Comparative Economic Impact of Green Energy Investments: Evidence from India, USA, Germany, and Denmark

Abstract

Renewable energy has become an imperative global focus in the battle against climate change, addressing energy security and the development of a sustainable energy economy. This study examines the economic effects of green energy investments in four distinct countries: India, the USA, Germany, and Denmark, using time-series data on employment generation, energy efficiency, and GDP growth for the period from 1996 to 2023. The study employed regression analysis, and the research isolates important differences in outcomes for these regions that are generated by renewable energy policies and investments. The findings suggest that labor-intensive renewable energy projects benefit emerging markets, such as India. Due to market saturation, the projects become counterproductive for mature markets like Germany. For large-scale project development in a stable policy environment, the USA scores highly, while Denmark excels in innovation and sustainability in wind energy. The study highlights the value of targeted policy interventions in maximizing the economic benefits from renewable energy. In addition, it emphasizes the need to tackle country-specific issues, encourage innovation, and ensure a fair pathway to a green energy system. The results of this research will be beneficial for policymakers and stakeholders in evaluating decisions regarding renewable energy investments.

1. Introduction

The transition of the global energy landscape is a fast-occurring evolution driven by the necessity to curb climate change, improve energy security, and realize sustainable development goals [1,2,3]. We are discussing transitioning towards renewable energy sources, such as solar, wind, hydro, and other green technologies [4]. In addition to GHG emissions reductions, these technologies create substantial socio-economic benefits and energy-secure access, especially for rural regions [5,6,7]. However, the economic and environmental impacts of renewable energy investments vary among countries, owing to differences in market maturity, policy frameworks, and types of technology [8,9,10]. Like many other emerging economies, India has shown clear economic benefits from renewable energy projects. The generation of employment and Gross Domestic Product (GDP) growth due to these projects tends to be labor-intensive [11,12]. Furthermore, renewable energy initiatives in India have enhanced energy accessibility in rural and less developed regions of the country, serving as an impetus to reduce dependence on fossil fuels and accelerate the path to inclusive socio-economic development [13,14]. Alternatively, markets, such as Germany, face challenges associated with high market saturation and high integration, necessitating innovative solutions to remain competitive. A case in point is Germany’s focus on technologies, such as energy storage systems and smart grids, which mature markets prioritize to generate economic returns and maintain grid stability [15,16,17].

With a wide span of different energy choices, the United States has successfully transitioned to renewable energy by utilizing large-scale projects with a stable energy policy environment. Private sector investment has been stimulated, energy infrastructure modernized, and jobs created by federal incentives and state-level policies [12,18]. Denmark, meanwhile, is a global exemplar in renewable energy adoption, especially in its wind energy innovation. The success of Denmark is attributed to its energetic and successful approach to working with the community, as well as ambitious policies supported by public–private partnerships, which have helped implement technological advancements and promote energy sustainability [19,20].

However, several challenges exist to a global energy transition. Applying renewable energy solutions is complicated as they entail variability in policy effectiveness, technological readiness, and socio-economic contexts [21]. Furthermore, resource limitations and geopolitical impacts compound energy transition justice and equality [22,23]. Thus, for example, while developed nations focus on grid modernization and technological innovation, developing countries focus on energy access and low cost, which leads to an uneven pace and scale of renewable energy uptake [24,25,26].

This study explores the economic impacts of green energy investments in four countries (India, the United States, Germany, and Denmark) by using multiple regression analysis over the period from 1996 to 2023. This research analyzes key employment generation, energy efficiency, and GDP growth metrics to gather insights into the economic effects of renewable energy policy and investment in diverse contexts. In addition, this study demonstrates the benefit of promoting innovation, tackling country-specific challenges, and harmonizing national policies with global sustainability targets to achieve a more equitable and sustainable energy transition worldwide. Therefore, this study makes three key contributions to the literature on renewable energy economics. First, it provides a comprehensive comparative analysis of green energy investments across four countries with varying market maturity levels—India, the USA, Germany, and Denmark—over a long period (1996–2023). This cross-country perspective fills a gap in the existing literature, which often focuses on single-country or short-term studies. Second, by integrating multiple economic indicators (GDP growth, employment, and energy use) with detailed investment data, the study offers robust evidence on the diverse socio-economic impacts of renewable energy adoption. Third, the use of combined multiple regression and time-series trend analysis provides methodological value for understanding how policy contexts and investment dynamics interact to shape economic outcomes. Together, these contributions provide policymakers and stakeholders with practical insights for designing more effective, context-specific renewable energy strategies.

This research paper is structured as follows: After the introduction, Section 2 reviews relevant literature on the economic impact of renewable energy, environmental benefits and challenges, policy and market mechanisms, and identifies research gaps. Section 3 outlines the research methodology, including data collection, a description of the variables used in the study, and the analytical approach employed. Section 4 presents the study’s results, while Section 5 and Section 6 discuss the implications of these findings. Finally, Section 6 offers potential areas for future research.

2. Review of Literature

Transitioning to renewable energy has become the primary strategy for combating climate change, providing energy security, and advancing sustainable development [1]. This Section reviews the literature on the economic, environmental, and policy dimensions of investment in renewable energy, both their transformative potential and challenges, before concluding with a discussion of adaptation measures for low-income households.

2.1. Economic Impacts of Renewable Energy

Investments in renewable energy have long been acknowledged for their capacity to generate economic benefits, particularly in terms of employment. For instance, this study estimated that the clean energy industry can create approximately 2.5 to 3.5 times more jobs per megawatt-hour of electricity generated compared to fossil fuel industries [12,27]. The impact of fiscal policies and environmental taxes on promoting the adoption of clean energy was also considered [6]. Similarly, China’s renewable energy policies created an estimated 3.4 million direct jobs in the sector by 2015, highlighting the scale of labor-intensive opportunities [28]. Furthermore, recent studies have stressed the need to apply advanced econometric techniques (i.e., panel data regression) to fully disentangle the dynamic linkages between renewable energy investments and economic outcomes since it is likely that returns to both are inversely related [18,21]. Using these methods, researchers can control for fluctuations over time and differences in country-specific factors at work, giving more reliable insights into the effects of renewable energy investments. From such studies, future research could utilize panel datasets to examine long-term trends and interactions across various contexts.

2.2. Environmental Benefits and Challenges

From reducing greenhouse gas emissions to mitigating climate change, the environmental benefits of renewable energy are substantial [29]. Many studies projected that transitioning to 100% renewable energy could reduce US greenhouse gas emissions by up to 80% by 2050, while also saving over 60,000 lives annually due to improved air quality [25]. Brazil’s renewable energy deployment could cut its CO₂ emissions by 43% by 2030 under its Paris Agreement commitments [5]. For the renewable energy transition to be equitable, marginalized communities must have access to clean energy [30]. Policies in the developing world must be put forward to help provide energy access and ensure it is affordable in regions lacking energy [22].

2.3. Policy and Market Mechanisms

Public policy is extensively documented to play a role in accelerating renewable energy transitions. The stable and transparent policy frameworks are key to successfully mobilizing private finance for green energy projects [17,21,31]. The government and the private sector must collaborate to support the energy transition through pioneering market mechanisms and the necessary policy support [32]. The green investments can simultaneously address energy security and climate mitigation goals, especially amid global energy crises [9]. Denmark’s success in renewable energy, particularly wind energy, is a global exemplar of effective policy implementation. Denmark’s achievements in its robust policy frameworks, public–private partnerships, and community engagement were attributed [19,20]. However, energy governance challenges, including regulatory barriers and market volatility, must be addressed to sustain long-term progress [33].

In addition to market mechanisms and fiscal incentives, recent studies emphasize that carbon markets play a crucial role in promoting renewable energy development by providing clear price signals for emissions reduction. For instance, recent evidence from China shows that carbon markets can significantly drive investment in the renewable energy sector by lowering financing constraints [34]. Governance quality and institutional frameworks are also critical factors [35]. Studies show that good governance has a positive impact on investment flows, aligning with the need for stable policies to attract green energy investments in emerging markets like India [36]. Moreover, issues, such as corporate greenwashing, can undermine investor confidence and distort the expected economic benefits of green energy projects [37]. These perspectives complement the present study by showing how policy design, governance, and market instruments interact to shape the scale and impact of green energy investments.

2.4. Synthesis and Research Gaps

While existing studies provide valuable insights into the economic, environmental, and policy dimensions of renewable energy, several gaps remain. For example, the research primarily deals with particular case studies or areas, constraining the universality of the outcomes in multiple settings [24]. The relationship between investments in renewable energy and socio-economic factors, such as equity, inclusion, and community engagement, is also being explored [6]. By doing so, they are applicable since emerging technologies, such as energy storage and hydrogen, which can potentially solve intermittency, are relatively new and deserve more consideration [25]. This review is therefore warranted as there remains a requirement for such fundamental, comparative analyses of renewable energy investment under different circumstances. These studies help characterize policy interventions and specific investment strategies that ensure renewable energy transitions are both fair and, most importantly, sustainable. Therefore, these gaps make it possible for future research to detail the manifold effects of renewable energy and provide stakeholders with insights into maneuvering the squishy shores of the global energy transition. These quantitative insights highlight the measurable economic and environmental impacts of renewable energy; however, there remains a need for comparative analyses to quantify differences across countries and contexts.

3. Methodology

This paper uses data sources, analytical methods, and regression models outlined in this Section. The methodology was developed to achieve the research objectives while maintaining the transparency and robustness inherent in the analysis.

3.1. Data Collection and Description

With a dataset covering many decades, the study examines the relationship between green energy investments, GDP growth, employment, and energy use efficiency across four countries. The countries are India, the USA, Germany, and Denmark, for which the study utilizes data from reputable national and international organizations, ensuring accuracy and reliability. The following provides a detailed explanation of each variable and its associated timeframe. Table 1 (a) and (b) provide a summary and descriptive statistics of the variables used in the study.

GDP Growth (%): Derived from the World Bank and International Monetary Fund (IMF) databases’ annual percentage change in gross domestic product data. Data fields for all countries from 1961 to 2023 encompass short-term economic fluctuations and long-term trends.

Employment Rate (%): The percentage of the working age population (15–64 years of age) with a focus on females, taken from labor market statistics officially published by national statistical offices and the International Labour Organization (ILO). Employment details are from 1991 to 2023, considering the initiatives in renewable energy.

Green Energy Investments (USD M): Renewable energy investment reports from the International Energy Agency (IEA) or Bloomberg New Energy Finance (BNEF) in millions of USD. Data from 2004 to 2023 reflects significant progress in adopting renewable energy.

Energy Use (TWh): Energy consumption measured in terawatt-hours by country, from the International Energy Agency (IEA). From 1961 to 2023, data provides insights into energy efficiency and consumption trends.

All variables are carefully chosen to provide a comprehensive picture of the socio-economic and environmental effects of green energy investments. Each country has 63 observations for GDP Growth and Energy Use, 33 for Employment Rates, and 20 for Green Energy Investments. These timeframes guarantee that the analysis reflects both past trends and recent developments in the renewable energy segment.

The variation in the number of observations across variables arises from differences in data availability and reporting frequency by international and national data sources. For GDP Growth and Energy use, long historical time series are consistently published by institutions, such as the World Bank, IMF, and IEA, covering the period from 1961 to 2023. Employment data, however, are only available from 1991 onwards for most countries with reliable disaggregation by renewable energy sectors and gender. For green energy investments, annual investment data at the country level are systematically reported only from 2004 onwards by the IEA and Bloomberg New Energy Finance. Therefore, the smaller number of 20 observations reflects the limited availability of consistent, comparable data on green energy investment flows, which is a common constraint in this research area. The periods nonetheless align with the phase of significant global expansion of renewable energy investment and are thus appropriate for this study’s objectives.

The dataset admits robust statistical analysis of how economic growth, employment, and energy efficiency interact within different national contexts, including descriptive statistics and regression modeling.

3.2. Analytical Approach

The economic impacts of green energy investments, as evaluated in the study, are analyzed using a two-pronged analytical approach.

3.2.1. Multiple Regression Analysis

In this manner, the relationships between green energy investments and the key economic indicators (GDP growth, employment rates, energy consumption) for each country were studied using cross-sectional regression models. Given the unavailability of panel data for nearly all variables over the period analyzed, we opted to use cross-sectional regression. Although dynamic interactions and adjustments taking place throughout a period could be investigated employing panel data regression, the authors refrained from this type of analysis for two main reasons: data sparsity and inconsistencies in measuring variables across the selected countries. This limitation could be addressed through future research that incorporates wider-ranging and consistent panel datasets.

3.2.2. Time-Series Trend Analysis

Compound Annual Growth Rate (CAGR) and time-series trends were analyzed to capture the historical performance of green energy investments and their evolving impacts on economic indicators. This provides a temporal dimension to the findings, complementing the cross-sectional regression results.

3.3. Regression Models

The regression models used in this study are specified as follows:

Model 1: Impact on Employment

(1)${\mathrm{E}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{o}\mathrm{y}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}}_{\mathrm{i}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1.{\mathrm{I}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}}_{\mathrm{i}} +{\in }_{\mathrm{i}}$

Model 2: Impact on Energy Consumption

(2)${\mathrm{E}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{u}\mathrm{m}\mathrm{p}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1.{\mathrm{I}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}}_{\mathrm{i}} +{\in }_{\mathrm{i}}$

Model 3: Impact on GDP Growth

(3)${\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{G}\mathrm{r}\mathrm{o}\mathrm{w}\mathrm{t}\mathrm{h}}_{\mathrm{i}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1.{\mathrm{I}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}}_{\mathrm{i}} +{\in }_{\mathrm{i}}$

The variables in Equations (1)–(3) are explained as follows, ${\mathrm{E}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{o}\mathrm{y}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}}_{\mathrm{i}}$ represents the Employment rate in country I, while ${\mathrm{E}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{u}\mathrm{m}\mathrm{p}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}}$ denotes Energy consumption in country I and ${\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{G}\mathrm{r}\mathrm{o}\mathrm{w}\mathrm{t}\mathrm{h}}_{\mathrm{i}}$ captures the GDP growth rate in country i. The ${\mathrm{I}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}}_{\mathrm{i}}$ refers to Green energy investments in country i. The model includes the Intercept term (${\beta }_0)$, Coefficient representing the impact of green energy investments on the respective dependent variable (${\beta }_1)$ and the Error term for country I (${\in }_i$).

These models are simple linear regressions that aim to isolate the direct effect of green energy investments on each outcome variable, assuming other factors are held constant or represented within the error term [38]. Each model was run separately for the four countries to account for differences in economic and policy contexts.

It is acknowledged that the regression models are specified in a simplified form and do not include additional control variables, such as energy prices, fiscal incentives, or macroeconomic factors, due to data limitations and comparability across countries. This may introduce some degree of omitted variable bias, which future research could address by incorporating more comprehensive panel datasets with additional explanatory variables.

Furthermore, the current analysis assumes linear relationships between green energy investments and the selected economic indicators. Future studies could apply nonlinear modeling techniques or threshold tests to investigate whether diminishing returns or other nonlinear dynamics exist.

3.4. Justification for Country Selection

This research chose the four countries to capture variations in the broad market and policy dynamics.

India: Rapidly growing green energy investments and a labor-intensive renewable energy sector make it an emerging market.

USA: Large-scale renewable energy adoption with advanced policy frameworks in a competitive and mature market.

Germany: Considerable market saturation and a high level of automation of a pioneer in renewable energy.

Denmark: Now, a global leader in wind energy innovation and development of wind energy technology.

This choice guarantees that the results mirror various economic and policy conditions.

3.5. Limitations

The study acknowledges the following limitations:

First, using cross-sectional regression models does not capture dynamic interactions over time, which could be addressed by employing panel data regression in future studies. Second, the small sample size may limit the generalizability of the findings, although robustness checks and sensitivity analyses were conducted to mitigate this issue. And finally, there may be data quality issues due to reliance on secondary sources.

4. Results and Discussion

This Section discusses the study’s findings, focusing on the economic impacts of renewable energy investments on employment generation, energy efficiency, and GDP growth across India, the USA, Germany, and Denmark. Statistical results are presented and contextualized within the existing literature.

4.1. Employment Generation

The analysis reveals that green energy investments make a significant contribution to employment generation, particularly in emerging economies like India. Table 2 presents the regression results, which show the relationship between green energy investments and employment rates.

The positive and statistically significant coefficient indicates that higher green energy investments contribute to increased employment rates in all four countries. The largest effect is observed for India (coefficient = 0.89), indicating that a 1% increase in green energy investment is associated with an estimated 0.89% increase in the employment rate. This finding underscores the labor-intensive nature of renewable energy projects in emerging markets, where such investments generate substantial job opportunities, particularly in construction, installation, and maintenance. For Denmark, the coefficient is also relatively high (0.85), which aligns with the country’s strong wind energy sector and its emphasis on community engagement and local job creation. The USA shows a positive effect (0.74), reflecting the role of large-scale projects and supportive policies in driving employment gains. Germany, however, shows the lowest positive coefficient (0.61) due to market maturity, high levels of automation, and challenges related to grid integration, which limit the marginal employment impact of additional green investments.

Our findings align with [27], which highlighted the employment potential of green energy in emerging economies earlier. The coefficient is also high in Denmark, which is a leader in wind energy innovation [19]. However, such means of correlation are backed up in Germany due to market saturation and the automation of its renewable energy sector [15,16].

4.2. Energy Consumption and Efficiency

Green energy investments have also influenced energy consumption patterns and led to improvements in energy efficiency. Table 3 summarizes the regression results for energy consumption.

Denmark shows a strong positive relationship (coefficient = 0.84), reflecting its leadership in wind energy technology and its continuous investment in grid efficiency and sustainable infrastructure. The USA also demonstrates a high coefficient (0.81), indicating that large-scale renewable projects and modernized energy infrastructure contribute to more efficient and cleaner energy use. India’s coefficient (0.72) indicates improved energy access and efficiency gains in emerging markets, where renewable energy projects expand supply, particularly in underserved rural areas. Germany’s coefficient is lower (0.55), which may reflect integration challenges in balancing intermittent renewable energy with a stable grid, as well as the fact that further investments in mature markets tend to yield smaller and marginal gains in efficiency. Overall, these results support previous findings [14,17,20] that highlight the critical role of continued investment and technological innovation in improving energy efficiency and supporting a cleaner energy mix.

4.3. GDP Growth

The findings reveal varying impacts of renewable energy investments on GDP growth across the four countries. Table 4 provides the regression results for GDP growth.

India demonstrates the strongest positive effect, with a coefficient of 0.82, suggesting that a 1% increase in green energy investment is associated with an estimated 0.82% increase in GDP growth. This reflects India’s rapid expansion of renewable energy and the large-scale, labor-intensive nature of its projects, which stimulate related sectors, such as manufacturing and infrastructure development. The USA also exhibits a positive impact (coefficient = 0.69), highlighting how stable policy frameworks and large-scale private investment contribute to sustained economic growth. Denmark’s moderate positive coefficient (0.66) indicates that its innovative wind energy sector continues to deliver economic benefits, supported by community engagement and export-oriented technology development. In contrast, Germany’s coefficient is slightly negative (−0.18), indicating that additional green energy investments may have a diminishing or even counterproductive effect on GDP growth in a mature, saturated market. This is consistent with prior studies [8,12,17,18,21,39,40], which suggest that integration challenges and high costs can limit the marginal returns from further investment in countries with well-developed renewable energy capacity. These findings underscore the importance of aligning investment strategies with market maturity and supporting innovation and grid modernization to sustain economic benefits.

4.4. Compound Annual Growth Rate (CAGR) Analysis

For each country, the Compound Annual Growth Rate (CAGR) determines the annualized growth rate of green energy investments over a particular period (Table 5). This metric measures the growth of renewable investments, indicating each country’s commitment to renewable energy development.

The CAGR is calculated using the following formula:

(4)$CAGR=({{\displaystyle \frac{V_f}{V_i}})}^{{\displaystyle \frac{1}{n}}}-1$

where:

CAGR: Compound Annual Growth Rate

$V_f$: Final value of green energy investments throughout the analysis.

$V_i$: Initial value of green energy investments at the beginning of the analysis period.

n: Number of years throughout the analysis.

The rapid adoption of renewable energy initiatives, government incentives, and growing foreign investments is evident in India, which holds the highest CAGR at 26.91%. The increase is a testament to India’s commitment to labor-intensive renewable energy development and its promise to address energy poverty. A 17.24% CAGR is shown in the USA, a hotspot for renewable energy investments. Although its CAGR is below that of India’s and Denmark’s, the USA maintains the highest absolute investment levels in large-scale global renewable initiatives. Germany’s CAGR of 7.19% reflects moderate growth in renewable energy investments. This is consistent with its already developed renewable energy market and issues related to grid integration and market saturation. A case in point is the 23.44% CAGR, as shown by Denmark. Although not as substantial as the absolute investment levels, these are high-efficiency and targeted projects [19,21]. The calculation with CAGR shows significant variance in the growth rates of green energy investments; India and Denmark continue to grow rapidly as their renewable energy sectors expand. With the highest absolute investment levels, the USA shows steady growth. As Germany is a mature market, it grows more slowly as challenges around integration come into play. The analysis shows disparate paths of renewable energy investment as a function of country-specific economic, technological, and policy circumstances.

4.5. Comparative Insights

The analysis shows that green energy investments offer diverse economic and environmental benefits and differ from country to country. India is an excellent case, displaying robust labor-intensive renewable energy projects and fast-expanding infrastructure, which lead to superior employment generation and GDP growth. The USA has a history of large-scale renewable energy initiatives and significant investment, bringing steady economic benefits and notable energy efficiency improvements. Denmark has become a leader in renewable energy innovation, especially in wind energy, and consequently, high sustainability and energy efficiency levels have been achieved. Conversely, Germany is encountering market saturation, grid issues, and high costs, limiting its economic return from renewable energy investments [20,21,41]. The findings show the need to optimize renewable energy strategies within their socio-economic, technological, and policy contexts to achieve the best economic and environmental outcomes. Though cross-sectional regression models help gain insights into these relationships, using panel data regression in future research will be helpful in obtaining more dynamic relationships over time. This would enable us to better understand how renewable energy investments affect socio-economic and environmental outcomes across countries and periods. Moreover, using more diverse case study countries (e.g., African and Southeast Asian countries) would increase the generalizability of the findings and provide a more complete picture of the global energy transition.

5. Conclusions

The transition to renewable energy is a critical transformational step for achieving global climate goals, ensuring energy security, and sustainable economic growth. This study offers a comparison of green energy investments in four countries: the United States, India, Germany, and Denmark, each corresponding to a distinct socio-economic, technological, and policy context. According to the findings, renewable energy investments contribute mainly to creating employment, increasing GDP, and improving the environment. India can capitalize on labor-intensive projects and expedited infrastructure development. At the same time, the USA maintains a strong pace, backed by large-scale investment and a stable policy framework. Denmark has excelled in wind energy innovation and energy efficiency, while Germany is rapidly expanding its wind farms, outpacing the development of adequate grid infrastructure. Policy inconsistencies, socio-economic inequalities, and technological integration mar these achievements. These findings underscore the urgent requirement for efforts informed by national priorities and global sustainability targets, with strategies tailored to specific countries. To achieve a just and equitable transition to renewable energy systems, policymakers and stakeholders must balance regional disparities, spur innovation, and implement inclusive policies [42]. It is vital to learn from the rich experiences of these countries in order to facilitate the adoption of renewable energy at a faster pace, with the most significant socio-economic and environmental benefits.

6. Policy Implications and Future Research Directions

This study emphasizes that clear, country-specific policies are crucial for maximizing the benefits of renewable energy investments. For emerging economies like India, expanding decentralized systems, such as off-grid solar, offering targeted subsidies for low-income households, and investing in workforce training can boost access and job creation [32,34]. Mature markets like Germany and Denmark should focus on modernizing grids, supporting innovation in storage and smart technologies, and encouraging community-based projects to tackle market saturation [33]. The USA should maintain stable incentives, strengthen public–private partnerships, and increase funding for new technologies like hydrogen. Globally, sharing knowledge, enhancing regional cooperation, and utilizing innovative financing mechanisms, such as green bonds, are vital. Future research should utilize larger panel data, encompass more countries, and employ dynamic methods better to inform tailored policies for an equitable green energy transition.

This study, while offering important insights, has several limitations that should be acknowledged and addressed in future research. First, the use of cross-sectional regression models does not capture dynamic interactions over time [9]. Second, the relatively small sample size limits the generalizability of the findings. Third, reliance on secondary data may lead to issues with data quality [38]. Future research should address these limitations by utilizing robust panel data, expanding the geographical scope to include underrepresented regions, such as Africa and Southeast Asia, and employing more advanced analytical techniques to capture the dynamic linkages between green energy investments and socio-economic outcomes more effectively. This study does not test the possible mediating role of employment as a transmission channel between green energy investments and GDP growth. Future research could address this by applying a mediation effect model to examine the pathway from green energy investment to employment and GDP.

Author Contributions

Conceptualization, S.K.M., P.K., T.L.B., L.D. and A.C.N.; Data curation, P.K., T.L.B., L.D. and A.C.N.; Formal analysis, S.K.M.; Methodology, S.K.M., P.K., T.L.B., L.D. and A.C.N.; Supervision, A.C.N.; Writing—original draft, S.K.M., P.K., T.L.B., L.D. and A.C.N.; Writing—review and editing, S.K.M., P.K., T.L.B., L.D. and A.C.N. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The original contributions of this study are provided within the article.

Conflicts of Interest

The authors declare no competing interests.

Footnotes

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Tables

Table 1

(a) Description of variables. (b) Descriptive Statistics.

(a)
Variable	Description				Source
GDP Growth (USD trillions)	Annual percentage change in gross domestic product				World Bank and International Monetary Fund (IMF)
Employment Rate (%)	Employment percentage of the working age population (15–64 years of age) with a focus on females				National statistical offices and the International Labour Organization (ILO)
Green Energy Investments (USD M)	In millions of USD				Reports from the International Energy Agency (IEA) or Bloomberg New Energy Finance (BNEF)
Energy Use (Btu):	Total Energy consumption in quadrillion British thermal units (Btu):				International Energy Agency (IEA)
(b)
Country		Variable	Mean	Std. Dev.	Min	Max	Count
India		GDP Growth (%)	5.48	4.13	−5.78	9.69	63
		Employment (%)	51.97	2.23	47.50	55.50	33
		Green Energy Investments (USD M)	15,450	10,987	230	35,600	20
		Energy Use (TWh)	21.67	7.84	0.23	34.00	63
USA		GDP Growth (%)	2.56	2.16	−5.55	6.05	63
		Employment (%)	60.00	2.01	56.60	63.50	33
		Green Energy Investments (USD M)	41,800	25,500	5700	88,000	20
		Energy Use (TWh)	91.78	3.82	84.44	98.97	63
Germany		GDP Growth (%)	1.46	2.38	−4.10	7.38	63
		Employment (%)	57.79	1.93	51.29	60.69	33
		Green Energy Investments (USD M)	23,850	9940	10,500	41,200	20
		Energy Use (TWh)	13.50	1.62	10.18	14.85	63
Denmark		GDP Growth (%)	2.46	2.16	−1.78	7.38	63
		Employment (%)	59.18	1.11	56.95	60.69	33
		Green Energy Investments (USD M)	3238	3255	230	8200	20
		Energy Use (TWh)	0.74	0.07	0.64	0.87	63

Table 2

Regression Results for Employment Generation.

Country	Coefficient (${\mathit{\beta }}_1$)	R2	p-Value
India	0.89	0.87	<0.01
USA	0.74	0.71	<0.01
Germany	0.61	0.62	<0.05
Denmark	0.85	0.86	<0.01

Table 3

Regression Results for Energy Consumption.

Country	Coefficient (${\mathit{\beta }}_1$)	R2	p-Value
India	0.72	0.73	<0.01
USA	0.81	0.79	<0.01
Germany	0.55	0.56	<0.05
Denmark	0.84	0.85	<0.01

Table 4

Regression Results for GDP Growth.

Country	Coefficient (${\mathit{\beta }}_1$)	R2	p-Value
India	0.82	0.82	<0.01
USA	0.69	0.68	<0.01
Germany	−0.18	−0.17	<0.05
Denmark	0.66	0.65	<0.01

Table 5

Results of CAGR Analysis (2004–2023).

Country	Initial Investment (${\mathit{V}}_{\mathit{i}}$)	Final Investment(${\mathit{V}}_{\mathit{f}}$)	Period (n)	CAGR (%)
India	USD 230 million	USD 35,600 million	20 years	29.91
USA	USD 5700 million	USD 88,000 million	20 years	17.24
Germany	USD 10,500 million	USD 41,200 million	20 years	7.19
Denmark	USD 230 million	USD 8200 million	20 years	23.44

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