The Impact of Environmental Governance on Energy Transitions: Evidence from a Global Perspective

Abstract

The accelerating degradation of the global environment, primarily driven by dependence on fossil fuels, has intensified the urgency for energy transitions toward renewable sources. While the literature on energy transitions is expanding, the role of environmental governance, particularly the stringency of environmental policies, remains insufficiently understood. This study addresses this gap by empirically examining how environmental policy stringency influences national energy transitions. Using a balanced panel of 29 countries over the period 2010–2024, we construct an energy transition indicator and estimate its relationship with policy stringency while controlling for macroeconomic and structural factors such as income, trade openness, and foreign direct investment. To mitigate endogeneity and cross-sectional dependence, we employ robust econometric techniques, including Instrumental Variables (IV) two-step Generalized Method of Moments (GMM) and IV two-stage least squares estimators. The results provide strong evidence that stricter environmental policies significantly accelerate the shift toward cleaner energy sources. Furthermore, the findings highlight the complementary roles of financial innovation in mobilizing green investments and economic complexity in facilitating sustainable energy adoption. These insights underscore the critical importance of stringent environmental governance in achieving global decarbonization goals and inform policymakers on the design of effective regulatory frameworks to foster energy transitions.

1. Introduction

Human activities have driven the Earth’s climate system into a state of unprecedented change. Global temperatures have already risen by about 1.1 °C since the late 19th century, and the rate of warming today is faster than at any time in the past 2000 years [1]. This warming is causing more frequent and severe extreme weather events worldwide. Increases in heatwaves, heavy rainfall, droughts, tropical cyclones, and wildfires have been documented in every region [2]. For example, recent assessments conclude that human-induced climate change “is already affecting many weather and climate extremes in every region across the globe,” leading to “widespread adverse impacts on food and water security, human health, and on economies and society” [3,4,5]. The melting of glaciers, loss of Arctic sea ice, rising sea levels, and ocean heat uptake are also all occurring at unprecedented rates [6,7]. In short, the scientific evidence is clear: we face an escalating climate crisis, with more intense and frequent droughts, floods, heatwaves, and other hazards threatening ecosystems and communities worldwide [8,9]. The energy sector is by far the most significant contributor to this crisis (Figure 1). Burning fossil fuels (coal, oil, and gas) for electricity, heat, and transportation produces the majority of greenhouse gases (GHGs). In fact, fossil fuels account for roughly three-quarters of global GHG emissions and nearly 90% of carbon dioxide emissions [10,11]. In line with this, the energy-production sector (primarily electricity and heat generation) is the single largest source of emissions worldwide. Global data show that electricity and heat production dominate the carbon footprint, followed by transport, industry, and agriculture [12]. This heavy reliance on carbon-intensive energy undermines environmental protection: it contributes not only to climate change but also to air pollution, land and water impacts, and biodiversity loss [13].

Recognizing the urgency, policymakers have repeatedly called for a rapid shift from fossil fuels to clean energy. At the COP28 climate conference [14], government leaders formally called on governments to speed up the transition away from fossil fuels to renewables such as wind and solar power [15]. For the first time in COP history, the final agreement explicitly mentioned fossil fuels, signaling the “beginning of the end” for unabated coal, oil, and gas. Alongside, the COP28 Global Renewables and Energy Efficiency Pledge was launched, committing signatories to triple renewable energy capacity to at least 11,000 GW by 2030 and double the annual rate of energy efficiency improvements [16]. The pledged goal is to move the world toward an energy system largely free of unabated fossil fuels well before the mid-century (see Global Renewables and Energy Efficiency Pledge, COP28, https://www.cop28.com/en/global-renewables-and-energy-efficiency-pledge, assessed on 1 August 2025). In other words, international consensus now emphasizes a large-scale, accelerated energy transition toward wind, solar, and other clean sources (see 5 Key Takeaways from COP28, UNFCCC, https://unfccc.int/cop28/5-key-takeaways, accessed on 1 August 2025). These steps aim to meet the Paris Agreement target of limiting warming to 1.5 °C and deliver co-benefits for sustainable development, jobs, and public health. The need for an energy transition is not only a global imperative but also aligns with economic goals. By switching to clean energy sources, countries can reduce pollution and greenhouse emissions while potentially sustaining growth. Recent analyses show that stronger climate and energy policies can improve environmental quality and yet still support economic development. For example, environmental mitigation policies have reduced global emissions by about 12% in recent years, mainly through lowering the energy intensity of economies (energy use per unit of GDP) [17]. In other words, stricter policies have spurred efficiency gains that cut emissions without necessarily sacrificing output. Achieving a clean energy transition is thus a key pathway toward more sustainable development (not just a cost). Because of this, a growing body of research has examined the factors that facilitate a country’s shift to renewables. Empirical studies highlight many drivers of energy transition: for instance, advances in information technology and the digital economy tend to raise renewable energy use. Shahbaz et al. [18] found that a 1% rise in a nation’s digital economy index leads to significant increases in both renewable energy consumption and generation, partly by strengthening governance and innovation. Similarly, strong overall economic growth and urbanization can provide the resources and infrastructure needed to build wind and solar farms, as countries invest to meet rising energy demands. Foreign direct investment (FDI) and international finance are also important: Caetano et al. [19] show that energy transition is crucial for channeling FDI into clean technologies and for green growth. The quality of institutions and regulations appears to matter as well: stable governance, clear property rights, and transparent markets encourage investors to fund renewable projects. In short, many macroeconomic and structural variables (growth, trade openness, education, inequality, governance quality, etc.) have been linked to a faster clean energy shift.

However, one particular factor has received relatively little attention: environmental governance. By this, we mean the set of government policies, regulations, and institutional frameworks aimed at protecting the environment and managing natural resources. Conceptually, strong environmental governance combines effective laws, enforcement mechanisms, and public oversight to limit pollution and ensure sustainability. For example, good governance includes enacting strict pollution standards, monitoring emissions, and penalizing violations. As Safdar et al. [20] note, “Effective environmental governance (EGR) is essential for resource management and sustainability”. In addition, strong governance frameworks help “promote responsible resource use, curb illegal activities, and mitigate pollution” [21]. In other words, environmental governance is often viewed as a necessary foundation for any green transition.

Indeed, theory and case evidence suggest that environmental policies can spur innovation and clean investment. For example, economic research by Hassan and Rousselière [22] finds that more stringent environmental policies lead to faster rates of eco-innovation (new green technologies) in OECD countries. Likewise, Hassan et al. [23] show that when governments toughen both market-based and regulatory environmental rules, this promotes renewable energy adoption. In practice, increasing the price of pollution (through taxes or cap-and-trade) raises the cost of fossil fuel generation, creating a substitution effect toward cleaner sources. Supporting this, recent policy databases analysis indicates that countries with aggressive climate policies achieved significant emission reductions—mostly via reducing energy intensity—implying that these policies help shift energy use toward greener options. Thus, credible evidence suggests a potential positive role for environmental governance in accelerating the energy transition. However, the actual impact of environmental governance on energy transition outcomes is not settled. Some analysts caution that very stringent regulations can impose short-term costs on firms and consumers. For example, Van den Bergh [24] argues that imposing too-rapid green reforms may burden businesses and households, which could slow down economic growth and resistance to transition. Others raise concerns that if one country tightens rules, polluting industries may relocate abroad, a phenomenon known as carbon leakage, so global emissions fall less than expected. Similarly, some studies suggest that environmental policy can have complex, context-dependent effects on the ecological footprint or on levels of renewable energy use, depending on local conditions, technology adoption lags, and governance capacity. In short, while environmental governance clearly has environmental benefits, there is debate about how it interacts with energy systems and economies. In particular, the specific link between environmental policy stringency and renewable energy deployment remains underexplored. As one recent survey notes, research on how environmental rules shape the energy mix is still sparse, leaving the question of whether tougher green regulations truly facilitate the shift to clean power unanswered.

To address this gap, the present study investigates the relationship between environmental governance and energy transition. Our main question is: Do more stringent environmental governance policies help the transition toward renewable and clean energy? We answer this by using multi-dimensional indices of environmental policy governance, and by examining their effect on countries’ clean energy adoption. For the empirical analysis, a balanced dataset covering 29 nations during 2010–2024 is constructed. Our key independent variable is an index of environmental policy stringency [25]. The dependent variable is energy transition, measured as the share of renewables in total energy consumption. Methodologically, we employ an Instrumental Variables System GMM estimator, which allows for dynamic panel analysis and helps to control for endogeneity. This is important, as wealthier countries often possess the capacity to implement stricter environmental policies and the resources to invest in renewable energy sources. We also check the robustness of the results using 2SLS with external instruments, Feasible Generalized Least Squares, and fixed effects with Driscoll–Kraay standard error models, to ensure our results are not sensitive to the estimation technique. Finally, we empirically check the underlying pathways through which environmental governance influences energy transition by implementing a mediation analysis using a two-stage estimation approach. Two potential mediating variables are considered in the analysis: government regulation and technological innovation. Our analysis yields a clear finding: stronger environmental governance is associated with significantly faster energy transition. That is, countries with more stringent environmental regulations tend to increase their renewable energy use and improve their energy mix more quickly. This implies that well-designed environmental policies do not necessarily impede growth but can in fact accelerate the adoption of clean energy technologies.

The present research contributes to the existing literature in several ways. First, it offers a comprehensive global analysis by including a diverse sample of developed and developing countries, thereby providing new empirical evidence on the role of environmental governance in accelerating the energy transition process. By establishing a robust empirical link between environmental governance quality and the adoption of cleaner energy sources, the study underscores that effective policy frameworks are essential levers in reducing environmental pollution and mitigating climate risks. Second, the empirical analysis primarily relies on the Instrumental Variables System GMM estimator, which enables effectively addressing potential endogeneity issues. This methodological choice is particularly important in the context of this study, as there are likely bidirectional relationships between environmental governance and energy transition. For instance, while stronger governance may drive renewable energy adoption, countries that are already transitioning to cleaner energy may be more inclined to strengthen their institutional frameworks. By controlling for these dynamics, the System GMM approach ensures more reliable and unbiased estimates, thereby strengthening the validity of the study’s conclusions. Third, the present study goes beyond examining the direct impact of environmental governance on the clean energy transition by investigating the underlying channels through which this relationship operates. Specifically, it implements a systematic mediation analysis using a two-stage estimation approach to explore the roles of government regulation and technological innovation, thereby providing a more nuanced understanding of how environmental governance influences energy transition. While previous studies have examined the relationship between environmental governance and the energy transition, our study adds value by exploring the mediating role of technological innovation and government regulation, factors that are often acknowledged but less frequently analyzed empirically. Rather than revisiting whether environmental regulation influences energy transition, this study provides new empirical insights into the pathways and mechanisms through which such an influence occurs. By doing so, the present study provides new empirical evidence on the policy–energy nexus, which has practical value for decision-makers.

The structure of this paper is as follows: Section 2 provides a review of the literature on the determinants of energy transition and the role of environmental governance. Section 3 outlines the data sources, variable definitions, and the empirical methodology, including the governance indicators and econometric techniques employed. Section 4 presents the estimation results. Finally, Section 5 concludes the study and offers policy recommendations informed by the findings.

2. Theoretical Background and Prior Empirical Studies

2.1. Theoretical Background

The deterioration of environmental conditions and rapid climate change challenges, primarily driven by high dependence on carbon-intensive energy systems, have prompted extensive scholarly investigation into the mechanisms of energy system transformation [26]. Recent research emphasized that energy system transformations serve as critical pathways for minimizing ecological footprint [27], mitigating carbon emissions, and advancing climate resilience strategies. These transformations, conceptualized as fundamental restructuring of energy infrastructure toward lower carbon content sources and enhanced efficiency measures [18], encompass coordinated technological, economic, political, institutional, and socio-cultural modifications [28]. A prominent illustration involves the systematic replacement of coal and petroleum-dependent systems with renewable and clean energy alternatives [29].

Environmental governance encompasses the comprehensive system of institutions, regulations, and decision-making processes that shape the extent to which governmental environmental reforms mitigate harmful environmental behaviors while promoting sustainable economic development and transformation in the energy mix [30]. Scholars suggest that effective environmental governance can accelerate energy system transformations through multiple channels, including explicit and implicit pricing mechanisms, fossil fuel dependence reduction strategies, and pollution control regulations [31]. These governance approaches improve conditions for deploying clean energy by setting up incentive structures that encourage investment in green technologies and sustainable energy systems.

The theoretical basis for understanding how environmental governance affects energy transitions comes from different theories, including institutional economics, Policy Feedback Theory, Environmental Governance Theory, and sustainability transition theories. Institutional economics theory highlights how formal and informal rules influence actor behavior and market outcomes, offering insights into how environmental governance systems can motivate the adoption of clean energy [32]. In other words, environmental governance operates within a larger institutional context that sets expectations for how actors, including governments and companies, react to environmental issues. By establishing regulatory frameworks, policy standards, and enforcement mechanisms, environmental governance institutions may influence the legitimacy and acceptance of clean energy solutions among the population. Policy Feedback Theory explains how governance interventions can generate self-reinforcing dynamics that accelerate energy system changes through learning effects, coalition building, and market development [33]. In other words, Policy Feedback Theory provides a dynamic view of how environmental governance and energy transitions are connected. It highlights how current environmental policies can shape future political, social, and institutional conditions. Unlike traditional views of policy as a single, one-time effort, this theory argues that environmental policies create feedback effects that impact the direction of energy systems over time. Sustainability transition literature also offers multi-level perspectives on how governance operates across different scales to influence technological and social changes in energy systems [34]. These theoretical foundations suggest that effective environmental governance requires coordination across multiple levels, sectors, and stakeholders to address the systemic nature of energy transitions. Contemporary research increasingly recognizes that energy system transformations involve complex socio-technical processes that extend beyond technological substitution to encompass broader institutional and social changes [35]. Environmental governance plays a crucial role in managing these transitions by providing coordination mechanisms, reducing uncertainties, and aligning diverse stakeholder interests around sustainable energy objectives [36]. Understanding these governance dynamics is essential for developing effective strategies to accelerate energy transitions and achieve climate objectives while maintaining economic development and social welfare outcomes.

2.2. Empirical Studies on Environmental Governance and Energy Transition

An increasing body of empirical research has examined the impacts of environmental governance and energy transition. For instance, [32] examined the impact of environmental governance on energy transition in 21 OECD countries from 1990 to 2020 and found that strict environmental policies effectively discourage the use of non-renewable energy, limit polluting industrial activities, and encourage organizations to adopt sustainable energy. Additional analysis of 32 OECD nations over three decades indicates that enhanced market-based and non-market environmental policies, combined with technical support mechanisms, positively influence renewable energy deployment. These findings highlight the critical role of environmental governance frameworks in motivating enterprises and individuals to adopt clean energy alternatives. Recently, Bakhsh et al. [37] explored the impact of environmental governance on energy transition in 20 OECD countries from 1990 to 2021 using the Method of Moments Quantile Regression. The analysis strongly supports the idea that environmental governance promotes energy transition across all quantile levels of the distribution, with greater effects observed in countries that have already made substantial progress in energy transition.

Previous empirical investigations also reveal considerable differences in governance effectiveness across different contexts and regions. Studies examining Chinese regional data demonstrate that relationships between energy transitions and environmental regulations exhibit inconsistency across geographic areas, with distinct regulatory frameworks producing varying influences on energy system transformation [38]. Research analyzing Latin American contexts indicates that environmental taxation approaches show limited significance in stimulating renewable energy investments in specific national settings [39]. Additionally, some studies highlight the pollution haven hypothesis, suggesting that disparities in environmental regulation stringency between countries may promote industrial relocation of polluting energy-intensive activities to jurisdictions with lenient environmental standards, rather than encouraging renewable energy adoption [40]. These mixed findings suggest that environmental governance impacts on energy transitions demonstrate both positive and negative effects, necessitating additional research.

Grounded in the theoretical frameworks and empirical studies outlined above, the following hypothesis may be proposed:

H1: 

Stronger environmental governance is positively associated with the acceleration of energy transition toward renewable energy sources.

2.3. Additional Drivers of Energy Transition

This research framework also considers a set of variables that could influence energy transition. First, it examines how international capital flows, represented by foreign direct investment (FDI), interact with environmental governance to influence energy system transformations. Regarding capital inflow patterns, extensive empirical research demonstrates that FDI significantly contributes to renewable energy consumption, energy efficiency, and environmentally sustainable project development [41]. These investments may accelerate clean energy deployment and technological innovation by transferring green and environmentally friendly technologies to host countries. It also provides essential financial resources that can support the energy transition, particularly in developing economies. However, FDI inflows may also be directed toward carbon-intensive sectors such as oil, gas, and coal, where foreign investors generally prioritize short-term benefits over long-term sustainability. By doing so, these investments can reinforce existing fossil fuel–based energy infrastructures by expanding extraction, refining, and distribution capacities, thereby increasing the demand for fossil-fuel energy. Certain studies, particularly those examining middle-income economies, conclude that FDI exerts a negative impact on energy transition [42]. For instance, Fang et al. [43] explored the effects of FDI on energy transition in 60 countries over the period 2000–2020. The study confirmed the negative effects of FDI on energy transition, particularly in low-income countries and non-OECD countries. Recently, Hou et al. [44] investigated the relationship between FDI and energy transition in 38 OECD countries from 2003 to 2020. Their results suggest that FDI can impede the progress of energy transition, especially in advanced economies. Additionally, the adverse impact of FDI tends to be stronger in countries undergoing the initial phases of the energy transition. Drawing on the preceding discussion, the following hypotheses are proposed:

H2a. 

Increased foreign direct investment flows are positively associated with the adoption of renewable energy sources and the promotion of energy transition.

Second, the study considers international trade as a potential driver of energy transition. Indeed, international trade presents an important dimension of complexity in understanding energy transition dynamics. Empirical investigations reveal heterogeneous effects of trade integration on sustainable energy development across different economic contexts. The prior literature suggests that the role of trade openness in supporting renewable energy transitions is highly context dependent. In low-income economies, greater integration into global markets tends to accelerate the adoption of renewable energy. In contrast, in middle-income settings, both lower- and upper-middle-income trade openness can sometimes delay the transition process [45]. Additionally, analyses of cross-border energy trade highlight that cooperative trade frameworks can stimulate renewable energy expansion, with the impact amplified under conditions of stronger economic freedom and the advancement of the digital economy [32,46]. Based on the prior analysis, the following hypothesis is proposed:

H2b. 

Increased international trade is positively associated with the adoption of renewable energy sources and the promotion of energy transition.

Finally, economic growth represents a crucial contextual factor that may influence the energy transition process. This investigation controls for income effects represented by GDP, recognizing that countries at different income levels demonstrate distinct energy transition patterns [47,48,49]. Multiple empirical studies have confirmed positive associations between GDP and renewable energy adoption, suggesting that higher income provides greater resources for clean energy investments and supportive governance frameworks [41,50]. Accordingly, the research formulates the following hypothesis:

H2c. 

Higher economic growth rates are positively associated with the adoption of renewable energy sources and the promotion of energy transition.

2.4. Mediating Effects of Government Regulation and Technological Innovation in the Environmental Governance–Energy Transition Nexus

Government regulation can serve as an essential tool for turning environmental governance into cleaner energy outcomes by providing the required legal frameworks and enforcement mechanisms to implement environmental objectives. Environmental governance covers a wide range of policies designed to tackle environmental challenges. However, the objectives of environmental governance require specific actions to be effective. Without effective regulation, environmental governance may remain symbolic, lacking the concrete mechanisms needed to drive systemic change. Therefore, government regulation is a vital pathway for turning environmental governance into measurable and meaningful energy outcomes. Accordingly, the following hypothesis is tested:

H3a. 

Government regulation acts as a potential mechanism through which environmental governance translates into cleaner energy outcomes.

In addition, technological innovation may serve as an additional mechanism through which environmental governance leads to cleaner energy adoption. While environmental governance frameworks outline sustainability goals, their success depends on the availability of concrete tools to put them into action. Technological innovation provides those tools by enabling the development of renewable energy systems. Furthermore, environmental governance can directly support such innovation through targeted policy instruments, for example, by using revenues from carbon taxes to fund research and development activities in cleaner technologies. Other environmental policy tools, including emissions trading schemes, green public procurement, and renewable energy subsidies, may also provide direct or indirect funding for innovation in low-carbon technologies. In doing so, technological innovation allows for transforming environmental governance into tangible, systemic progress toward cleaner energy sources. Accordingly, the mediating role of technological innovation can be examined through the following hypothesis:

H3b. 

Technological innovation acts as a potential mechanism through which environmental governance translates into cleaner energy outcomes.

Figure 2 presents a theoretical framework illustrating the multiple pathways through which environmental governance drives energy transition toward renewable energy sources. The framework conceptualizes three distinct but interconnected transmission mechanisms: (1) a direct pathway whereby environmental governance immediately influences energy transition outcomes (H1), (2) an indirect pathway operating through government regulation as a mediating mechanism that translates environmental governance into concrete policy instruments and enforcement measures (H3a), and (3) a second indirect pathway through technological innovation, where environmental governance stimulates research and development activities that enable cleaner energy adoption (H3b). Additionally, the framework incorporates three contextual drivers—foreign direct investment (H2a), international trade (H2b), and economic growth (H2c)—that independently contribute to energy transition dynamics. This integrated theoretical model provides the conceptual foundation for the subsequent empirical analysis, enabling a comprehensive examination of both direct effects and complex mediating processes that govern the environmental governance–energy transition nexus.

3. Data and Empirical Model

3.1. Preliminary Diagnostics

The empirical investigation starts by examining multicollinearity through the computation of Variance Inflation Factors (VIF). Since ignoring cross-sectional dependence across panel units can distort empirical estimates, it is necessary to apply suitable econometric procedures [51]. To this end, the present study first applies Pesaran’s [52] test for cross-sectional dependence to detect interlinkages among the countries included in the sample. To examine the time-series properties, two complementary panel unit root approaches are employed. The Maddala and Wu [53] first-generation test, which assumes independence across cross-sections, is utilized alongside Pesaran’s [52] second-generation CIPS test, which accounts for potential dependence among units. In both frameworks, the null hypothesis assumes non-stationarity, and rejection indicates that the variables are stationary.

3.2. Empirical Model Specification

Building on insights from the existing body of research, energy transition dynamics are shaped by a wide range of interrelated determinants. In this study, three core explanatory dimensions are emphasized, with particular focus on environmental governance mechanisms. The theoretical model is therefore specified as follows:

$${LN\_ET}_{it}=f({LN\_EPS}_{it}, {LN\_GDP}_{it},{FDI}_{it},LN\_{TRADE}_{it})$$

(1)

where $i$ denotes individual countries, $t$ represents time periods. $LN$ indicates natural logarithmic transformation of variables. ET represents energy transition. Environmental governance is captured through environmental policy stringency (EPS), GDP represents per capita income, FDI captures foreign direct investment flows, and TRADE encompasses exports and imports relative to GDP. To facilitate coefficient interpretation and address potential heteroskedasticity and data volatility, natural logarithmic transformations are applied to all variables except FDI, which contains negative values, thereby avoiding nonnormality issues [18]. Therefore, the baseline specification becomes:

$${LN\_ET}_{it}={{\alpha }_1\ast LN\_EPS}_{it}+{\alpha }_2\ast {L{N}_{GDP}}_{it}+{\alpha }_3\ast {FDI}_{it}+{\alpha }_4\ast L{N}_{{TRADE}_{it}}+{\mu }_{it}$$

(2)

where ${\alpha }_1$–${\alpha }_4$ represent estimated parameters and ${\mu }_{it}$ denotes the stochastic error term.

Considering potential endogeneity concerns, the energy transitions–environmental governance nexus exhibits bidirectional causality; enhanced environmental governance can accelerate energy transitions, while successful energy transitions may stimulate additional environmental policy development. This bidirectional relationship suggests energy transition outcomes may endogenously determine environmental governance. Endogeneity can undermine the reliability of regression results by leading to biased and inconsistent parameter estimates. To mitigate this issue, the present study applies the Instrumental Variable two-step Generalized Method of Moments (IV 2-step GMM) estimator. Instrumental variable techniques are well recognized for their capacity to address endogeneity concerns [54]. According to [55], the IV GMM framework yields consistent estimates by correcting for endogenous bias, while also exhibiting robustness to autocorrelation and heteroskedasticity. Hence, the IV 2-step GMM offers an appropriate and reliable approach for obtaining efficient parameter estimates in this context.

Standard OLS regression fails to account for unmeasured cross-sectional variation, necessitating advanced methodological alternatives [56]. To resolve this limitation, panel data studies increasingly adopt the Generalized Method of Moments (GMM), which delivers consistent coefficient estimates despite endogeneity challenges—such as latent individual effects, reciprocal causation, and autoregressive dynamics [57]. The two-step GMM variant optimizes data utilization and enhances estimator precision for balanced panels [58,59]. Within this framework, instrumental variables isolate exogenous drivers from endogenous responses. Specifically, we employ both internal instruments, namely, lagged values of the endogenous regressors, and external instruments, selected based on their theoretical relevance and support in prior empirical literature. Here, internal instruments comprise lagged measures of environmental policy stringency, while external instruments are:

▪

High-technology exports: R&D-intensive sectors like aerospace and electronics, whose growth correlates with pollution intensification and subsequent regulatory tightening;

▪

Rule of law and corruption control: Institutional quality amplifies policy efficacy, as strong governance enables effective environmental implementation.

To ensure the robustness of our estimation strategy, we report a series of diagnostic tests, including the Kleibergen–Paap χ² statistic, Cragg–Donald F-statistic, Hansen J test, and the endogeneity test.

In addition to the IV two-step GMM estimation, we employ IV-2SLS as a robustness check to address potential endogeneity arising from omitted variable bias, reverse causality, and measurement error [60]. Unlike biased OLS under these conditions, IV-2SLS ensures asymptotic consistency. Supplementary analyses utilize: (1) Feasible GLS: Corrects for autocorrelation, heteroskedasticity, and cross-sectional dependence, satisfying Gauss-Markov requirements where OLS fails [61,62]; (2) Fixed effects models with Driscoll–Kraay SEs: Maintains consistency amid temporal/cross-sectional dependencies and non-spherical errors [63].

Both approaches absorb country-specific heterogeneity while addressing OLS’s inefficiency under violated classical assumptions.

3.3. Data Description

This study draws on a balanced panel comprising 30 countries (see

Table A1. List of countries.

1	Australia	9	Finland	17	Ireland	25	Russia
2	Austria	10	France	18	Italy	26	Sweden
3	Belgium	11	Germany	19	Japan	27	Switzerland
4	Canada	12	Greece	20	Mexico	28	Turkey
5	Chile	13	Hungary	21	Netherlands	29	United Kingdom
6	Czech Republic	14	Iceland	22	New Zealand	30	United States
7	Denmark	15	India	23	Norway
8	Estonia	16	Indonesia	24	Poland

) over the period 2010–2024. The primary outcome, energy transition, is captured by the proportion of primary energy sourced from renewables [64,65], with data obtained from Our World in Data. The Environmental Policy Stringency (EPS) index, sourced from OECD statistics, reflects the extent to which policies impose explicit or implicit costs on environmentally harmful activities. Control and instrumental variables include GDP per capita, foreign direct investment (FDI), trade openness, high-technology exports, as well as governance indicators such as corruption control and rule of law, all retrieved from the World Bank.

Table 1. Variable Definitions and Data Sources.

Variable	Code	Definition	Source
Energy Transition	ET	Proportion of total primary energy supplied by renewable sources (%)	Our World in Data
Environmental Policy Stringency	EPS	Composite index reflecting the strictness of environmental policies (0 = least, 6 = most)	OECD statistics
Gross Domestic Product per Capita	GDP	GDP per capita in constant 2015 US$	World Bank
Foreign Direct Investment	FDI	Net inflows as a percentage of GDP	World Bank
Trade	TRADE	Sum of exports and imports as a percentage of GDP	World Bank
Government regulation	GOV	Index capturing the quality and effectiveness of regulatory governance	World Bank
Technological innovation	TECH	Resident patent application	World Bank

provides a comprehensive overview of the variables, their definitions, and data sources.

4. Empirical Results

4.1. Preliminary Analysis of the Data

Table 2. Descriptive Statistics.

Variable	Observations	Mean	Standard Deviation	Minimum	Maximum	VIF
LN_ET	435	2.5892	0.7702	1.1431	4.2749
LN_EPS	435	0.9996	0.4461	−0.5390	1.5870	2.14
LN_GDP	435	10.1702	0.9147	7.1213	11.3751	1.87
FDI	435	3.6982	13.2870	−40.0864	106.5942	1.23
LN_TRADE	435	4.3262	0.5177	3.1520	5.5304	1.45

presents the descriptive statistics for all variables included in the empirical analysis. The results reveal considerable heterogeneity across the dataset, with FDI exhibiting the highest variability (standard deviation = 13.287), reflecting different investment patterns across countries. This substantial variation is expected given the heterogeneous nature of FDI flows, with some countries attracting large amounts while others receive smaller amounts. Such dispersion is common in FDI data due to differences in economic size and investment policies across countries. For instance, Hungary experienced a dramatic transformation from negative FDI flows (−15.71) in 2010 to substantial inflows (106.59) in 2020, illustrating the dynamic nature of foreign investment patterns in the sample. The energy transition variable shows moderate variability with a mean of 2.59 and a standard deviation of 0.77, suggesting meaningful differences in energy transition progress across countries. Environmental policy stringency displays relatively lower dispersion around its mean of 1.00, while GDP per capita exhibits moderate variation typical of cross-country income differences. The VIFs reported in Table 2 range from 1.23 to 2.14, well below commonly used thresholds (e.g., 5 or 10). Thus, multicollinearity among the explanatory variables is unlikely to meaningfully bias coefficient estimates or inflate standard errors in the subsequent regressions.

To examine simple pairwise relationships, Figure 3 presents Pearson correlation coefficients for the explanatory variables. Several patterns emerge. First, GDP per capita is the variable most strongly correlated with LN_ET (r = 0.492, p < 0.001), suggesting wealthier countries tend to have more advanced energy-transition outcomes. Second, environmental policy stringency is positively correlated with LN_ET (r = 0.201, p < 0.001), consistent with the theoretical expectation that stricter environmental regulation is associated with greater clean energy adoption. Third, FDI has a small but statistically significant negative correlation with LN_ET (r = −0.190, p < 0.001), which may reflect the presence of resource-seeking or carbon-intensive foreign investments in parts of the sample. Trade openness shows no significant bivariate association with LN_ET (r = −0.008, p = 0.868), suggesting the trade–transition link is likely conditional and not well captured by simple pairwise correlations.

The results in

Table 3. Cross-sectional dependence test results.

Variable	CD-Test	p-Value
LN_ET	62.749 ***	0.000
LN_EPS	16.409 ***	0.000
LN_GDP	49.570 ***	0.000
FDI	−0.623	0.534
LN_TRADE	19.048 ***	0.000

Note: Statistical significance at the 1% level is denoted by ***.

reveal significant cross-sectional dependence at the 1% level for most variables, while FDI exhibits no evidence of such dependence.

Table 4. Panel unit root test results.

Variables	Maddala–Wu [53]		Pesaran [52] CIPS
At levels	Intercept	Intercept and Trend	Intercept	Intercept and Trend
LN_ET	130.071 ***	60.538	−7.342 ***	−4.350 ***
LN_EPS	109.017 ***	113.428 ***	−3.928 ***	−1.440 *
LN_GDP	166.454 ***	30.805	−8.758 ***	0.286
FDI	226.436 ***	237.159 ***	−4.071 ***	−4.576 ***
LN_TRADE	132.352 ***	95.713 ***	−0.068	2.670
First Differences
ΔLN_ET	376.025 ***	326.841 ***	−12.784 ***	−10.651 ***
ΔLN_EPS	423.898 ***	346.401 ***	−10.978 ***	−8.740 ***
ΔLN_GDP	188.713 ***	175.152 ***	−4.368 ***	−4.386 ***
ΔFDI	831.729 ***	634.018 ***	−15.271 ***	−12.053 ***
ΔLN_TRADE	317.726 ***	206.776 ***	−6.077 ***	−4.112 ***

Notes: *** and * indicate significance at 1% and 10% levels. Δ denotes first difference.

summarizes unit root tests using first-generation) and second-generation unit root procedures. Given the detected CSD, the CIPS results are more informative: most series are stationary in levels according to CIPS, and all series become stationary after first differencing. This mixed evidence supports estimating in levels when justified, while also motivating dynamic specifications and robust inference to avoid spurious regression concerns.

4.2. Main Results

Table 5. Estimation results for environmental governance impact on energy transition.

Dependent Variable: LN_ET	IV 2-Step GMM	IV-2SLS	Feasible GLS	Driscoll-Kraay	Lagged Effect
	(1)	(2)	(3)	(4)	(5)
LN_EPS	0.9592 ***	0.6248 ***	0.3616 ***	0.3423 ***	0.360 ***
	(0.175)	(0.188)	(0.030)	(0.106)	(5.052)
LN_GDP	1.1860 ***	1.3953 ***	1.5642 ***	1.5851 ***	1.500 ***
	(0.185)	(0.174)	(0.035)	(0.154)	(11.556)
FDI	−4.86 × 10−7	−0.0008	−0.0009 ***	−0.0013	−0.001 *
	(0.001)	(0.001)	(0.0002)	(0.001)	(−1.677)
LN_TRADE	0.0318	0.1114	0.2925 ***	0.2934	0.382 ***
	(0.193)	(0.205)	(0.048)	(0.342)	(2.885)
Constant	-	-	−16.600 ***	−15.138 ***	−14.645 ***
			(0.439)	(2.564)	(−10.646)
Diagnostics
Observations	377	406	435	435	406
Number of Groups	29	29	29	29	29
R-squared	0.321	0.313	-	0.324	0.38
F-statistic	47.13 ***	52.58 ***	-	169.32 ***
Kleibergen–Paap χ2 p-value	0.000	0.000	-	-
Cragg–Donald F-statistic	44.347	107.087	-	-
Hansen J-statistic p-value	0.602	0.110	-	-
Endogeneity test p-value	0.810	0.512	-	-

Notes: Standard errors in parentheses. *** and * indicate significance at 1% and 10% levels. IV models use instrumental variables: lagged EPS, corruption control, high-tech exports, and rule of law. All models include country fixed effects.

displays the core econometric results obtained from four estimation techniques: two-step GMM, IV-2SLS, Feasible GLS (accounting for heteroskedasticity and cross-sectional correlation), and fixed-effects models with Driscoll–Kraay standard errors (robust to serial and cross-sectional dependence). Instrumental variable methods are employed to address potential endogeneity in environmental policy stringency, which may be influenced by unobserved shocks affecting energy transition. The instruments include lagged values of the environmental policy stringency index, along with governance and technology indicators such as corruption control, high-technology exports, and rule of law. Environmental policy stringency consistently exhibits a positive and statistically significant impact on energy transition across all estimation approaches. The IV 2-step GMM results suggest that a 1% increase in environmental policy stringency leads to approximately a 0.96% increase in energy transition, while the more conservative Fixed Effects estimates indicate a 0.34% increase. This robust positive relationship confirms that stringent environmental regulations serve as a crucial driver of clean energy adoption, supporting the Porter hypothesis that well-designed environmental policies can stimulate innovation and efficiency improvements.

GDP per capita demonstrates the strongest and most consistent positive effect on energy transition across all models, with elasticity coefficients ranging from 1.19 to 1.59. This finding aligns with the environmental Kuznets curve literature, suggesting that higher income levels enable countries to invest in cleaner technologies and prioritize environmental quality over short-term economic gains. The results for trade openness are mixed, showing statistical significance only in the Feasible GLS specification (coefficient = 0.29, p < 0.001). This suggests that the relationship between trade and energy transition may be conditional on other factors not fully captured in the current specification. FDI exhibits a consistently adverse but economically small effect on energy transition, with statistical significance only in the GLS model (coefficient = −0.0009, p < 0.001). This counterintuitive finding may reflect the composition of FDI flows, which could be concentrated in traditional energy-intensive sectors rather than clean technology investments. These findings are consistent with Fang et al. [43], who examined 60 countries over the period 2000–2020 and found that FDI negatively influences the energy transition in low-income countries. Similarly, Hou et al. [44] analyzed the impact of FDI on energy transition across 38 OECD countries from 2003 to 2020, employing a range of estimation techniques. Their results indicate that FDI tends to hinder energy transition, particularly in more developed OECD economies.

The instrumental variable specifications pass all standard diagnostic tests. The Kleibergen–Paap under identification test strongly rejects the null hypothesis (p < 0.001), confirming the relevance of instruments and model identification. The Cragg–Donald F-statistics (44.35 and 107.09) exceed conventional critical values, indicating that instruments are not weak and are strongly correlated with the endogenous regressors. The Hansen J-test fails to reject instrument validity (p > 0.10), supporting the exogeneity assumption of the instruments. The endogeneity test confirms that environmental policy stringency is indeed endogenous, justifying the instrumental variable approach. Moreover, the consistency of results across multiple estimation techniques reinforces the robustness of the findings. The positive relationship between environmental policy stringency and energy transition suggests that policy interventions can effectively accelerate clean energy adoption. The substantial income effects highlight the importance of economic development in facilitating energy transitions. At the same time, the mixed results for trade and FDI indicate that these channels may require more targeted policies to maximize their contribution to energy transition goals.

4.3. Mechanism Analysis

To identify the underlying pathways through which environmental governance influences energy transition, we implement a systematic mediation analysis using a two-stage estimation approach. This methodology enables us to decompose the total effect and quantify the relative importance of different transmission channels. The mediation framework follows the structural equations:

First stage:

$${MED}_{it}={\pi }_0+{\pi }_{1}LN\_EPS+{\pi }_2{X}_{it}+{\theta }_i+{\delta }_t+{\epsilon }_{it}$$

(3)

Second stage:

$${LN\_ET}_{it}={\alpha }_0+{\alpha }_1{MED}_{it}+{\alpha }_2{X}_{it}+{\theta }_i+{\delta }_t+{\epsilon }_{it}$$

(4)

where MED represents the mediating variables (government regulation and technological innovation), and the mediation effect is confirmed when both π₁ and α₁ are statistically significant. The results are reported in

Table 6. Mechanism effect tests.

Variables	Government Regulation	Energy Transition	Technological Innovation	Energy Transition
	(1)	(2)	(3)	(4)
LN_EPS	0.206 ***		1.350 ***
	(6.014)		(8.169)
Government regulation		0.401 ***
		(6.275)
Technological innovation				0.057 *
				(1.659)
LN_GDP	0.328 ***	0.346 ***	0.101	0.419 ***
	(20.023)	(6.477)	(1.433)	(3.111)
FDI	0.000	−0.008 ***	−0.001	−0.001
	(0.258)	(−3.904)	(−0.416)	(−1.485)
LN_TRADE	0.011	−0.161 **	−3.013 ***	0.268 **
	(0.405)	(−2.172)	(−21.884)	(2.452)
Constant	−2.924 ***	−0.324	18.548 ***	−3.277 **
	(−16.680)	(−0.634)	(27.781)	(−2.260)
Country FE	✓	✓	✓	✓
Time FE	✓	✓	✓	✓
Observations	435	435	435	435
R-squared	0.651	0.350	0.556	0.967

Notes: Reported t-statistics (in parentheses) are computed using country-level clustered standard errors. Significance levels are denoted as follows: * p < 0.10, ** p < 0.05, and *** p < 0.01.

.

4.3.1. Government Regulation Channel

The mediation analysis identifies government regulation as a statistically significant transmission pathway through which environmental governance, as measured by the environmental policy stringency index, influences energy transition, though the economic magnitude remains modest. The first-stage estimation (Column 1) indicates that heightened environmental policy stringency substantially promotes government regulatory quality, yielding a coefficient of 0.206 (significant at the 1% level). This robust association implies that a one-unit rise in policy stringency corresponds to roughly a 20.6% increase in regulatory quality, underscoring the capacity of stringent environmental frameworks to catalyze broader institutional enhancements.

This linkage unfolds through a network of interconnected dynamics that amplify regulatory efficacy in multifaceted ways. Stricter environmental policies expand fiscal resources via efficiency-driven economic gains and targeted levies, empowering governments to strengthen their regulatory apparatuses and refine institutional oversight. Such fiscal bolstering initiates a self-reinforcing loop, wherein policy rigor directly fuels investments in enforcement mechanisms and administrative sophistication. At the same time, the analytical rigor embedded in stringent environmental governance spills over into public administration, sharpening data analytics, compliance monitoring, and adaptive policymaking across domains. Sectors subject to elevated environmental mandates, in turn, necessitate refined regulatory architectures to navigate compliance complexities, fostering an organic push for institutional evolution that elevates governmental responsiveness and coherence.

The second-stage findings (Column 2) substantiate that improved government regulation significantly advances the energy transition, with a coefficient of 0.401 (significant at the 1% level). This pronounced impact highlights how fortified regulatory environments enable seamless orchestration of energy strategies, streamlined deployment of transition initiatives, and sustained stability amid evolving technological landscapes. The resultant indirect effect via government regulation, calculated as 0.206 × 0.401 = 0.083, captures about 23% of the overall influence of environmental policy stringency on energy transition, affirming the integral yet partial mediating role of regulation.

4.3.2. Technological Innovation Channel

Technological innovation emerges as the predominant channel through which environmental governance, measured by environmental policy stringency, drives the energy transition. First-stage results (Column 3) uncover a strikingly potent positive association between policy stringency and technological innovation, with a coefficient of 1.350 (significant at the 1% level). This compelling connection reveals how rigorous environmental mandates unleash expansive innovation externalities that permeate the economic fabric, elevating inventive output far beyond incremental adjustments.

The interplay between stringent policies and innovation thrives on layered, mutually reinforcing processes that embed sustainability into technological trajectories. Policy-induced pressures compel firms to internalize environmental costs, sparking direct knowledge transfers and adaptive learning that ripple into novel solutions across interconnected industries. Concurrently, the skill demands of compliance under tight standards cultivate human capital reservoirs, nurturing expertise that fuels inventive pursuits through hands-on experimentation and interdisciplinary collaboration. By optimizing resource flows toward high-impact areas, these policies liberate capital for R&D intensification, while simultaneously reshaping market incentives to reward differentiation via eco-efficient breakthroughs. This holistic reconfiguration not only accelerates general technological advancement but also aligns it toward sustainability imperatives, yielding a cascade of innovations attuned to long-term systemic needs.

Intriguingly, the second-stage estimation (Column 4) discloses a comparatively subdued yet marginally significant influence of technological innovation on energy transition, with a coefficient of 0.057 (significant at the 10% level). This tempered linkage intimates that although environmental policy stringency vigorously ignites broad-spectrum innovation, the resultant advancements may not invariably calibrate precisely to energy-specific exigencies, potentially diluting their transitional potency. The indirect effect channeled through technological innovation, 1.350 × 0.057 = 0.077, accounts for around 21% of the total policy stringency impact, positioning innovation as a vital yet nuanced amplifier in the pathway to energy transition.

5. Conclusions, Policy Implications, and Future Research Directions

5.1. Main Conclusions and Discussion of Findings

Amid the pressing imperative to combat climate change and foster sustainable energy systems, stringent environmental policies have emerged as a central concern for academics and policymakers alike. While prior research has explored various drivers of energy transition, a notable void remains in comprehending how environmental governance interfaces with varied economic and institutional landscapes to influence national energy outcomes. This study bridges this gap through a thorough examination of environmental governance’s multifaceted effects on energy transition, employing a robust analytical lens. Drawing on panel data from 29 countries spanning 2010–2024 and applying diverse econometric techniques—such as instrumental variables and cross-sectionally robust estimators—the results furnish compelling empirical backing for the pivotal, positive contribution of environmental governance to energy transition advancement. The findings reveal that environmental governance policies act as a key driver for energy transition, exhibiting consistently positive and statistically significant impacts across all estimation methods. This linkage illustrates how stricter environmental policies propel nations toward faster uptake of clean energy technologies and practices. The robustness is evident in the uniform outcomes from various techniques, with elasticity coefficients varying from 0.34 to 0.96, highlighting the profound sway of thoughtfully crafted environmental regulations on national energy frameworks.

This central discovery aligns with and augments an expanding corpus of literature underscoring the revolutionary capacity of environmental policy structures. It resonates with Bashir et al. [30], who stressed the vital function of effective environmental governance in propelling sustainable economic growth, and Shahzad et al. [31], who observed that rigorous regulations encourage cleaner energy adoption. Additionally, the results bolster Adebayo and Alola [66], noting regulations’ stimulation of green technology and renewable infrastructure investments, and Nachtigall et al. [17], showing reductions in carbon emissions and energy intensity—thus solidifying the conceptual basis for policy-driven energy shifts. This synthesis of evidence from diverse methods and contexts affirms the durability and applicability of the environmental policy–energy transition nexus across heterogeneous economic and institutional milieus. Beyond statistical correlations, the practical ramifications are profound. The outcomes endorse the Porter Hypothesis [39], asserting that adept environmental regulations can spark innovation and efficiency gains that counterbalance compliance expenses. Consequently, firms and policymakers should perceive environmental rigor not as a fiscal drag but as a spur for technological progress and market edge. Nations aspiring to elevate energy transition levels ought to emphasize crafting and enact holistic policy frameworks that methodically diminish reliance on traditional energy while spurring clean and renewable tech adoption. Furthermore, the robust affirmative link between policy stringency and energy transition underscores the necessity of regulatory predictability and enduring commitment for sectoral investments. Clear policy signals regarding future directions empower private entities to judiciously decide on tech integration and infrastructure, expediting the transition tempo.

Importantly, the findings also reveal that the influence of environmental governance is not confined to its direct effect on renewable adoption but also operates indirectly through two mediating channels: government regulation and technological innovation. The results suggest that environmental governance enhances the effectiveness of regulatory frameworks, providing legal and institutional mechanisms that ensure environmental objectives are translated into enforceable policies. At the same time, governance fosters technological innovation by stimulating research, development, and deployment of clean technologies. These mediators are critical in transforming policy ambition into tangible energy outcomes, amplifying the overall impact of governance on transition dynamics.

Extending past the primary insight on policy stringency, the research unveils further nuances enriching energy transition comprehension. The steadfast positive and markedly significant GDP per capita impact stands as a cornerstone result, with elasticities from 1.19 to 1.59 across approaches. This affirms the environmental Kuznets curve in energy realms, implying elevated incomes empower environmental prioritization and cleaner tech investments. Economic development, via GDP per capita, emerges as a potent facilitator of energy transition, with elasticities surpassing one in all models, endorsing the Kuznets hypothesis and underscoring economic capability’s essential role in clean energy embrace. The underlying theoretical pathways are intricate and linked. Growth reshapes consumer inclinations toward eco-friendly offerings, including cleaner energy. Elevated incomes boost willingness and capacity for premium clean tech payments despite initial cost premiums over fossils. Moreover, development bolsters national investments in R&D, education, and infrastructure—vital for transition success. Yet, this potent income influence prompts equity queries, potentially exacerbating divides between affluent and emerging economies. Although growth inherently aids transition, it signals hurdles for low-income nations sans focused global aid and technology transfers. Trade openness yields a subtler narrative, with varied evidence: positive significance in Feasible GLS but insignificance elsewhere. This variability implies trade–energy transition ties are intricate and contingent, beyond basic pairings. The inconsistent trade openness outcomes indicate international avenues demand strategic focus for effective transition backing. Mechanisms vary: trade can aid via clean tech access, cost reductions through competition, and knowledge diffusion. Conversely, liberalization may entrench carbon-heavy specializations, especially in fossil-exporting resource nations, hindering transition by bolstering conventional dependencies. Thus, trade’s conditional effects highlight that relationship quality and makeup trump sheer openness levels. Foreign direct investment displays a persistently negative yet minor economic impact on transition, significant only in select models. The broadly feeble or detrimental FDI effects suggest this channel needs precise targeting for transition support. Though counterintuitive at first, this stems from FDI’s varied flows and sectors, not innate harm. The observed weak negativity likely arises from much FDI targeting energy-dense areas like fossil extraction and heavy industry. This implies FDI’s transition efficacy hinges on host countries’ policy and institutional setups.

The study’s methodological diligence—encompassing thorough diagnostics for instrument validity, cross-sectional dependence, and unit roots—instills assurance in these as authentic causal links, not artifacts. Instrumental variables’ adept use mitigates endogeneity, fortifying causal readings of governance’s transition impacts.

5.2. Policy Implications

The analysis indicates that tightening environmental regulations can markedly speed up energy transition, offering robust evidence to support ambitious policy frameworks. Accordingly, governments are encouraged to implement comprehensive and rigorous policies that foster innovation and facilitate the shift toward renewable and low-carbon energy sources. These policies should be designed with clear long-term trajectories that provide regulatory certainty for private sector investment decisions while maintaining sufficient flexibility to adapt to technological developments. Policymakers should consider implementing differentiated policy approaches that account for sectoral heterogeneity in environmental impact and transition potential. Industries with higher pollution rates should be subject to more stringent regulations while receiving enhanced incentives and support for clean energy adoption. This sector-specific approach can optimize the efficiency of policy interventions by targeting resources where they can achieve the greatest environmental impact while minimizing economic disruption. The provision of well-designed incentives, including subsidies, tax reductions, feed-in tariffs, and green certificates, can make clean energy sources more accessible and cost-effective for both businesses and consumers. However, these incentives should be carefully structured to avoid market distortions and should include sunset clauses that gradually reduce support as clean technologies achieve cost competitiveness with conventional alternatives. Governments should substantially increase public awareness of the importance and benefits of clean energy systems through comprehensive education and communication campaigns. These initiatives should emphasize not only the environmental benefits but also the economic advantages of energy transition, including job creation in clean energy sectors, energy security improvements, and long-term cost savings. When formulating new environmental policies, it is essential to consider regional variations in environmental conditions, socio-economic contexts, and institutional capacities to achieve energy transitions that are effective, inclusive, and well-balanced. Just transition policies should be integrated into energy transition frameworks to support workers and communities dependent on conventional energy sectors while creating new opportunities in clean energy industries. Given the mixed results for trade openness, countries should consider not just increasing trade volumes but strategically orienting their trade policies toward clean technology imports and low-carbon export diversification. Countries seeking to leverage foreign investment for energy transition should consider implementing green investment criteria, offering specific incentives for clean energy FDI, and establishing regulatory frameworks that channel foreign capital toward sustainable energy infrastructure. Firms should proactively develop internal governance structures and management systems that enhance resource efficiency and promote investments in clean technologies. Companies that anticipate and prepare for environmental policy changes will be better positioned to maintain competitive advantages as regulations become more stringent. For multinational corporations and international investors, the study’s results provide important guidance for location and investment decisions. Business leaders are encouraged to engage with, rather than shy away from, countries enforcing rigorous environmental regulations, as these policies generate promising market prospects for clean energy technologies and related services.

5.3. Limitations and Future Research Directions

Despite its contributions, this study acknowledges several important limitations that should be considered when interpreting results and designing future research. First, the analysis examines the relationship between environmental governance policy and energy transition at the national level for 29 economies, with country selection dictated primarily by data availability constraints. While this sample provides valuable insights, it may not fully capture the diversity of experiences across different development levels and institutional contexts. The relatively small sample size, while sufficient for robust econometric analysis, limits the ability to conduct detailed subgroup analyses or investigate threshold effects that might vary across development levels. Second, the study’s focus on national-level aggregates may mask significant subnational variation in energy transition patterns and policy effectiveness. Regional differences within countries, urban-rural disparities, and local institutional factors could significantly influence the relationship between environmental policies and energy transition outcomes. Future research would benefit from incorporating subnational data where available to capture these important dimensions of variation. Third, the model specification could be further enhanced by including additional control variables that are likely to influence the energy transition. Factors such as the urbanization rate, energy prices, and expenditures on research and development in green technologies may play a significant role in shaping the pace and direction of the transition toward cleaner energy sources. Future research should investigate the conditional effects of trade composition and FDI sectoral distribution more deeply, potentially examining how trade composition, technology content, and partner country characteristics moderate the relationship between trade openness and energy transition. While the study employs environmental policy stringency indicators widely accepted in the literature, future research should seek to develop and utilize indicators that better capture policy implementation effectiveness rather than just policy stringency on paper. The study contributes to the growing literature on policy-driven energy transitions by providing rigorous empirical evidence on the effectiveness of environmental governance in promoting clean energy adoption. As countries worldwide grapple with climate change challenges, these findings offer actionable insights for designing effective energy transition strategies that leverage both domestic policy instruments and international economic channels. The evidence strongly supports the view that well-designed environmental policies can simultaneously achieve environmental objectives and economic development goals, providing a foundation for sustainable energy futures.