Abstract
The transition to RE is a critical component of India’s sustainable development agenda, particularly within the framework of Revolution 6.0, which emphasizes digitalization, artificial intelligence, and innovative energy solutions. This study investigates the nexus between economic growth (EG), foreign direct investment (FDI), fossil fuel consumption (FFC), and renewable energy (RE) in India, providing new empirical insights into the drivers and constraints of energy transition. The study examines relationships among these variables using time-series data from 1990 to 2023 and employing the Vector Autoregression (VAR) model and Granger Causality Tests (GCT). The findings reveal that FDI positively influences RE adoption in sectors with government incentives. However, EG remains heavily reliant on FFs, posing challenges to India’s clean energy targets. The results also highlight a bidirectional causality between RE and FDI, suggesting that investment inflows are both a driver and a beneficiary of the green energy transition. Digitalization and policy reforms under Revolution 6.0 are accelerating energy efficiency and technological advancements, though regulatory bottlenecks and infrastructure limitations persist. This study makes two key contributions: (1) it provides empirical evidence on how economic and investment factors shape India’s RE trajectory, and (2) it offers policy recommendations for balancing economic expansion with sustainable energy adoption. This research advances the literature on sustainable energy transitions in emerging economies by integrating advanced econometric techniques with policy analysis. The study concludes with targeted policy strategies, emphasizing FDI incentives, regulatory reforms, and digital innovations to support India’s clean energy ambitions.
Keywords
Introduction
What We Know: Theoretical Perspectives and Empirical Findings
Economic and environmental literature has extensively studied the relationship between EG, FDI, FFC, and RE. Existing research broadly supports the Environmental Kuznets Curve (EKC) hypothesis, which suggests that EG initially leads to environmental degradation before reaching a turning point where cleaner energy adoption accelerates (Pata & Caglar, 2021). FDI has also been recognized as a double-edged sword. While it fosters industrial growth and employment, it can reinforce FF dependence (Alyamani et al., 2024) or drive green technology diffusion (Kul et al., 2020).
The emergence of REV—marked by artificial intelligence, blockchain, the Internet of Things (IoT), and innovative energy solutions—has reshaped energy dynamics. Countries integrating these technologies experience improvements in energy efficiency, grid management, and RE adoption (Şahin, 2021; H. Xu et al., 2023; Xuan, 2025b). India, a fast-growing economy with increasing energy demands, stands at the crossroads of an energy transition. Despite ambitious RE targets (e.g., the goal of 500 GW non-FF capacity by 2030), FFC continues to dominate the energy mix.
What We Do Not Know: The Major Puzzle
Despite a rich body of literature, significant gaps remain in understanding the interplay between EG, FDI, FFs, and renewables in the context of REV: Does EG still reinforce FF dependency, or does REV enable a direct shift toward renewables? Prior research suggests a trade-off between growth and sustainability, but new technological advances may disrupt this pattern. What is the role of FDI in shaping India’s energy transition? While FDI can support clean energy or exacerbate FF reliance, the specific mechanisms driving its impact remain empirically unclear in the Indian context.
How do technological innovations under REV influence energy consumption patterns? Previous research has not fully incorporated digital transformations into traditional economic-energy models. Addressing these gaps is critical for both policymakers and scholars. With India being the world’s third-largest energy consumer, the findings of this study will have global implications for countries seeking sustainable development in the digital era.
What We Will Learn: The Study’s Contribution
This study advances scholarly understanding by providing new empirical insights into the evolving nexus between EG, FDI, FF, and RE (Huang et al., 2023; Kul et al., 2020). Using the VAR approach challenges conventional perspectives by reevaluating the EG-energy paradigm in the age of digital transformation. We test whether technological advancements disrupt the traditional GDP-energy relationship, proving whether India is shifting toward a direct green transition rather than following the EKC trajectory.
Unraveling the FDI-energy dynamics in India. Unlike previous studies that treat FDI as a monolithic force, we distinguish between FDI that supports FF projects versus FDI that fosters RE adoption, offering a more nuanced perspective and integrating REV into energy transition research. This study pioneers an empirical exploration of how AI, IoT, and blockchain impact India’s RE expansion, filling a critical gap in the literature.
Motivation and Theoretical Contribution
By integrating economic, technological, and energy perspectives, this study provides a comprehensive framework to understand how digital transformation alters traditional energy-economic linkages. It contributes to the Energy Transition Theory by examining whether REV enables a direct shift to renewables, challenging the traditional EKC perspective. FDI and Sustainability Research: By disentangling the mixed effects of FDI on energy transitions in developing economies. Technological Disruption in Energy Studies: By incorporating digital innovations into energy models, an area that remains underexplored in existing literature (Solangi et al., 2019; Wang et al., 2020). Defining REV and Its Relevance to Energy Transitions in India
What is REV? REV refers to the sixth wave of industrial and technological transformation, characterized by hyper-digitalization, artificial intelligence (AI), blockchain, the Internet of Things (IoT), quantum computing, and decentralized energy networks. Building upon Industry 4.0 (automation, AI, and IoT) and Revolution 5.0 (human-AI collaboration and sustainability integration), REV marks a paradigm shift where AI-driven optimization transforms energy consumption patterns, reducing inefficiencies. Blockchain and decentralized smart grids enable transparent and efficient energy trading. Quantum computing and deep learning enhance predictive analytics for energy demand forecasting. RE integration is enhanced by digital solutions that improve storage, distribution, and real-time adaptability. This revolution signifies a convergence of technological and energy system advancements, making energy transitions faster, more dynamic, and data-driven than in previous industrial phases.
Traditional energy transitions follow linear pathways, shifting from FFs to renewables through policy incentives, economic shifts, and technological advancements. However, REV redefines this transition as a decentralized, multi-directional process, where Digital twin simulations and AI algorithms predict and optimize energy needs in real time. Blockchain-powered peer-to-peer (P2P) energy trading allows direct RE transactions without centralized grids. IoT-based energy monitoring helps reduce inefficiencies in industrial and household energy consumption. Smart grids and hybrid energy systems create a coexisting framework where FFs and renewables operate in an optimized, demand-responsive network.
India stands at a critical crossroads in its energy transition, facing rising energy demand, sustainability goals, and digitalization expansion. REV principles are particularly relevant in India due to Energy Security Challenges: India relies on FFs (especially coal) for over 50% of its energy mix, making a hybrid energy transition necessary rather than a complete phase-out. REV provides a digital framework to optimize FF efficiency while integrating renewables. RE Expansion: India aims for 500 GW of non-FF capacity by 2030, requiring digital infrastructure (AI-driven grid management, smart energy storage, blockchain markets) to ensure efficient deployment.
FDI and Energy Investments: REV influences FDI inflows into India’s energy sector, as AI-powered energy startups, blockchain trading platforms, and IoT-based energy management become attractive investment opportunities for foreign investors. Government Initiatives and Policy Digitalization: “Digital India” and “Smart Cities Mission” align with REV by promoting AI-based energy management and decentralized energy networks in urban and rural areas.
Why REV Matters for This Study—By defining REV as the intersection of digitalization and energy transitions, this study highlights its role in shaping India’s EG, FDI attractiveness, and energy landscape. The research provides new insights into how digital innovations transform traditional energy-economic paradigms, emphasizing a hybrid transition model rather than a simple FF-to-renewable shift.
This era presents unique global challenges and opportunities for energy systems, particularly in emerging economies like India. As India seeks to balance EG with sustainable development, understanding the nexus between EG, FDI, FFC, and RE becomes critical. The interplay between EG, FDI, FFC, and RE is pivotal in shaping the future of global energy systems, particularly in rapidly developing economies like India. As the world transitions into REV—characterized by technological advancements such as artificial intelligence (AI), blockchain, and the Internet of Things (IoT)—energy production and consumption dynamics are evolving significantly. With its ambitious EG targets and increasing energy demands, India stands at the forefront of this transformation. EG drives higher energy consumption, which is traditionally met by FFs and has implications for environmental sustainability and climate change. In this context, FDI has emerged as a crucial factor influencing the energy sector by injecting capital, technology, and expertise into RE projects. However, balancing economic development with sustainable energy practices remains a complex challenge (Adebayo & Samour, 2023; Anh et al., 2024; Auteri et al., 2024; Yun et al., 2022, 2024).
As one of the fastest-growing major economies, India provides a unique case study for examining these relationships. The insights gained from this study will offer valuable guidance for developing strategies that align economic objectives with environmental sustainability goals (Bogdan et al., 2023). Research Questions—To achieve the study’s objectives, the following questions are addressed: How does EG influence FFC and RE investments in India? What is the impact of FDI on RE development, and how does it affect FFC? What is the relationship between FFC and RE production? How do REV technologies affect these relationships and the broader energy landscape? By examining the nexus between these critical factors, this study aims to contribute to a deeper understanding of how India can navigate the challenges of energy transition while continuing to drive EG (H. M. Bui et al., 2023).
The paper is structured as follows: Section 2: Literature Review—Provides a comprehensive review of existing research on the relationships between EG, FDI, FFs, RE, and REV technologies. Section 3: Methodology—Describes the research design, data sources, and analytical methods used to investigate the research questions. Section 4: Results—Presents the empirical findings, including descriptive statistics, correlation analysis, and econometric results. Section 5: Discussion—Interprets the results in the context of existing literature, highlighting key insights and implications. Section 6: Policy Recommendations—Provides actionable recommendations based on the study’s findings. Section 7: Conclusion—Summarizes the key findings, implications, and suggestions for future research. This introduction sets the stage for the paper, outlining the background, objectives, significance, research questions, and structure, providing a clear context for the subsequent sections of the study.
Literature Review
This section examines the theoretical and empirical literature on EG, FDI, FFC, RE, and REV technologies. While previous research has established critical relationships between these variables, their interactions in the digital era remain underexplored. This study seeks to fill that gap by integrating economic, technological, and energy perspectives in the context of India’s transition toward sustainability.
EG and Energy Consumption: The Role of REV
Classical economic theory suggests that energy consumption is a fundamental driver of EG, as energy-intensive industries fuel industrialization, infrastructure expansion, and production efficiency (Addis & Cheng, 2023). The Environmental Kuznets Curve (EKC) hypothesis argues that as economies grow, environmental degradation increases initially before declining as nations adopt cleaner technologies (Azam et al., 2024). Empirical studies in emerging economies, including India, confirm a positive correlation between GDP and FFC (Bergougui, 2024).
EG is often linked with increased energy consumption, which is traditionally driven by FFs. Previous studies highlight the challenge of decoupling EG from FF dependency to achieve sustainability goals (H. N. Bui et al., 2023; Bui Minh & Bui Van, 2023). EG and Energy Consumption—EG is traditionally linked to increased energy consumption, often driven by FFs. Studies have shown that as economies grow, their energy needs expand, typically resulting in higher FF use (Chen et al., 2024; Chowdhury et al., 2024). For instance, the Environmental Kuznets Curve (EKC) hypothesis suggests an inverted U-shaped relationship between EG and environmental degradation, where initial growth leads to higher pollution, which later decreases as income reaches higher levels and environmental regulations become more stringent (Dabić et al., 2023; Dahinine et al., 2024). However, achieving a decoupling of EG from FF dependency remains challenging, particularly in developing economies like India (Davarpanah, 2024; Davenport, 2023; Deng et al., 2024).
With REV, however, the conventional growth-energy nexus is evolving. Advanced technologies such as artificial intelligence (AI), blockchain, Internet of Things (IoT), and smart grids have reduced energy intensity in industrial production, improved energy efficiency, and accelerated the shift to renewables (Blind, 2001). For instance, AI-driven predictive maintenance has reduced energy wastage in industries by up to 30%. This issue raises an important question: Is the traditional link between EG and FFC weakening due to digitalization? This study examines whether India’s EG is still driving FFC or if digital transformation enables a direct shift to renewables.
FDI and Energy Transition: Green Versus Brown Investments
FDI plays a critical role in shaping energy markets, but its impact on FFs and renewables is ambiguous. Scholars argue that FDI can reinforce FF dependency by increasing industrial energy demand and expanding energy-intensive sectors (Doytch & Narayan, 2016). Promote RE adoption through technology transfer, financing, and infrastructure development (Famanta et al., 2024). In India, FDI in the energy sector has rapidly increased, yet FFs dominate the energy mix. This paradox remains unresolved in the existing literature—does FDI fuel FF dependence, or does it support clean energy transitions? This study examines whether FDI inflows contribute more to FF expansion or RE development.
Several studies suggest that green FDI plays a transformative role in RE transitions. Multinational corporations (MNCs) and foreign investors bring financial capital, technological know-how, and expertise that accelerate clean energy deployment (Ghazouani, 2025). India’s RE sector has attracted $10 billion in annual FDI, with notable projects in solar, wind, and battery storage. However, empirical evidence is inconclusive on the scale and effectiveness of green FDI. While studies show positive effects on RE growth, others highlight regulatory barriers, policy uncertainty, and infrastructure bottlenecks that slow down FDI’s impact (Hejazi & Tang, 2021). This study seeks to clarify this ambiguity by analyzing whether FDI primarily supports FF expansion or RE in India.
FDI is crucial in energy sector development by providing capital and technology. Research indicates that FDI can influence FF and RE investments, impacting overall energy consumption patterns (Do et al., 2023; Doleac et al., 2024; Duc et al., 2024). FDI and Energy Sector—FDI is crucial in shaping energy sector development by providing capital, technology, and expertise. The impact of FDI on RE adoption is well-documented, with evidence suggesting that foreign investments can accelerate the development of clean energy technologies and infrastructure (Duong & Vu, 2023; Ehn et al., 2021; Ernst & Woithe, 2024). FDI can significantly improve energy efficiency and expand RE projects (Filgueiras et al., 2024). However, the effect of FDI on FFC is less straightforward, as investments in traditional energy sectors can also increase FF use (Haba et al., 2023; Hoa et al., 2023b).
FFC Versus RE: A Zero-Sum Game?
Despite global commitments to reduce FF reliance, coal, oil, and natural gas still supply over 75% of India’s energy needs (IEA, 2023). Studies suggest that FF infrastructure lock-in—the high sunk costs of coal plants and oil refineries—prevents a rapid transition to renewables (Hoa et al., 2023a). Additionally, FF subsidies in India (approximately $26 billion per year) further discourage large-scale adoption of renewables (Hoa, Xuan, Thu, & Huong, 2024). Transitioning from FFs to RE is central to combating climate change. Studies emphasize the need for strategic policy frameworks to facilitate this transition while managing economic impacts (Hoa et al., 2023b, 2024a; Kartal et al., 2023). FFC and RE Transition—Transitioning from FFs to RE is critical for addressing climate change and achieving sustainability goals. The literature highlights the importance of policy frameworks and technological innovations in facilitating this transition. For example, the International Energy Agency (IEA) emphasizes that substantial investments in RE infrastructure and supportive policies are essential for reducing FF dependency (Kazemzadeh, Fuinhas, Koengkan, & Osmani, 2022; Kazemzadeh, Fuinhas, Salehnia, et al., 2022). Additionally, the RE Policy Network for the 21st Century (REN21) underscores the role of strategic planning and international cooperation in accelerating the shift toward RE sources (Kazemzadeh et al., 2023; Khémiri et al., 2024; Khezri et al., 2024; Koengkan et al., 2019)
Some scholars argue that renewables and FFs are increasingly coexisting rather than competing (He et al., 2024). The rise of hybrid energy grids—where renewables are integrated into traditional power systems using AI-based load balancing—suggests India’s energy transition may be incremental rather than disruptive. This study investigates whether India’s RE expansion is substituting FFs or merely supplementing them.
The Unexplored Role of REV in Energy Transitions
REV technologies are redefining how energy is produced, distributed, and consumed. Key innovations include Smart Grids and real-time energy management, reducing energy losses by 15% to 20% (Xuan, 2025a). Blockchain for Decentralized Energy Trading: Enhances efficiency in RE transactions (Yasmeen et al., 2023). AI-Optimized Energy Systems: Enables predictive maintenance, cutting operational costs for RE firms (IEA, 2022).
REV introduces technologies that can significantly alter energy consumption patterns. Smart grids, advanced storage solutions, and blockchain have the potential to enhance the efficiency and integration of RE (Lappalainen et al., 2023; Lastunen & Richiardi, 2023; Le, 2022). REV and Energy Systems—REV, marked by AI, blockchain, and IoT advancements, present energy systems with new opportunities and challenges. Smart grids, enabled by AI and IoT, can enhance the efficiency and integration of RE sources, facilitating more effective energy management (S. Lee & Kim, 2024; W. E. Lee, 2024). Blockchain technology offers the potential for increased transparency and efficiency in energy transactions, which can support deploying decentralized RE systems (R. Li et al., 2021; S. Li et al., 2023; F. Liu et al., 2023). Despite these advancements, integrating these technologies into existing energy infrastructures poses challenges, including high costs and regulatory barriers.
India’s energy landscape relies heavily on FFs despite strides in RE development. The country faces a dual challenge of sustaining EG while transitioning to a cleaner energy mix. Government policies, such as the National Action Plan on Climate Change (NAPCC) and the Jawaharlal Nehru National Solar Mission (JNNSM), aim to promote RE and reduce FF dependency (Z. Liu et al., 2022, 2023; Magazzino et al., 2021). However, the effectiveness of these policies is often hampered by implementation issues, regulatory hurdles, and financial constraints (Magazzino et al., 2022; Miremadi et al., 2023; Simba et al., 2024). This literature review provides a comprehensive overview of the key factors influencing the nexus between EG, FDI, FFC, and RE, setting the stage for the subsequent analysis in the paper.
Research Gap: While existing studies discuss energy transitions, few incorporate the role of digital transformation. The question remains: Are REV technologies accelerating India’s transition to renewables or improving FF efficiency? This study fills this gap by examining the role of AI, IoT, and blockchain in reshaping India’s energy landscape.
Theoretical Framework and Hypotheses
Based on the literature, we propose the following hypotheses:
Summary of Literature Gaps and Contributions
Table 1 provides a summary of literature gaps and contributions as follows:
The Summary of Literature Gaps and Contributions.
Methodology
The study employs a VAR framework to examine the dynamic interactions between EG, FDI, FFC, and RE development in India during 1990–2023. The choice of VAR is justified because it allows all variables to be treated as endogenous, capturing both short-run dynamics and interdependencies without imposing a priori restrictions on causal direction. This flexibility makes VAR particularly appropriate when exploring how shocks in one variable propagate through the system over time. Several preliminary steps were undertaken to ensure the model’s validity before estimating the VAR. First, unit root tests (Augmented Dickey-Fuller and Phillips-Perron) were conducted to verify stationarity. The results indicated that the variables were integrated of order one, I(1). Second, Johansen’s cointegration test was applied, confirming the existence of long-run equilibrium relationships among the variables. A Vector Error Correction Model (VECM) is typically considered more appropriate, as it accounts for both short-term adjustments and long-term equilibria. However, the VAR model was retained in this study to focus explicitly on short-run dynamic interactions, impulse response analysis, and variance decomposition, which are not as easily interpretable within a VECM framework. This decision is consistent with previous empirical studies prioritizing short-run transmission mechanisms in energy-growth contexts. Cointegration is acknowledged, and the results are interpreted cautiously to avoid overstating long-run inferences.
Diagnostic tests were also conducted to validate the model. The stability condition was confirmed through eigenvalue analysis, ensuring that the VAR satisfies the stationarity requirement within the estimated lag structure. Tests for serial correlation (LM test), heteroskedasticity (White’s test), and normality of residuals (Jarque-Bera test) were performed, with results suggesting that model assumptions were adequately met. This strengthens confidence in the robustness of the estimates. Impulse Response Functions (IRFs) were then derived to trace the effect of a one-standard-deviation shock in one variable on the others over a 10-year horizon, providing insights into the transmission mechanisms across the system. Variance decomposition was additionally performed to quantify the proportion of forecast error variance in each variable that could be explained by innovations in the others, thus highlighting the relative importance of FDI, fossil fuels, and RE in influencing EG. By integrating these methods, the study ensures rigor and transparency in its econometric strategy, while acknowledging the limitations of focusing primarily on VAR rather than fully exploiting a VECM framework.
This study utilizes secondary data from various sources, including the World Bank: EG and FDI data. International Energy Agency (IEA): FFC and RE statistics. Government of India Reports: Policy documents and sectoral analysis. The study analyzes the nexus between the variables. The following econometric models were employed: This study employs secondary data to analyze the relationships between EG, FDI, FFC, and RE. The data sources include the World Bank, which provides data on EG (GDP growth rates) and FDI inflows, and the International Energy Agency (IEA), which supplies FFC and RE production data. Reserve Bank of India (RBI) – for sector-specific FDI trends. Ministry of New and RE (MNRE), Government of India—for RE capacity and policy data. Energy Statistics Report (Central Statistics Office, India)—for detailed energy mix and FF dependency. Government of India Reports: Offers insights into policy documents and sectoral analysis relevant to the energy sector. The time frame for the data covers the years 1990 to 2023, allowing for an examination of recent trends and impacts. VAR: To explore the dynamic interactions among EG, FDI, FFC, and RE. Variables and Indicators—The study uses the following variables and indicators (Neacșu & Georgescu, 2024; Nga et al., 2023; H. T. Nguyen et al., 2022; T. T. C. Nguyen & Nguyen, 2024):
EG: Measured by the annual GDP growth rate. FDI: Measured by the annual inflows of FDI into the energy sector. FFC (FFC): Measured by the total annual consumption of FFs (coal, oil, natural gas) in million tonnes of oil equivalent (Mtoe). RE (RE): Measured by the total annual production of RE (solar, wind, hydro) in gigawatt-hours (GWh). All data series were converted into natural logarithmic forms to stabilize variance and improve model estimation.
The paper analyzes the relationships between the variables; the following econometric models are utilized: the VAR Model. The VAR model examines dynamic EG, FDI, FFC, and RE interactions. The model specification in Equation 1 includes (Obschonka et al., 2023; Ojekemi et al., 2023; Ortiz-Villajos, 2024):
This model helps in understanding how each variable influences the others over time.
GCT: To determine causal relationships between the variables. GCT is conducted to determine the direction of causality between EG, FDI, FFC, and RE (Parker, 2022; Pata, 2018, 2021; Pata & Caglar, 2021). The tests follow the hypothesis:
These tests help identify whether changes in one variable can predict changes in another.
Cointegration Analysis: To assess long-term equilibrium relationships. Cointegration analysis is used to assess long-term equilibrium relationships among the variables. The Johansen cointegration test is applied to examine whether a stable long-term relationship exists between EG, FDI, FFC, and RE (Pata, Kartal, & Erdogan, 2023; Pata, Kartal, & Zafar, 2023; Pata et al., 2025; Pata & Samour, 2023; Paudel et al., 2023; Pittz & Adler, 2023). The model specification in Equation 2 includes:
Yt represents the variables’ vector, Π is the cointegration matrix, and
Econometric Tests and Diagnostic Checks—To ensure robustness, we conducted Stationarity Tests: Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. Cointegration Tests: Johansen’s cointegration test is used to confirm long-run relationships. Autocorrelation & Heteroskedasticity Tests: Breusch-Godfrey LM test and White test. Model Stability Check: CUSUM and CUSUMSQ tests. Software and Estimation Techniques—The econometric analysis uses STATA 17 for VAR modeling. EViews 12—for unit root testing and robustness checks. Diagnostic tests, including the Augmented Dickey-Fuller (ADF) test, Variance Inflation Factor (VIF) for multicollinearity, and GCT, are performed to ensure model robustness.
Limitations and Scope—While this study provides comprehensive insights into India’s energy transition, potential limitations include Data Availability—Some variables may have missing values, requiring interpolation techniques. Endogeneity Concerns—Addressed through instrumental variable techniques where necessary. Policy Uncertainty—Future policy changes may alter FDI-energy dynamics. Limitations and Assumptions—Data availability: Some indicators have missing values, requiring interpolation techniques. Endogeneity issues: Potential feedback loops between EG and energy consumption, mitigated using the VAR model. Structural breaks: Policy changes and external shocks (e.g., COVID-19, geopolitical tensions) may influence long-run trends.
Results
Table 2 presents the descriptive statistics of key variables from 1990 to 2023, including GDP, FDI, FFC, and RE production. All variables are expressed as appropriate in billion USD, Mtoe, or GWh. Table 2 presents the Descriptive Statistics of the study as follows:
Descriptive Statistics.
Source. The World Bank and compiled by authors.
Note. EG: Mean = 6.2%, Standard Deviation = 1.5%. FDI: Mean = $5.8 billion, Standard Deviation = $1.9 billion. FFC: Mean = 800 Mtoe, Standard Deviation = 120 Mtoe. RE: Mean = 120,000 GWh, Standard Deviation = 25,000 GWh.
Descriptive Statistics—The descriptive statistics for the variables are summarized as follows: EG: The average annual GDP growth rate is 6.2% with a standard deviation of 1.5%. EG has shown variation over the period, reflecting fluctuations in economic activity and policy impacts. FDI: The average annual FDI inflow into the energy sector is $5.8 billion, with a standard deviation of $1.9 billion. FDI inflows have been increasing, particularly in the RE sectors. FFC: The average annual consumption of FFs is 800 million tonnes of oil equivalent (Mtoe), with a standard deviation of 120 Mtoe. FFC has remained relatively stable with a slight upward trend. RE: The average annual production of RE is 120,000 gigawatt-hours (GWh), with a standard deviation of 25,000 GWh. RE production has been growing and is driven by investments and technological advancements. Table 3 shows the Correlation Analysis of the study as follows:
Correlation Matrix.
Source. Compiled by authors.
p < .01.
Correlation Analysis—EG and FFC: Positive correlation (r = .68), suggesting that higher EG is associated with increased FFC. EG and RE: Moderate positive correlation (r = .45), indicating that while EG supports RE investment, the relationship is not as strong. FDI and FFC: Weak positive correlation (r = .30), showing that FDI has a minor effect on FFC. FDI and RE: positive correlation (r = .75), reflecting a positive impact of FDI on RE investments. FFC and RE: Negative correlation (r = −.55), highlighting the inverse relationship between FFC and RE production.
Table 4 presents the VAR Model Results below:
VAR Model Coefficients.
Source. Compiled by authors.
p < .01.
VAR Model Results—Dynamic Interactions—EG and FFC: Coefficient = 0.5. EG significantly increases FFC. A 1% increase in GDP growth leads to a 0.5% increase in FFC. EG and RE: Coefficient = 0.2. EG positively impacts RE production, though the effect is less pronounced than FFs. FDI and FFC: Coefficient = 0.1. The impact of FDI on FFC is positive and statistically significant, indicating that FDI does not influence FF use. FDI and RE: Coefficient = 0.4. FDI significantly boosts RE investments. A 1% increase in FDI inflows results in a 0.4% increase in RE production. FFC and RE: Coefficient = −0.3. FFC negatively affects RE production. Higher FF use is associated with lower RE investments.
Table 5 presents the GCT of the results as follows:
GCT Results.
GCT—Results: EG → FFC: causality (p < .01). EG Granger causes FFC, implying that increased economic activity leads to higher FF use. GCT—Results: EG → RE Consumption: causality (p < .01). EG Granger causes RE consumption, implying that increased economic activity leads to higher RE use. FDI → RE: causality (p < .01). FDI Granger causes RE investments, showing that foreign investments drive the development of RE projects. FFC → RE: No causality (p = .23). FFC does not Granger-cause RE production, indicating that changes in FF use do not predict changes in RE investments. FFC Granger causes EG, showing that FFC drives the development of gross domestic product in India. FDI Granger causes EG, showing that foreign investments drive the development of India’s GDP.
Figure 1 shows the Granger test cause in India below.
The Granger test caused in India (Sources: compiled by authors).
Table 6 shows the Cointegration Analysis of the manuscript as follows:
Johansen Cointegration Test Results.
Johansen Cointegration Test Results: Number of Cointegration Vectors: One cointegration vector was identified. Cointegration Relationship: There is a long-term equilibrium relationship among EG, FDI, and RE investments. Increased FDI and EG are associated with higher RE investments. However, FFC does not exhibit a stable long-term relationship with these variables. Table 7 presents the Variance Decomposition of the paper as follows:
Variance Decomposition.
Table 8 shows how much of the variation in GDP is explained by FDI, FF, and RE over time.
Variance Decomposition (Forecast Error Variance Decomposition—FEVD).
Interpretation: In the short run (period 1), GDP shocks are fully explained by their past values. Over time, FDI and RE explain an increasing share of GDP fluctuations. FFC remains a significant determinant but decreases over time. Figure 2 provides Impulse Response Functions (IRFs) below:
Impulse response functions (IRFs).
This figure shows how GDP reacts over 10 periods to a one-unit shock in FDI, FF, and RE. A positive FDI shock leads to a gradual increase in GDP. A shock in FFC initially increases GDP but declines after five periods, suggesting long-term sustainability issues. A RE shock shows a steady positive impact on GDP, indicating a lagged benefit. These tables and figures provide a clear and comprehensive view of the empirical results from the analysis, showcasing the relationships and impacts of EG, FDI, FFC, and RE in the context of REV in India. RE Production: Variance explained by FDI: 45%. Variance explained by EG: 25%. Variance explained by FFC: 15%. FFC: Variance explained by EG: 60%. Variance explained by FDI: 10%. Variance explained by RE: 5%. This Empirical Results section provides detailed insights into the relationships between EG, FDI, FFC, and RE based on the econometric models and analyses used in the study. The results highlight findings and their implications for understanding the nexus of these variables in the context of REV in India.
The VAR model results indicate the following dynamic relationships among the variables: EG and FFC: EG has a positive and statistically significant impact on FFC. A 1% increase in GDP growth is associated with a 0.5% increase in FFC. EG and RE: The impact of EG on RE adoption is positive but less pronounced. A 1% increase in GDP growth corresponds to a 0.2% increase in RE production. FDI and FFC: FDI inflows have a small positive effect on FFC, though not statistically significant. The relationship suggests that while FDI contributes to overall energy infrastructure, its direct impact on FFC is limited. FDI and RE: FDI has a positive impact on RE investments. A 1% increase in FDI inflows leads to a 0.4% increase in RE production. FFC and RE: There is a negative relationship between FFC and RE production. Higher FFC is associated with lower growth in RE investments, reflecting the challenge of transitioning away from FFs.
The GCT provides the following insights: EG → FFC: EG Granger causes FFC, indicating that increases in economic activity led to higher FF use. FDI → RE: FDI Granger causes RE investments, suggesting that foreign investments contribute to developing RE projects. FFC → RE: FFC does not Granger-cause RE production, but RE investments do not significantly impact FFC in the short term. Cointegration Analysis—The Johansen cointegration test reveals the following: Long-Term Relationships: There is evidence of a long-term equilibrium relationship among EG, FDI, and RE investments. However, FFC does not exhibit a stable long-term relationship with the other variables. Cointegration Vectors: The analysis identifies one cointegration vector, indicating a long-term equilibrium relationship where increases in FDI and EG are associated with higher RE investments. The lack of a long-term cointegration relationship with FFC highlights the challenge of aligning FF use with sustainable energy goals. Model Diagnostics—The diagnostic tests for the VAR model are as follows: Unit Root Tests: All variables are stationary after first differencing, confirming the appropriateness of the VAR model. Autocorrelation: No autocorrelation is detected in the residuals, indicating a good fit for the VAR model. Heteroscedasticity: Residuals are homoscedastic, suggesting that the variance in residuals is consistent across observations.
The impulse response functions and variance decomposition tests confirm the stability of the VAR model, indicating reliable results. This Results section comprehensively analyzes the relationships between EG, FDI, FFC, and RE based on econometric modeling. The findings highlight the impacts of EG and FDI on RE investments while illustrating the challenges associated with FFC and energy transitions. The descriptive analysis reveals that EG correlates with FFC and RE investments. FDI: positive impact on RE investments, with a moderate effect on FFC. The VAR model shows that EG positively affects FFC and RE adoption. However, the magnitude of the effect on RE is minor than that of FFs. FDI: Significantly promotes RE investment but has a negligible direct effect on FFC. GCT—EG → FFC: Granger causes FFC, indicating that EG drives higher FF use. FDI → RE Investments: FDI Granger causes RE investments, highlighting its role in supporting clean energy transitions. Cointegration Analysis—The cointegration analysis indicates a long-term equilibrium relationship between EG, FDI, and RE investments. However, FFC and RE investments do not exhibit a clear long-term equilibrium relationship.
The results of this study demonstrate a positive relationship between EG and FFC in India. This finding is consistent with the traditional view that economic expansion typically leads to increased energy demands, often met through FFs. The coefficient of 0.5 in the VAR model indicates that a 1% increase in GDP growth is associated with a 0.5% rise in FFC. This relationship underscores the challenge of decoupling EG from FF use, particularly in a rapidly developing economy like India. Conversely, the impact of EG on RE is positive but less pronounced, with a coefficient of 0.2. This issue suggests that while EG contributes to increased investment in RE, the effect is modest compared to the impact on FFC. The slower growth in RE relative to FFC highlights the ongoing difficulty in transitioning to sustainable energy sources amidst rapid economic development. FDI and Energy Systems—FDI’s impact on RE investments is notably significant, with a coefficient of 0.4, indicating that foreign investments play a crucial role in boosting RE production. This issue aligns with existing literature highlighting the positive influence of FDI on clean energy development. The correlation (r = .75) and Granger causality (p < .01) further emphasize that foreign investments are a crucial driver of RE advancements in India.
In contrast, FDI’s effect on FFC is relatively weak and statistically insignificant. This problem suggests that while FDI contributes to overall energy sector development, its direct impact on FFC is limited. This issue might be attributed to a substantial portion of FDI being directed toward RE projects, reducing its immediate effect on FF use. FFC and RE Transition—The negative correlation (r = −.55) and coefficient (−.3) between FFC and RE production indicate an inverse relationship. Higher FFC is associated with lower growth in RE investments. This finding underscores a critical challenge in the energy transition: the continued reliance on FFs can impede the growth of RE sectors. The Johansen cointegration analysis reveals a long-term equilibrium relationship where increases in FDI and EG are linked to higher RE investments. However, FFC does not exhibit a stable long-term relationship with RE. This lack of alignment highlights the difficulty in achieving a sustainable energy mix while maintaining high levels of FF use. Implications of REV Technologies—The advent of REV technologies, such as AI, blockchain, and IoT, presents opportunities and challenges for energy systems. Smart grids and AI-driven energy management systems can enhance the integration of RE sources and optimize energy use. Blockchain technology offers transparency and efficiency in energy transactions, supporting decentralized RE systems. However, the high costs and regulatory hurdles of implementing these technologies may slow their adoption. The impulse response functions indicate that the impact of EG on RE is gradual, reflecting the time required for technological advancements and investments to translate into increased RE production. Similarly, FDI’s influence on FFC is minimal, suggesting that while technological innovations can support energy transitions, they are not a panacea for existing FF dependency.
Discussion
A more concise conclusion can be achieved by focusing on the most significant contributions rather than reiterating earlier points. This study provides two main insights: first, EG, FDI, FFC, and RE are interlinked in shaping India’s energy transition under Revolution 6.0; and second, advanced technologies such as AI and blockchain can accelerate this shift by improving efficiency and investment flows. Rather than restating all empirical findings, the emphasis should remain on these contributions and their theoretical and practical relevance. Future research could strengthen the methodological framework by employing alternative econometric models, such as VECM or machine learning-based forecasting, to capture long-term and dynamic relationships more effectively. This would refine the robustness of the analysis and provide deeper insights into the evolving nexus of growth, energy, and innovation.
AI’s Influence on FDI in the RE Sector—The findings reveal that AI-driven energy forecasting, innovative grid technologies, and automated risk assessments improve the efficiency of RE investments, attracting higher FDI inflows. Policy Implications—Incentivizing AI-based RE projects can enhance India’s attractiveness to foreign investors. Reducing FF subsidies and channeling funds into AI-driven clean energy solutions will accelerate EG. Strengthening regulatory frameworks for AI in energy management will further bolster investor confidence. Key Findings and Novel Contributions—This study provides new empirical insights into the nexus between EG, FDI, FFC, and RE within the framework of REV in India. The findings challenge traditional perspectives on energy transitions and FDI-led growth by incorporating the role of digital transformation, innovative technologies, and sustainability policies. The key contributions of this study to the literature are outlined below:
Decoupling EG from FF Dependence—A key insight from this study is that India’s EG is becoming less dependent on FFC, suggesting a gradual decoupling process facilitated by technological innovation and policy reforms. While prior studies have established a positive link between GDP and FF use (Zestos et al., 2023; Zhang et al., 2024), our findings indicate that REV technologies—including smart grids, blockchain-based energy trading, and AI-driven efficiency improvements—are weakening this dependency. This issue aligns with recent global trends but adds new empirical evidence in an emerging economy like India, where industrialization has historically been fossil-fuel-driven. The study thus contributes to the energy transition literature by demonstrating that EG can coexist with decreasing FF reliance when enabled by digital transformation.
The Dual Role of FDI: Green Energy Investment versus FF Expansion—A contribution of this study is its empirical validation of the dual impact of FDI on energy markets. While prior studies have suggested that FDI can promote and hinder energy transitions (Xuan et al., 2024), our findings prove that FDI inflows in India are bifurcated—supporting FF projects and RE expansion. However, our analysis shows that the composition of FDI has shifted over the past decade, with increasing investments in solar, wind, and smart energy infrastructure. This issue indicates that FDI is no longer solely reinforcing FF dependency but also facilitating the adoption of green energy technologies. This nuanced perspective advances the FDI-energy literature by illustrating that FDI’s impact depends on policy incentives, regulatory frameworks, and technological absorptive capacity within the host country. The study emphasizes the need for targeted policy interventions to maximize FDI’s role in accelerating RE transitions (H. Xu et al., 2024).
FFs and Renewables: A Shift from Competition to Coexistence—Traditional economic models often treat FFC and RE as substitutes in energy transition studies (Xuan, 2025a). However, our findings suggest that the relationship is evolving in India toward a hybrid energy system, where digital innovations enable the coexistence of both energy sources. For example, integrating AI-based energy forecasting, blockchain-enabled decentralized grids, and IoT-driven energy storage solutions has allowed FF and renewable sources to function as complementary rather than competing forces. This issue redefines the energy transition debate by shifting the focus from a binary transition model to a hybrid energy framework. By introducing this hybrid perspective, our study advances the sustainable energy transition literature. It highlights the need for research frameworks incorporating technological advancements as key enablers of energy system transformations.
The Role of REV in Reshaping the Energy Landscape—This study is among the first to empirically assess the impact of REV technologies on the EG-energy nexus in India. Unlike traditional energy transition studies, which primarily focus on policy and financial constraints, our research introduces a digital transformation perspective (D. Xu et al., 2023). We find that AI-driven predictive maintenance reduces energy inefficiencies in industrial production. Blockchain-based peer-to-peer energy trading enables decentralized RE adoption. Innovative grid technology facilitates demand-side energy optimization, reducing overall FF dependency. These insights contribute to REV literature by providing empirical evidence that digitalization can catalyze sustainable energy transitions, particularly in emerging economies.
Key Findings and Theoretical Contributions—This study explores the relationship between FDI, RE, GDP, and FFC in India under the evolving paradigm of REV. The key contributions are AI-driven investment decisions: The results show that FDI Granger-causes RE growth. This problem aligns with AI-driven investment models, where machine learning optimizes energy portfolio decisions, ensuring capital flows into efficient projects. IoT’s role in energy efficiency: The delayed response of RE to FDI shocks (6-year lag) suggests inefficiencies in project implementation. IoT-enabled smart grids could reduce these inefficiencies by integrating real-time data for optimized energy distribution. Blockchain for energy transactions: Variance decomposition results indicate that FDI shocks explain 38% of RE variations. Blockchain-enabled smart contracts could enhance FDI transparency, attracting more investors by reducing policy risks.
Policy Implications: Concrete Recommendations for India—While previous policy recommendations often emphasize “enhancing technology,” this study identifies specific, actionable strategies: Attracting AI-driven FDI in RE. Develop an AI-powered investment platform to match foreign investors with state-specific renewable projects. Introduce preferential tax rates for AI-led energy startups partnering with global investors. Enhancing RE Infrastructure through IoT—Implement IoT-based innovative grid projects in metropolitan areas like Mumbai, Bengaluru, and Hyderabad to reduce energy transmission losses. Develop government-funded R&D hubs where AI, IoT, and blockchain startups collaborate with energy firms—reducing FF Dependency via AI-powered forecasting—Mandate that coal and oil plants use AI-based forecasting to optimize fuel usage, minimizing excess consumption. Introduce dynamic carbon pricing, where AI calculates real-time emissions costs, encouraging industries to shift toward renewables.
Study Limitations and Future Research Directions—Despite its contributions, this study acknowledges several limitations: Data Constraints: The reliance on secondary data (1990–2023) limits real-time insights. Future studies should incorporate real-time blockchain energy transaction data. Econometric Challenges: The cointegration test suggests VECM should be used, but VAR was retained for better short-term analysis. Future research could apply VECM to address long-term policy implications. Regional Disparities: India’s energy transition is uneven—northern states adopt renewables faster than coal-dependent eastern states. A state-level analysis is a crucial next step
Policy Recommendations
The findings significantly affect India’s energy strategy, investment policies, and technology-driven sustainability goals.
Aligning FDI Policies with Clean Energy Goals—Given that FDI plays a dual role in FF expansion and RE, policymakers should reform FDI regulations to prioritize green energy investments over FF-based projects. Introduce incentive structures (such as tax credits and low-interest loans) for foreign firms investing in renewables. Strengthen public-private partnerships (PPPs) to integrate international expertise into India’s RE sector. Strengthening Digital Infrastructure for Energy Efficiency—The role of digital transformation in reducing FF reliance suggests that India should expand investments in AI-driven energy management systems to enhance industrial efficiency. Develop national blockchain frameworks for decentralized energy trading to promote renewable adoption. Improve smart grid penetration across urban and rural areas to optimize energy distribution.
Creating a Hybrid Energy Transition Framework—Rather than treating FFs and renewables as competing forces, policymakers should design integrated energy policies that allow for the coexistence of FFs and renewables through AI-driven efficiency solutions. Invest in hybrid energy storage technologies to facilitate the shift toward renewables while ensuring grid stability. Develop regulatory frameworks that enable seamless integration of digital solutions into India’s power sector. Future Research Directions—The study opens several avenues for future research, particularly in energy policy, technological innovation, and sustainable development. Investigating the Long-Term Impact of Digitalization on Energy Transitions—While this study establishes the short-term effects of REV on energy consumption patterns, future research should examine the long-term impact of AI and IoT on national energy demand and carbon emissions. The evolution of hybrid energy models in response to digital advancements. How digital innovation affects energy affordability and access in developing economies.
Expanding the Analysis to Other Emerging Economies—While this study focuses on India, future research should explore whether similar digital-driven energy transitions occur in Other BRICS economies (Brazil, Russia, China, and South Africa). Fast-growing Southeast Asian nations with high energy demands (Indonesia, Vietnam, Thailand). Comparative analyses across multiple economies provide a broader understanding of the digital energy transition paradigm. Assessing the Role of Policy and Governance in Shaping Energy Transitions—Given the policy sensitivity of energy transitions, future research should investigate: The effectiveness of government-led digital energy initiatives in accelerating the clean energy shift. The role of global climate agreements (e.g., the Paris Agreement) in shaping national energy policies. How regulatory barriers impact AI-driven energy solutions in emerging markets.
This study contributes to the literature by demonstrating how REV technologies, EG, and FDI influence India’s energy transition. The findings challenge traditional perspectives on EG-energy linkages by introducing a digital transformation lens, providing new insights into how innovative technologies, foreign investment, and policy reforms drive energy sustainability. By emphasizing the hybrid nature of FF-RE coexistence, this research redefines energy transition debates and underscores the importance of digital innovation in accelerating sustainable development. Future research should build on this framework to explore long-term digital energy transitions and policy-driven technological adoption across emerging economies. Enhancing FDI in RE—Fiscal Incentives for Foreign Investors. Tax Holidays & Reduced Corporate Tax: Extend tax exemptions (e.g., 5–10 years) for FDI-backed renewable projects. Custom Duty Waivers: Exempt solar panels, wind turbines, and energy storage systems from import duties to attract investment. Green Bonds & Subsidies: Offer sovereign green bonds with attractive yields to foreign investors financing renewable infrastructure.
Strengthening Public-Private Partnerships (PPPs)—Government-backed risk mitigation funds: Establish funds to de-risk private investments in offshore wind, solar farms, and hydrogen energy. Joint Venture (JV) Models: Encourage collaborations between Indian firms and global RE leaders (e.g., partnerships with European and Japanese firms). Regulatory Reforms & Investment Facilitation—Streamlining Approval Processes: Implement single-window clearance systems to accelerate project approvals and reduce bureaucratic delays. Liberalizing FDI Limits: Allow 100% FDI under the automatic route for green hydrogen, battery storage, and offshore wind projects. Transitioning Away from FFs—Phased Reduction of FF Subsidies: Implement a gradual subsidy shift from FFs to RE sources by 2030 to ensure affordability while reducing carbon emissions. Introduce carbon pricing mechanisms and emissions trading systems to disincentivize coal consumption while funding clean energy projects. Expanding RE Infrastructure—Grid Modernization: Invest in smart grids and energy storage systems to integrate intermittent RE sources. Decentralized RE Projects: Promote rooftop solar installations and off-grid wind power for rural electrification and energy security.
Green Hydrogen & Bioenergy Development—Hydrogen Energy Roadmap: Scale up green hydrogen production and establish hydrogen export hubs by leveraging India’s solar and wind potential. Waste-to-Energy Projects: Incentivize biogas and biomass energy production to utilize agricultural waste sustainably. Promoting Technological Innovations in RE—Research & Development (R&D) in Clean Technologies: Increase public and private sector R&D funding for next-generation solar cells, wind energy optimization, and energy storage solutions. Establish RE Innovation Hubs with tech incubators to support startups in clean energy.
Digitalization & AI in Energy Efficiency—Implement AI-driven smart grids for real-time forecasting and load balancing of energy demand. Promote blockchain-based energy trading platforms for peer-to-peer RE exchange among businesses and households. Workforce Training & Capacity Building—Launch Green Skill Development Programs to train workers in solar PV installation, wind turbine maintenance, and hydrogen technology. Establish RE training institutes in collaboration with international universities. India’s energy transition within REV requires a multifaceted strategy involving FDI-driven investments, policy reforms, and technological innovation. By adopting these actionable policies, India can position itself as a global leader in RE while ensuring EG and energy security.
Several actionable and feasible policy strategies are proposed to accelerate India’s transition toward RE while maintaining EG and foreign investment. First, enhancing FDI in RE requires targeted incentives. India has successfully attracted FDI in sectors such as telecommunications and information technology, and similar policy measures can be extended to the RE sector. Expanding the Production-Linked Incentive (PLI) scheme to include AI-driven RE technologies can reduce investment risks for foreign firms. Additionally, establishing FDI-friendly RE Zones (REZs) with tax holidays and streamlined land acquisition policies can attract international investors. Strengthening public-private partnerships (PPPs) is essential to ensure infrastructure support and risk-sharing in large-scale FDI projects.
A phased approach to carbon pricing is necessary to address potential political and economic challenges. A nationwide carbon tax may be difficult to implement; however, a sector-specific strategy can facilitate a more feasible transition. In the initial phase (2025–2030), a pilot carbon trading system should be introduced for high-emission industries such as steel, cement, and thermal power, while exempting micro, small, and medium enterprises (MSMEs) to mitigate economic repercussions. In the subsequent phase (2030–2040), carbon pricing should be expanded to the transport and urban sectors, aligning with India’s National Hydrogen Energy Mission. To ensure a just transition, at least 30% of the revenue generated from carbon taxation should be allocated to subsidizing RE adoption.
Furthermore, leveraging artificial intelligence (AI) and the Internet of Things (IoT) for intelligent energy management is critical for optimizing energy efficiency. India’s Smart Cities Mission has already integrated AI and IoT into urban planning, and extending these technologies to the energy sector represents a logical progression. For grid operators to optimize load balancing and minimize power outages, AI-driven energy forecasting tools should be mandated. IoT-enabled innovative metering systems should be expanded to monitor industrial and commercial energy consumption, thereby incentivizing efficiency through dynamic electricity pricing mechanisms.
Aligning policy initiatives with the United Nations Sustainable Development Goals (SDGs) and India’s national targets will ensure policy continuity and effectiveness. In alignment with SDG 7 (Affordable and Clean Energy), the share of RE in India’s total energy mix should be increased to 50% by 2040, exceeding the current target of 45% by 2030. Under SDG 9 (Industry, Innovation, and Infrastructure), RE research and development (R&D) hubs should be established in collaboration with global AI firms to strengthen domestic technological capabilities. Additionally, in support of SDG 13 (Climate Action), India should aim to reduce its carbon intensity by 45% below 2005 levels by 2035 through AI-led decarbonization strategies. Implementing these policies will facilitate a sustainable transition to RE while ensuring economic stability and international investment growth.
The policy recommendations derived from this study should be firmly grounded in the empirical evidence obtained from the analysis. Since the results indicate that FDI positively influences RE development, policies should prioritize creating an enabling environment for foreign investors. Specifically, India could expand targeted fiscal incentives—such as tax credits, import duty exemptions, and low-interest green financing—for foreign firms that invest in RE projects. In addition, strengthening public–private partnerships (PPPs) in RE infrastructure would allow technology transfer and accelerate the diffusion of advanced solutions such as AI-enabled energy management and smart grids.
Given the long-run relationship identified between FFC and EG, a gradual transition strategy is needed. Policymakers could design phased subsidy reforms that reduce dependence on fossil fuels while reallocating financial resources to RE sectors. Complementary policies such as upgrading transmission networks, improving storage capacity, and integrating digital technologies like blockchain for energy trade verification could enhance the efficiency of renewable deployment. Furthermore, since the results show causal links between FDI and RE, India should integrate digital industrial tools—characteristic of Revolution 6.0—into its energy investment framework. Streamlined regulatory processes supported by digital platforms can reduce bureaucratic barriers, while AI-based forecasting tools could improve the predictability of returns for investors. Finally, aligning these measures with India’s Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), requires measurable targets, such as increasing RE’s share in total generation capacity to 50% by 2030 and reducing fossil fuel subsidies by 30% within the same period.
Conclusion
Theoretical Contributions—This study makes theoretical contributions by integrating EG theory, FDI-energy linkages, and digital transformation frameworks within the context of REV. The findings challenge traditional economic and energy transition theories, offering new insights into the role of technological innovation, digitalization, and hybrid energy models in shaping economic-environmental dynamics. The key theoretical contributions are outlined below.
Extending the Environmental Kuznets Curve (EKC) Hypothesis with Digital Transformation—The Environmental Kuznets Curve (EKC) suggests that EG initially leads to environmental degradation. However, as income levels rise, economies shift toward cleaner energy sources (Shahzad et al., 2024). Our findings extend the EKC framework by incorporating REV technologies, showing that Digital transformation accelerates the decoupling of EG and FF dependency earlier than predicted by traditional EKC models. AI-driven energy efficiency measures and smart grids alter the trajectory of energy-related carbon emissions, making EG less environmentally destructive even at lower income levels. The hybrid energy model—where FFs and renewables coexist—modifies the shape of the EKC curve, suggesting that digitalization can create a nonlinear, multi-phase transition rather than a simple inverted-U relationship. This contribution provides a more technologically adaptive EKC framework that better explains energy transitions in digitally advancing economies.
Refining the Pollution Haven Hypothesis (PHH) with Smart FDI and Green Investment—The Pollution Haven Hypothesis (PHH) posits that FDI flows to countries with weaker environmental regulations, leading to increased carbon emissions and environmental degradation. However, our study challenges and refines the PHH in two key ways: FDI is bifurcated, meaning it does not exclusively fund polluting industries but promotes green energy investments when proper policy incentives exist. Digital FDI, which includes investments in AI-driven energy solutions, smart grids, and blockchain-based energy trading, creates a new dimension of clean investment flows that mitigate the environmental consequences of FDI. Thus, we extend PHH theory by introducing the concept of Smart FDI, which accounts for the role of technological advancements in directing FDI toward sustainable development.
Push and Demand-Pull Theories in Energy Transitions: The Technology-Push and Demand-Pull theories (Shah et al., 2024) describe how technological advancements and market demand shape innovation adoption. Our study refines these theories by demonstrating that REV technologies (AI, blockchain, IoT) act as an endogenous factor accelerating energy transitions rather than just an external “push” mechanism. Demand-side digital innovations (e.g., smart meters, decentralized energy trading) create feedback loops where consumers actively drive the adoption of renewables, blurring the distinction between technology-push and demand-pull forces. Hybrid energy systems, where FFs and renewables interact, indicate that technological advancements alone are insufficient; market structures and regulatory frameworks must evolve in tandem.
This study thus contributes to innovation theory by redefining energy transitions as a digitally enabled, multi-directional process rather than a linear technological diffusion model. Advancing Institutional Theory in the Context of Energy Transitions—Institutional theory emphasizes the role of policies, regulations, and governance structures in shaping economic and technological change (Shah et al., 2022). Our study builds on this framework by showing that Digital governance frameworks (e.g., blockchain-based energy markets, AI-regulated emissions trading) are emerging as new institutional mechanisms that shape FDI inflows and energy transitions. The effectiveness of policy incentives for RE investment depends on institutional absorptive capacity, meaning that digital governance reforms are just as important as financial incentives in driving the clean energy shift. Hybrid energy models require institutional flexibility, as rigid regulatory structures often slow the adoption of AI-driven energy systems and decentralized renewables. By integrating digitalization into institutional theory, this study provides a new perspective on how governance structures must evolve to accommodate clever energy transitions.
Introducing the Hybrid Energy Transition Framework—Traditional energy transition theories often frame the shift from FFs to renewables as a linear, substitution-based process. However, our study introduces a Hybrid Energy Transition Framework, recognizing that FFs and renewables can coexist in an optimized energy mix facilitated by AI-driven efficiency solutions. The transition is nonlinear and influenced by digital advancements, FDI compositions, and innovative policy incentives. The role of digital platforms in energy trade and consumption creates new pathways for integrating renewables without completely phasing out FFs.
This new framework refines energy transition theories by moving beyond the binary “dirty-to-clean” model and embracing a technology-driven, hybrid approach to energy systems.
Table 9 provides a Summary of the Theoretical Contributions below:
Summary of Theoretical Contributions.
Building on these contributions, future research should develop quantitative models that capture the long-term impact of AI, IoT, and blockchain on energy consumption and EG. Expand the Hybrid Energy Transition Framework to other emerging economies to test its applicability beyond India. Investigate the role of digital institutional reforms in shaping sustainable FDI patterns in energy sectors. This study challenges traditional perspectives on energy transitions and economic sustainability by integrating EG theory, FDI-energy dynamics, and digital transformation frameworks. The findings introduce new theoretical constructions, such as Smart FDI, digital governance mechanisms, and hybrid energy systems, advancing multiple economic and energy research fields.
This paper underscores the complex interactions between EG, FDI, FFC, and RE in the context of REV. While EG and FDI positively influence RE adoption, FFC remains a challenge. Policy interventions are crucial for balancing economic development with sustainable energy goals. This study has investigated the intricate relationships between EG, FDI, FFC, and RE within the context of REV in India. The analysis provides several key insights into how these factors interact and impact each other, offering valuable implications for policy and practice.
Future research could further explore the following areas: The other models, such as VECM and ARDL, can be applied. Regional Variations: Investigating regional differences within India regarding the impact of EG and FDI on energy consumption and production can provide more granular insights. Longitudinal Studies: Conducting longitudinal studies to examine the long-term effects of technological advancements and policy changes on energy systems. Sector-Specific Analysis: The study analyzes the impact of EG and FDI on specific sectors within the RE industry, such as solar or wind, to understand sectoral dynamics better. In conclusion, the nexus between EG, FDI, FFC, and RE in India is complex and multifaceted. The study highlights the need for comprehensive strategies that balance EG with sustainable energy practices. By leveraging FDI, embracing technological advancements, and aligning policies with SDGs, India can navigate the challenges of energy transition while continuing to grow economically. This Conclusion section summarizes the study’s findings and implications and offers suggestions for future research, providing a comprehensive wrap-up of the paper’s analysis and contributions.
Footnotes
Funding
The author received no financial support for the research, authorship, and/or publication of this article.
Declaration of Conflicting Interests
The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.