−0.20), indicating an absence of extreme outliers. However, the GEA range from 30.0% to 85.0% reveals significant differences in adoption rates, with certain countries demonstrating a stronger commitment to renewable energy than others. Economic output, as measured by GDP, shows a mean of USD 46,072, with a median of USD 45,753, reflecting a relatively symmetric distribution of economic wealth. The standard deviation of USD 8565 indicates moderate dispersion, while slight skewness (0.15) and negative kurtosis (−0.25) suggest a tendency toward higher GDP values with limited extreme deviations. The range from USD 30,438 to USD 67,425 underscores the economic disparities within the OECD, which may influence countries’ capacities to invest in green technologies. The GII, with a mean of 51.7 and a median of 51.8, shows a slight negative skew (−0.05). A standard deviation of 8.2 reflects moderate variability in innovation capabilities, which play a critical role in supporting green energy adoption and economic competitiveness. Similarly, the HDI demonstrates high uniformity across OECD nations, with a mean of 0.87 and a small standard deviation of 0.06. This consistency in HDI, ranging from 0.72 to 0.98, highlights the OECD’s focus on human well-being and sustainable social policies.

Figure 1 offers a visual representation of the distribution and variability of these variables. The GEA boxplot shows that most OECD countries exhibit moderate levels of renewable energy adoption, with a median slightly above 55%. The interquartile range is narrow, suggesting that many countries follow a similar trajectory in transitioning to renewable energy, although significant variability exists. This variability may be linked to differences in national energy policies, investment in green technology, and geographic factors that affect renewable energy capacity. The GDP boxplot reveals more pronounced disparities, with a longer upper whisker indicating substantial economic heterogeneity. Luxembourg, for instance, stands out with its exceptionally high GDP, while Mexico remains at the lower end of the spectrum. These variations raise critical questions about the relationship between economic size and sustainability, prompting a deeper examination of whether high GDP values are aligned with sustainable practices or driven by short-term, resource-intensive strategies.

Figure 2 tracks trends over the decade, illustrating how GEA, GDP, GII, and HDI have evolved from 2013 to 2022. GEA shows a steady upward trend, rising from 48.5% to 59.2%, reflecting the increasing prioritization of renewable energy as part of broader decarbonization efforts. The alignment of GEA growth with global environmental policies suggests that sustained policy interventions and international agreements, such as the Paris Agreement, have played a pivotal role in driving these changes. GDP also shows steady growth, but this raises important questions about whether this growth is supported by sustainable practices or whether it may be driven by unsustainable economic activities, such as fossil fuel reliance in certain countries. The GII exhibits modest improvement over the period, suggesting gradual enhancements in innovation capacities that support green energy solutions and contribute to long-term economic competitiveness. HDI trends also show consistent improvement, implying that life expectancy, education, and income levels have improved alongside economic and environmental advancements. These positive trajectories, however, warrant further exploration to determine the extent to which they reflect true progress towards sustainability or are influenced by external factors such as economic cycles or technological breakthroughs.

The correlation analysis in

Table 2. Correlation matrix of key variables.

Variable	GEA	GDP	GII	HDI
GEA	1.000	0.421 *	0.587 *	0.629 *
GDP	0.421 *	1.000	0.367 *	0.539 *
GII	0.587 *	0.367 *	1.000	0.665 *
HDI	0.629 *	0.539 *	0.665 *	1.000

* p < 0.001. All correlations are statistically significant at the 0.1% level.

offers key insights into the relationships among GEA, GDP, GII, and HDI, all of which are statistically significant at a p-value less than 0.001. The moderate positive correlation between GEA and GDP (0.421) suggests a potential synergy between economic growth and renewable energy adoption, though further analysis is needed to explore causality. A stronger relationship is observed between GEA and GII (0.587), indicating that countries with stronger innovation systems are more capable of adopting green energy technologies. This underscores the importance of fostering innovation as a driver of sustainable energy transitions. The strongest correlation is between GEA and HDI (0.629), reinforcing the link between renewable energy adoption and overall human development, which supports the argument that green energy contributes significantly to sustainable development outcomes.

Figure 3 presents scatter plots to visualize the relationships between key variables. The plot between GEA and HDI reveals a clear positive association, exemplified by countries such as Iceland, Norway, and Sweden, which rank high in both green energy adoption and human development. In contrast, countries like Mexico and Turkey show lower scores, suggesting that targeted improvements in green energy adoption could enhance their overall development performance. The relationship between GDP and HDI, although positive, is more nuanced, as economic growth does not always lead to equitable development outcomes, as seen in cases like Mexico. Finally, the relationship between GII and HDI shows a strong positive correlation, underscoring the role of innovation in promoting sustainable human development.

The results of the random forest model in

Table 3. Feature importance scores of the random forest model.

Feature	Importance Score
Green Energy Adoption	0.43
GDP	0.37
GII	0.20

confirm that GEA is the most important predictor of sustainable development, with an importance score of 0.43. This finding highlights the central role of renewable energy in driving improvements in human development, directly addressing the first research question on GEA’s impact on sustainable development. The second most important predictor, GDP, with an importance score of 0.37, demonstrates the significant interaction between economic growth and green energy adoption, suggesting that wealthier economies are better positioned to invest in renewable energy infrastructure. While the GII has a lower importance score (0.20), it remains a crucial factor in enhancing the efficiency of green energy solutions and fostering long-term sustainability.

The model’s hyperparameters, displayed in

Table 4. Hyperparameters of the random forest model.

Hyperparameter	Value
Number of trees	1000
Max depth of tree	None
Min samples split	2
Min samples leaf	1
Max features	Auto

, were carefully selected to balance complexity and performance. A total of 1000 trees was chosen to ensure sufficient model complexity without overfitting, as demonstrated by the decreasing out-of-bag (OOB) error. The maximum depth of the trees remains unconstrained, allowing the model to fully explore the relationships in the data, while the minimum sample size required to split a node is set at two. These hyperparameters enable the model to capture non-linear patterns and interactions between the predictors, enhancing its predictive accuracy.

The cross-validation results, shown in

Table 5. Cross-validation results.

Fold	Train MSE	Test MSE	Train R2	Test R2
1	0.0012	0.0016	0.89	0.87
2	0.0011	0.0017	0.90	0.86
3	0.0013	0.0014	0.88	0.88
4	0.0014	0.0013	0.87	0.89
5	0.0012	0.0015	0.89	0.87
Mean	0.00124	0.0015	0.886	0.874

, provide further validation of the model’s robustness. A five-fold cross-validation procedure was employed to evaluate the model’s performance on unseen data. The mean squared error (MSE) remains consistently low across all folds, with a mean training MSE of 0.00124 and a mean test MSE of 0.0015. This consistency indicates that the model generalizes well, minimizing the risk of overfitting. The R-squared values confirm that the model explains approximately 88.6% of the variance in the training set and 87.4% in the test set, signifying strong predictive power and reliability in capturing the underlying dynamics of sustainable development.

Figure 4 provides several visualizations to assess the model’s performance. The OOB error rate decreases as the number of trees increases, stabilizing around 1000 trees with an error rate of 0.10. This trend justifies the choice of 1000 trees, as it balances model complexity and predictive accuracy without overfitting. The learning curve further illustrates the model’s consistent performance across different training set sizes, demonstrating a good trade-off between bias and variance. The predicted vs. actual Plot confirms the model’s accuracy, as the predicted HDI values align closely with the actual observations, providing additional evidence of the model’s reliability. Lastly, the partial dependence plots visually depict the marginal effects of each predictor on HDI, highlighting the dominant influence of GEA and GDP in driving sustainable development outcomes.

The random forest model demonstrates high predictive power, accurately capturing the complex relationships between green energy adoption, economic growth, innovation, and sustainable development. The findings confirm the primary role of GEA and GDP, with innovation acting as a supporting factor. These results align with the study’s hypotheses and provide empirical evidence that green energy and economic growth are critical drivers of sustainable development in OECD countries.

5. Discussion

Several key findings emerge from this study, offering valuable insights into the drivers of sustainable development among OECD countries. The high feature importance score of GEA emphasizes its critical role in promoting sustainable development, as measured by the HDI. This result aligns with prior research by Sasmaz et al. [27], which found a strong positive correlation between renewable energy consumption and HDI across OECD countries. The results here further substantiate the argument that green energy adoption is central to achieving sustainable development outcomes, contributing to the growing body of evidence advocating for renewable energy as a pathway to sustainability. This study’s findings also echo the work of Spring [28], reinforcing the importance of GEA in sustainable development across developed economies.

Economic growth, measured by GDP, also plays a significant role in fostering sustainable development, as highlighted by its high feature importance score. This finding resonates with the environmental Kuznets curve (EKC) hypothesis, which posits that economic growth can lead to environmental improvements as countries invest in cleaner technologies [25]. The observed relationship between GDP and HDI aligns with the EKC hypothesis, suggesting that economic growth, coupled with investments in cleaner technologies, can lead to sustainable development outcomes. The relation between GDP and HDI in this study is consistent with the long-run co-integration observed by Kummu et al. [29], reinforcing the notion that, when aligned with sustainable practices, economic growth can contribute to broader developmental gains. However, the results here offer a comprehensive perspective by emphasizing the interaction between economic growth and green energy adoption, suggesting that the positive effects of GDP on sustainable development are amplified when supported by investments in renewable energy.

Innovation, as captured by the GII, also emerges as a crucial factor, though its importance is lower than that of GEA and GDP. Innovation enhances the efficiency of green energy solutions and supports long-term sustainability, extending the findings of [30], who identified innovation as a key contributor to human and sustainable development. The role of GII in this study suggests that while innovation may not be the primary driver, it plays a supportive role by fostering technological advancements that enhance the adoption and effectiveness of renewable energy solutions. This supports the argument made by [31], who noted that innovation acts as a catalyst for the effective use of green energy. The results of this study align with those of [32], who found that green energy consumption, economic growth, and innovation are vital contributors to sustainable development across Asian countries. The congruence between these findings and those presented here suggests that the synergies between these elements, e.g., green energy adoption, economic growth, and innovation, are universal across different regions. This supports the view that countries seeking to enhance their sustainable development outcomes must prioritize policies that integrate these three factors effectively.

The findings from this study provide valuable insights for policymakers seeking to foster sustainable development. The significant roles of GEA, GDP, and GII underscore the need for policy-driven initiatives that promote renewable energy adoption, economic growth aligned with sustainability goals, and innovation that supports clean energy solutions. By harnessing the synergies between these elements, countries can enhance their sustainable development outcomes and move toward a greener future. While this study focuses on OECD countries, it is important to consider whether its findings may be applicable to developing or non-OECD countries. The economic, social, and technological contexts in non-OECD nations can differ significantly, influencing the generalizability of these results. For instance, non-OECD countries may face greater economic constraints, limited technological infrastructure, and differing policy priorities, which could alter the dynamics observed in this study. Future research should extend this analysis to include a broader set of countries, particularly developing nations, to better understand how these interactions manifest under varying conditions. Additionally, these contexts may require tailored strategies that balance the need for economic growth with sustainable energy transitions and innovation.

6. Conclusions

This research underscores the urgency of addressing environmental challenges and highlights the critical roles of green energy adoption, economic growth, and innovation in fostering sustainable development. The findings provide empirical support for the argument that these factors interact synergistically, contributing to sustainability in OECD countries. Specifically, this study demonstrates that green energy adoption emerges as a primary driver of sustainable development, reinforcing the view that renewable energy not only mitigates greenhouse gas emissions but also promotes economic growth. The analysis reveals that economic prosperity and environmental sustainability are not mutually exclusive, and through the integration of green energy, these objectives can be aligned. Moreover, innovation plays a vital role in enhancing the efficiency and widespread adoption of green energy technologies, which further accelerates sustainability outcomes.

A key contribution of this research is the evidence of the interconnectedness between these factors, providing a foundation for integrated policy frameworks. This synergy suggests that focusing on green energy adoption alone is insufficient. Instead, policymakers should develop strategies that leverage the mutually reinforcing effects of innovation and economic growth to create a comprehensive approach to sustainability. For instance, incentivizing research and development in renewable technologies could further enhance the efficiency of green energy systems while simultaneously contributing to economic growth. In this way, policies that stimulate innovation and support green energy adoption could provide a robust foundation for long-term sustainable development. Additionally, the study offers important policy implications. Policymakers should prioritize the integration of green energy technologies while also fostering innovation ecosystems that support sustainable economic growth. This balanced approach is essential to achieving long-term sustainability goals, as it addresses both environmental and economic imperatives. However, it is critical that these policies are context-specific, taking into account the unique socio-economic and infrastructural conditions of individual countries. For example, while OECD countries may have the capacity to invest heavily in renewable energy infrastructures, developing countries may require tailored strategies that focus on building innovation capacities and ensuring equitable access to sustainable technologies.