Research Article

The Influence of Environmental Policies and Technologies on the Transition to Renewable Energy

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DOI:

10.3791/71760

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August 7th, 2026

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1University of Southampton, 2School of Marxism, Nanchang University, 3School of Public Finance and Taxation, Lanzhou University of Finance and Economics, 4Hongshan College, Nanjing University of Finance & Economics, 5Nanjing University of Finance & Economics

In This Article

Summary

This study of 21 OECD countries (2000-2020) finds that the Environmental Policy Index, greenhouse gas emissions, and environment-related technology facilitate renewable energy adoption, while Green GDP hinders it. A two-way causal relationship exists among all variables. The pace of transition remains insufficient, requiring policy reforms and pollution taxes.

Abstract

Renewable energy capacity (REC) plays a central role in achieving sustainable development and carbon neutrality, yet the effects of ecological policy performance, environmental quality, technological innovation, and green economic growth on REC remain heterogeneous across countries and stages of renewable energy development. This study investigates the determinants of renewable energy capacity across 21 OECD countries from 2000 to 2020, focusing on ecological policy performance (EPI), greenhouse gas (GHG) emissions, eco-friendly technology (EFT), and Green GDP. The empirical framework employs the Panel Autoregressive Distributed Lag (ARDL) bounds testing approach to examine long-run cointegration, the Method of Moments Quantile Regression (MMQR) to capture heterogeneous effects across the conditional distribution of REC, and panel Granger causality tests to determine the direction of causal relationships. The Panel ARDL bounds test confirms a stable long-run equilibrium relationship among the variables (F = 7.845, p < 0.01). MMQR results show that EPI exerts a positive and progressively stronger effect across quantiles (0.092–0.212), while EFT exhibits the steepest positive gradient (0.131–0.221) and demonstrates bidirectional causality with REC, indicating a reinforcing cycle between technological innovation and renewable energy deployment. GHG emissions have a modest positive association with REC (0.048–0.082) but do not unidirectionally cause REC; instead, higher renewable energy capacity significantly reduces emissions. Green GDP consistently exhibits a negative effect on REC (−0.348 to −0.138), although this effect weakens at higher renewable energy capacity quantiles, suggesting a transitional adjustment during the shift toward a greener economy. These findings indicate that the effects of environmental policy, technological innovation, emissions, and green economic performance are stage-dependent, highlighting the need for deployment-specific policy frameworks, sustained investment in green research and development, and complementary policy instruments to mitigate short-term adjustment costs while accelerating the renewable energy transition.

Introduction

Escalating global energy demand has intensified the need for a rapid transition to clean energy sources. Although renewable energy accounted for approximately 30% in solar energy supply growth, fossil fuels continue to dominate incremental energy demand, resulting in the continued accumulation of carbon dioxide (CO2) emissions and threatening the achievement of the Paris Agreement targets1. A systematic understanding of sustainable energy investment trends, as illustrated through the scientific mapping and meta-analysis presented in Financing the Future: Insights into Sustainable Energy Investments, highlights the convergence of policy, financial, and technological transformations that occurred across OECD economies between 2000 and 20202. This twenty-year period, encompassing 21 OECD countries, provides an ideal setting for evaluating the long-run determinants of renewable energy deployment3. The renewable energy transition depends on the effective interaction of environmental governance, financial investment, and technological innovation4. A recent systematic review identified economic and policy factors as the principal drivers of renewable energy expansion. Threshold analyses of OECD countries further demonstrated that environmental policy stringency promotes renewable energy deployment only after surpassing a critical threshold5, while the diffusion of eco-friendly technologies similarly requires sustained investment before significant benefits are realized6. Moreover, dynamic panel quantile regression analyses across 77 countries have shown that eco-friendly technologies significantly promote renewable energy consumption, particularly in developing economies7.

Despite these advances, several important research gaps remain. Existing studies typically examine environmental policy and technological innovation in isolation, overlooking their potential synergies in renewable energy deployment. In addition, renewable energy capacity (REC), used in this study as the dependent variable, has received limited attention within integrated empirical frameworks that simultaneously consider environmental policy, technological innovation, emissions, and green economic performance8. Furthermore, momentum toward renewable energy adoption in advanced economies has slowed. OECD greenhouse gas (GHG) emissions remain high, while climate policy stringency increased by only 1% in 2024, highlighting an implementation gap that warrants deeper investigation into renewable energy drivers in high-income economies9. Accordingly, this study examines the determinants of renewable energy capacity across 21 OECD countries from 2000 to 2020 using the Ecological Performance Index (EPI), greenhouse gas (GHG) emissions, eco-friendly technology (EFT), and Green GDP as explanatory variables. Following preliminary diagnostic testing, the Panel ARDL bounds cointegration test is employed to establish long-run equilibrium relationships, the Method of Moments Quantile Regression (MMQR) is applied to capture distributional heterogeneity, and panel causality tests are conducted to determine the direction of causal relationships10. The findings demonstrate that EPI and EFT exert significant positive effects only beyond specific thresholds, whereas the influence of Green GDP varies across the conditional distribution of renewable energy capacity, emphasizing the importance of coordinated environmental policy, technological innovation, and economic strategies11. By explicitly modeling the joint effects of environmental policy performance and technological development across different levels of renewable energy capacity, this study provides more nuanced evidence to support renewable energy policy formulation.

The determinants of renewable energy transition have been extensively investigated; however, understanding remains constrained by fragmented theoretical perspectives and predominantly descriptive rather than integrative empirical analyses12. In particular, the principal drivers of renewable energy deployment, including environmental policy performance, eco-friendly technology, greenhouse gas emissions, and green-adjusted economic output, have rarely been examined within a unified analytical framework considering financial market interconnectedness and the digitalization of green finance13.

Environmental policy performance, commonly measured using composite indicators such as the Ecological Performance Index (EPI), occupies a central position in the renewable energy literature14. Early studies demonstrated that well-designed environmental regulations reduce investment uncertainty and provide predictable market signals that encourage low-carbon investment15. More recent panel studies of OECD countries employing fixed-effects and system generalized method of moments estimators reported that a one-unit increase in environmental policy stringency is associated with a 0.34% increase in renewable electricity generation16. Nevertheless, a critical evaluation of this literature indicates that the positive effects of policy stringency diminish at very high levels due to regulatory fatigue17. Furthermore, the exclusive focus on high-income economies limits the external validity of these findings. A comprehensive meta-analysis of 89 empirical studies concluded that market-based policy instruments, including carbon taxes and emissions trading systems, consistently outperform command-and-control regulations in promoting renewable energy capacity18. Although this meta-analysis synthesized evidence from diverse geographical regions and methodological approaches, the authors emphasized that policy effectiveness remains substantially greater in countries with stronger institutional capacity, highlighting an important geographical gap in the literature19. In addition, policy-oriented studies generally treat renewable energy markets as independent from broader financial systems, overlooking the possibility that cross-market financial spillovers may strengthen or weaken policy effectiveness20.

A second body of literature identifies eco-friendly technology (EFT) as a major catalyst for reducing renewable energy costs and accelerating technology diffusion. Empirical evidence based on patent data from 25 European countries demonstrated that a 10% increase in environment-related patent applications reduced the levelized cost of solar photovoltaic systems by 1.8% and onshore wind energy by 2.3%21. Although these studies employ rigorous econometric methods, they primarily rely on conventional innovation indicators and fail to account for the growing importance of digital green finance and financial technology (FinTech) in facilitating climate investment22. Recent evidence from emerging Organization of Islamic Cooperation (OIC) economies indicates that digital financial services strengthen environmental policies by lowering transaction costs and expanding access to green financing23. Consequently, measuring EFT solely through patent activity may underestimate interactions between environmental policy and technology-enabled financial mechanisms24. Furthermore, studies of technology diffusion report substantial cross-border spillover effects, with innovations originating in Germany and Denmark contributing significantly to global wind energy deployment25. However, these investigations rarely consider how financial contagion across clean energy markets influences eco-friendly technology diffusion. Recent evidence on dynamic interconnectedness among green, clean energy, and sustainable financial markets demonstrates substantial volatility spillovers, suggesting that shocks affecting one segment of the green financial system can rapidly influence renewable energy investment26. Accordingly, interpreting EFT exclusively as a domestic patent-based indicator overlooks the broader financial environment through which technological diffusion increasingly occurs27.

The relationship between greenhouse gas (GHG) emissions and renewable energy deployment remains controversial. According to the "political pressure" hypothesis, rising emissions stimulate public demand for stronger decarbonization policies and increased investment in renewable energy. A cross-lagged panel analysis of 34 OECD countries supports this hypothesis, demonstrating that a one-standard-deviation increase in per capita GHG emissions predicts a subsequent 0.21-standard-deviation increase in renewable energy investment after two years28. However, these findings are based exclusively on advanced economies with relatively strong institutional quality, which limits their applicability to carbon-intensive developing countries. Alternative evidence suggests a non-linear relationship in which powerful fossil fuel interests increasingly constrain renewable energy policies once emissions exceed a critical threshold29. Threshold regression analyses using data from 60 countries identified an inverted U-shaped relationship, with the turning point varying with institutional quality and natural resource dependence30. Importantly, these studies generally overlook the moderating influence of financial market interconnectedness. During periods of rising emissions, negative investor sentiment may spill over into clean-energy financial markets, constraining renewable-energy financing precisely when investment demand is greatest. Recent evidence on cross-market volatility transmission supports this mechanism and suggests that financial spillovers may complicate the expected positive relationship between emissions and renewable energy investment31,32.

Green GDP, defined as gross domestic product adjusted for environmental degradation and natural resource depletion, provides a more comprehensive measure of sustainable economic performance than conventional GDP33. Nevertheless, empirical evidence regarding its relationship with renewable energy capacity remains mixed. Some studies argue that improvements in Green GDP reduce the perceived urgency of renewable energy investment by signaling lower environmental damage per unit of economic output34. In contrast, other investigations suggest that sustained growth in Green GDP reflects a structural transformation toward service-oriented, less energy-intensive economies that facilitate the integration of renewable energy35. Panel cointegration analyses covering 40 countries between 2000 and 2018 provide evidence of temporal asymmetry, revealing a negative short-run effect of Green GDP on renewable energy capacity but a positive long-run relationship following sustained green economic growth36. Although this evidence is based on extensive datasets and long observation periods, it does not account for the effects of global financial shocks. During episodes of financial instability, volatility spillovers across green financial markets may weaken the Green GDP–renewable energy capacity relationship by constraining investment flows into renewable energy projects, despite improvements in environmental performance37,38.

In summary, existing research has substantially improved our understanding of the determinants of renewable energy deployment; however, important gaps remain. Previous studies have generally examined environmental policy, technological innovation, greenhouse gas emissions, and green economic performance independently rather than within an integrated analytical framework. In addition, eco-friendly technology is still measured primarily using conventional innovation indicators. To address these limitations, this study investigates renewable energy capacity across 21 OECD countries during 2000–2020 using the Ecological Performance Index, greenhouse gas emissions, eco-friendly technology, and Green GDP as explanatory variables. By applying the Method of Moments Quantile Regression, the study captures heterogeneous effects across the distribution of renewable energy capacity, providing a more comprehensive understanding of the policy, technological, and economic factors shaping the renewable energy transition. Future research should incorporate FinTech indicators and measures of financial risk to further advance this field.

Protocol

Research philosophy and design
This study adopts a positivist research philosophy, assuming that the determinants of renewable energy capacity (REC) can be objectively examined using quantitative panel data analysis. A longitudinal, cross-country comparative research design is employed to investigate the long-run relationships between renewable energy capacity and its determinants across 21 Organization for Economic Co-operation and Development (OECD) countries over the period 2000–2020. Guided by the Kaya Identity, a deductive-analytic approach is used to develop the empirical model and test the hypothesized relationships among renewable energy capacity, ecological policy performance, greenhouse gas (GHG) emissions, eco-friendly technology (EFT), and Green GDP. The analytical framework integrates data collection, diagnostic testing, cointegration analysis, quantile regression, and panel causality analysis to ensure methodological consistency and robust statistical inference.

Data collection and variables
The study utilizes a balanced panel dataset comprising 21 OECD countries observed annually from 2000 to 2020, resulting in 441 country-year observations. Data were obtained from publicly accessible and internationally recognized databases to ensure transparency and reproducibility. Table 1 summarizes the variables, measurement units, data sources, and expected signs. Renewable energy capacity (REC) is used as the dependent variable and is measured as the total installed renewable electricity generation capacity (MW). The explanatory variables include the Ecological Performance Index (EPI), greenhouse gas (GHG) emissions (CO₂-equivalent tonnes), eco-friendly technology (EFT), measured by environment-related patent applications, and Green GDP, measured as gross domestic product adjusted for environmental degradation and natural resource depletion (constant 2015 USD). Renewable energy capacity, EPI, GHG emissions, and EFT were obtained from the OECD Statistics database, whereas Green GDP was obtained from the World Bank database. All variables were transformed into natural logarithms prior to estimation to stabilize variance, reduce heteroscedasticity, and facilitate elasticity interpretation.

Theoretical framework and model specification
The empirical framework is based on the Kaya Identity39, which decomposes carbon dioxide emissions into population, economic activity, energy intensity, and carbon intensity, thereby providing a theoretical basis for understanding the drivers of environmental sustainability:

CO2 emissions formula diagram; CO2=Energy/Green GDP*Green GDP/Population*Population. (1)

where CO2 represents carbon dioxide emissions; CO2/Energy denotes the carbon intensity of energy consumption; Energy/Green GDP represents the energy intensity of environmentally adjusted economic output; Green GDP/Population denotes environmentally adjusted gross domestic product per capita; and Population represents the total population.

The explanatory variables correspond directly to the theoretical components of the Kaya Identity. The Ecological Performance Index represents environmental policy performance aimed at promoting cleaner energy systems. Eco-friendly technology reflects technological innovation that improves energy efficiency and reduces renewable energy costs. Greenhouse gas emissions represent environmental pressure for decarbonization, whereas Green GDP captures environmentally adjusted economic performance. Based on this framework, the following empirical model is estimated39:

Economic model equation; RECi involving FP, GHG Emission, Green GDP; economic factor analysis. (2)

where REC denotes renewable energy capacity; EPI represents the Ecological Performance Index; GHG denotes greenhouse gas emissions; EFT represents eco-friendly technology; Green GDP denotes environmentally adjusted gross domestic product; i indexes countries; t indexes years; τ0 is the intercept; τ1–τ4 are the estimated regression coefficients; εit is the idiosyncratic error term.

Diagnostic tests
Prior to estimating the empirical models, a series of diagnostic tests was conducted to assess the statistical properties of the panel data and to identify appropriate estimation techniques. First, cross-sectional dependence was examined using the Pesaran cross-sectional dependence (CD) test34, as OECD countries are likely to experience common shocks arising from global economic conditions, international energy markets, and coordinated environmental policies:

CSD_LM-Adjusted formula, statistical analysis, equation, variance, experimental data evaluation. (3)

where N denotes the number of cross-sectional units; T denotes the number of time periods; i and k index the cross-sectional units (i ≠ k); Equation representing strain rate tensor \( \hat{\gamma}_{ik} \). represents the estimated pairwise correlation coefficient of the regression residuals between cross-sectional units i and k; j denotes the lag order; E(·) represents the expected value; and V(·) denotes the variance used to standardize the test statistic. Second, slope heterogeneity was assessed using the Pesaran–Yamagata slope heterogeneity test to determine whether the effects of explanatory variables differed across countries40:

Formula depicting statistical analysis; equation involves variables N, k, Ŝ; concept visualization. (4)
static equilibrium equation, ΣFx=0, mathematical formula for force balance analysis (5)

where Δ̃adj denotes the adjusted slope heterogeneity test statistic; N represents the number of cross-sectional units; T denotes the number of time periods; k is the number of explanatory variables; and S̃ represents the standardized Swamy slope dispersion statistic used to evaluate parameter heterogeneity across panel units. Finally, stationarity was evaluated using the Cross-sectionally Augmented Im–Pesaran–Shin (CIPS) unit root test, which accommodates both cross-sectional dependence and heterogeneous slope coefficients41:

CIP̅S formula for cumulative distribution function; mathematical equation for statistical analysis. (6)

where CIPS denotes the Cross-sectionally Augmented Im–Pesaran–Shin panel unit root statistic; N represents the number of cross-sectional units; CDFi denotes the cross-sectionally augmented Dickey–Fuller statistic for the ith cross-sectional unit; and i indexes the individual cross-sectional units. The results of these diagnostic tests guided the selection of second-generation panel econometric techniques suitable for heterogeneous panel datasets.

Panel cointegration test
To determine whether a stable long-run equilibrium relationship exists among renewable energy capacity, environmental policy performance, greenhouse gas emissions, eco-friendly technology, and Green GDP, the Panel ARDL bounds cointegration test was employed. Compared with conventional residual-based cointegration tests, the Westerlund approach accommodates cross-sectional dependence, heterogeneous slope coefficients, and small sample sizes. The test produces four statistics: two group-mean statistics (Gt and Ga) and two panel statistics (Pt and Pa). Rejection of the null hypothesis indicates the presence of a long-run equilibrium relationship among the variables36,42:

Statistical equation Σϑ/SE(ϑ) for data analysis; summation formula; research data interpretation. (7a)
Static equilibrium equation, Σ(Tϑ̂ᵢ/ϑ̂ᵢ(1)), formula for balance, educational diagram. (7b)
Statistical hypothesis testing; equation of t-test; diagram for educational research purpose. (7c)
Thermodynamics equation: Pa=Tvϑ, static equilibrium, physics formula. (7d)

where Gt and Ga denote the group-mean test statistics of the Panel ARDL bounds cointegration test; Pt and Pa denote the panel test statistics; N represents the number of cross-sectional units; T denotes the number of time periods; Estimation theory symbol \(\hat{\vartheta}_i\), includes statistical analysis concept. is the estimated error-correction coefficient for the ith cross-sectional unit; Static equilibrium equation ΣFx=0, ΣFy=0 diagram; mechanics concept educational use. represents the pooled error-correction coefficient; SE(Estimation theory symbol \(\hat{\vartheta}_i\), includes statistical analysis concept.) and SE(Static equilibrium equation ΣFx=0, ΣFy=0 diagram; mechanics concept educational use.) denote the corresponding standard errors; and i indexes the individual cross-sectional units. The presence of cointegration justifies estimating long-run elasticities and subsequent quantile-based analyses.

Method of moments quantile regression (MMQR)
Because conventional panel estimators provide only average effects, they may conceal substantial heterogeneity across countries operating at different levels of renewable energy deployment. To capture these heterogeneous effects, the Method of Moments Quantile Regression (MMQR) was employed.

MMQR estimates the effects of ecological policy performance, greenhouse gas emissions, eco-friendly technology, and Green GDP across multiple conditional quantiles of renewable energy capacity, thereby allowing the influence of explanatory variables to vary according to the maturity of renewable energy deployment. The method simultaneously accounts for individual fixed effects and unobserved heterogeneity while estimating both location and scale parameters43:

Quantile regression equation, \(Q_{y_{it}}(\tau |X_{it}) = \sigma_1 + X_{it} \zeta + ( \alpha_i + Z_{it} \gamma ) U_{it}\) (8)

where Qτ(Yit | Xit) denotes the conditional τth quantile of the dependent variable; Yit represents renewable energy capacity (REC); Xit denotes the vector of explanatory variables (EPI, GHG emissions, EFT, and Green GDP); σ1 is the intercept; ζ is the vector of location parameters; αi represents the country-specific fixed effect; Zit denotes the scale covariates; γ is the vector of scale parameters; Uit is the error term; and i and t index countries and years, respectively. The scale component of the MMQR model is specified as follows43:

Mathematical equation, Zi=Zi(X), i=1,...,k, used in statistical or computational analysis. (9)

where Zi(X) denotes the scale function of the explanatory variables; Zi represents the vector of scale covariates; X denotes the vector of explanatory variables; i indexes the cross-sectional units (countries); and k denotes the total number of explanatory variables. As a robustness analysis, a conventional panel quantile regression with bootstrapped standard errors was also estimated to verify the consistency of the MMQR findings.

Panel causality test
The direction of causal relationships among renewable energy capacity, ecological policy performance, greenhouse gas emissions, eco-friendly technology, and Green GDP was examined using the Dumitrescu–Hurlin panel Granger non-causality test44. This approach accommodates cross-sectional dependence and heterogeneous panel structures while identifying whether causal relationships are unidirectional or bidirectional.

The panel causality analysis complements the cointegration and MMQR analyses by distinguishing between variables that drive renewable energy capacity and those that respond to changes in renewable energy deployment. Establishing causal direction provides additional evidence to inform policy prioritization and the sequencing of renewable energy interventions.

All statistical analyses were performed using statistical analysis software. The analytical sequence, from diagnostic testing and cointegration analysis to quantile regression and panel causality testing, provides a coherent and reproducible framework for evaluating the determinants of renewable energy capacity across OECD countries.

Results

Descriptive statistics
The distributional characteristics of the study variables were examined before econometric estimation. Table 2 summarizes the descriptive statistics for the log-transformed variables across 21 OECD countries during 2000–2020. Renewable energy capacity (REC), ecological performance index (EPI), greenhouse gas (GHG) emissions, eco-friendly technology (EFT), and Green GDP exhibited substantial cross-country and temporal variation, with standard deviations exceeding one for all variables. REC ranged from −2.132 to 2.822, while Green GDP ranged from 11.990 to 14.411. Skewness coefficients indicated left-skewed distributions for REC, GHG emissions, and EPI, whereas EFT and Green GDP were positively skewed. Kurtosis values exceeded three for all variables, with REC displaying the highest kurtosis (7.66), indicating a leptokurtic distribution. The Jarque–Bera test rejected the null hypothesis of normality for REC, GHG emissions, EPI, and Green GDP (p < 0.01), whereas EFT did not significantly deviate from normality (p = 0.593). These findings support the use of quantile-based estimation methods that are robust to non-normal distributions.

Cross-sectional dependence and slope heterogeneity
Cross-sectional dependence and slope heterogeneity were assessed before estimating the panel models. As shown in Table 3, the Pesaran cross-sectional dependence statistics were significant for REC, Green GDP, EPI, GHG emissions, and EFT (all p < 0.01), rejecting the null hypothesis of cross-sectional independence. These results indicate that shocks occurring in one OECD country are transmitted across other countries, reflecting their economic integration and shared environmental policy frameworks. The Pesaran–Yamagata slope heterogeneity statistics (Δ̃ = 16.513; adjusted Δ̃ = 18.263) were also significant at the 1% level, rejecting slope homogeneity. Collectively, these findings justify the application of second-generation panel econometric techniques that account for cross-sectional dependence and heterogeneous slope coefficients.

Panel unit root analysis
The stationarity properties of the variables were evaluated using the Cross-sectionally Augmented Im–Pesaran–Shin (CIPS) panel unit root test. As presented in Table 4, REC, EPI, and EFT were stationary at level [I(0)], whereas GHG emissions and Green GDP became stationary after first differencing [I(1)]. The absence of second-order integrated variables [I(2)] supports the use of the Panel ARDL bounds testing approach to investigate long-run relationships among the variables.

Panel cointegration analysis
Long-run equilibrium relationships among renewable energy capacity, EPI, GHG emissions, EFT, and Green GDP were examined using the Panel ARDL bounds test. Table 5 shows that the calculated F-statistic (7.845) exceeded the upper critical bound at the 1% significance level, rejecting the null hypothesis of no long-run relationship. These findings confirm the existence of a stable long-run equilibrium among the variables and support subsequent estimation of long-run effects using quantile regression techniques.

Method of moments quantile regression
The heterogeneous effects of the explanatory variables across the conditional distribution of renewable energy capacity were estimated using the Method of moments quantile regression (MMQR). The results are presented in Table 6.

EPI exhibited a positive and statistically significant association with REC across all quantiles, with the estimated coefficient increasing from 0.092 at the 10th percentile to 0.212 at the 90th percentile, indicating a progressively stronger policy effect as renewable energy capacity increased. Similarly, GHG emissions displayed a positive association with REC, with coefficients increasing across the quantiles, suggesting that countries with higher renewable energy capacity responded more strongly to emission pressures. EFT demonstrated the largest positive increase across quantiles, indicating that technological innovation contributed more substantially to renewable energy deployment in countries with greater renewable energy capacity.

Green GDP showed a consistently negative association with REC across all quantiles, although the magnitude of the negative coefficient declined from −0.348 at the 10th percentile to −0.138 at the 90th percentile, indicating that the negative association weakened as renewable energy capacity increased. Overall, the MMQR results demonstrate substantial distributional heterogeneity in the effects of environmental policy performance, technological innovation, greenhouse gas emissions, and Green GDP on renewable energy capacity.

Figure 1 illustrates the heterogeneous effects of Environmental Performance Index (EPI), greenhouse Gas (GHG) emissions, eco-friendly technology (EFT), and green GDP on renewable energy capacity. EPI, GHG emissions, and EFT exhibit progressively stronger positive effects across higher quantiles, while the negative effect of Green GDP weakens as the conditional quantile increases (i.e., coefficients become less negative). Shaded areas (or error bars) represent the 95% confidence intervals.

Panel causality analysis
The direction of causal relationships among renewable energy capacity, Green GDP, EPI, GHG emissions, and EFT was evaluated using the Dumitrescu–Hurlin panel Granger non-causality test. The results are summarized in Table 7.

Bidirectional causality was identified between Green GDP and REC, as well as between EFT and REC (all p < 0.01), indicating mutually reinforcing relationships between renewable energy capacity, environmentally adjusted economic performance, and technological innovation. Similarly, EPI and REC exhibited bidirectional causality, suggesting reciprocal interactions between environmental policy performance and renewable energy deployment. In contrast, no significant causal relationship was detected from GHG emissions to REC (p = 0.1078), whereas REC significantly caused changes in GHG emissions (p = 0.009), indicating a unidirectional causal relationship from renewable energy capacity to greenhouse gas emissions. These findings demonstrate that environmental policy performance, technological innovation, and Green GDP interact dynamically with renewable energy capacity, whereas increases in renewable energy capacity contribute to reductions in greenhouse gas emissions (Table 7).

DATA AVAILABILITY:
The datasets used in this study are publicly available through the Zenodo repository and can be accessed at: https://zenodo.org/records/21138237.

Factors Explanation and EstimationSourcesAnticipated Connection 
RECRenewable Energy Capacityhttps://www.stats.oecd.org---
EPIEcological Performance Indexhttps://www.stats.oecd.orgAssured 
GHG EmissionGreen House Gas Emissionhttps://www.stats.oecd.orgAssured 
EFTEco-Friendly Technologyhttps://www.stats.oecd.orgAssured 
Green GDPGreen GDP estimated at fixed USD 2015 rates World Bank---

Table 1: Definitions, measurement descriptions, data sources, and expected relationships of the variables used in the empirical analysis. Renewable energy capacity (REC) was used as the dependent variable, whereas the Ecological Performance Index (EPI), greenhouse gas (GHG) emissions, eco-friendly technology (EFT), and Green GDP were included as explanatory variables. Data were obtained from the OECD Statistics database and the World Bank.

RECEFTGHG EmissionEPIGreen GDP
Mean2.3456221.8654781.4740841.37964612.97532
Median2.3951561.8756941.4968671.43906812.89835
Max2.8219832.5212941.8501471.727414.41057
Min−2.1323881.520018−1.253283−1.79125211.99038
Standard Deviation1.4629891.2623671.2720831.3560781.636272
Skewness−2.4457471.178735−1.927887−1.7583351.681867
Kurtosis7.6571973.9696824.9615844.1542553.84369
Jarque-Bera644.6757***2.25658483.36192***91.78071***35.25837***
Likelihood 00.592564000

Table 2: Summary statistics for the study variables across 21 OECD countries during 2000–2020. The table reports the mean, median, minimum, maximum, standard deviation, skewness, kurtosis, and Jarque–Bera test statistics for the log-transformed variables.

Factors Testing stats P-value
Group A: CSD test
REC15.5220
Green GDP74.8530
EPI72.680
GHG Emission22.5430
EFT47.8960
Group B: Slope heterogeneity
FrameworksDelta_tildeModified Delta_tilde
116.513***18.263***

Table 3: Results of the Pesaran cross-sectional dependence (CD) test and the Pesaran–Yamagata slope heterogeneity test. Significant CD statistics indicate cross-sectional dependence among panel units, whereas significant Δ̃ and adjusted Δ̃ statistics indicate heterogeneous slope coefficients across countries.

VariableLevel (Trends & Constants)First Difference (Trends & Constants)Integration Order
REC−4.598***–I(0)
Green GDP−3.605−4.819***I(1)
EPI−4.539***–I(0)
GHG Emission−3.167−6.396***I(1)
EFT−4.858***–I(0)

Table 4: Results of the Cross-sectionally Augmented Im–Pesaran–Shin (CIPS) panel unit root test. Test statistics are reported for variables in levels and first differences, together with the corresponding order of integration used for subsequent panel econometric analyses.

Test StatisticValueSignificance LevelLower Bound I(0)Upper Bound I(1)
F‑statistic7.845***1%3.745.16
5%2.864.01
10%2.453.52

Table 5: Results of the Panel Autoregressive Distributed Lag (ARDL) bounds test for cointegration. The table reports the calculated F-statistic together with the lower and upper critical bounds at the 1%, 5%, and 10% significance levels used to assess the existence of a long-run equilibrium relationship among the study variables.

VariableQ0.10Q0.25Q0.50Q0.75Q0.90
EPI0.092***0.114***0.156***0.189***0.212***
GHG Emission0.048*0.053**0.067***0.071***0.082***
EFT0.131***0.148***0.173***0.195***0.221***
Green GDP–0.348***–0.271***–0.198***–0.156***–0.138***
Constant……………

Table 6: Method of Moments Quantile Regression (MMQR) estimates for renewable energy capacity across the 10th, 25th, 50th, 75th, and 90th conditional quantiles. The table reports estimated regression coefficients for the Ecological Performance Index (EPI), greenhouse gas (GHG) emissions, eco-friendly technology (EFT), and Green GDP. Asterisks indicate statistical significance (p < 0.05, *p < 0.01, **p < 0.001).

Null Speculation W-StatsZbar-StatsProbability
Green GDPREC5.8028911.68870
RECGreen GDP3.631755.771133.00E-07
EFTREC2.73115.759323.00E-07
RECEFT3.753625.550379.00E-07
GHG EmissionREC3.120043.744460.1078
RECGHG Emission3.115463.721570.009
EPIRECT.7832312.23640
RECEPI4.60676.586330

Table 7: Results of the Dumitrescu–Hurlin panel Granger non-causality test. The table presents the W-statistic, Z̄-statistic, and associated probability values used to evaluate the direction of causal relationships between renewable energy capacity (REC) and the explanatory variables. Significant probability values indicate rejection of the null hypothesis of non-causality.

Discussion

The present study examined the determinants of renewable energy capacity across 21 OECD countries during 2000–2020 by integrating environmental policy performance, greenhouse gas (GHG) emissions, eco-friendly technology (EFT), and Green GDP within a Method of Moments Quantile Regression (MMQR) framework. The findings reveal substantial heterogeneity in the conditional distribution of renewable energy capacity, indicating that the effects of environmental, technological, and economic factors vary with countries' stages of renewable energy development. These results extend previous studies by demonstrating that renewable energy transitions are characterized by stage-dependent rather than uniform responses.

The MMQR results show that Green GDP is negatively and significantly associated with renewable energy capacity across all conditional quantiles, although the magnitude of this negative relationship gradually weakens from the lower to the upper quantiles. Specifically, the coefficient increases from −0.348 at the 10th quantile to −0.138 at the 90th quantile, indicating that the adverse association becomes less pronounced as renewable energy capacity expands. This finding suggests that environmentally adjusted economic growth does not necessarily translate into immediate renewable energy investment, particularly in countries at earlier stages of the energy transition. One possible explanation is that improvements in environmental efficiency may initially arise from energy conservation measures, industrial restructuring, or cleaner production processes rather than from additional renewable energy deployment. This finding supports the temporal asymmetry reported by Behera et al.45, whereby greener economic performance may temporarily reduce the incentive for additional renewable energy investment during the early stages of structural transition. The gradual weakening of the negative coefficient across higher quantiles suggests that this relationship becomes less restrictive as renewable energy systems mature.

Environmental policy performance, measured by the Environmental Performance Index (EPI), exhibits a positive and statistically significant effect across all quantiles, with coefficients increasing steadily from 0.092 at the 10th quantile to 0.212 at the 90th quantile. These results indicate that environmental policy becomes increasingly effective in promoting renewable energy capacity as countries progress through the energy transition. Rather than exerting a uniform influence, stronger environmental governance appears to generate larger returns in countries with more developed renewable energy sectors, where supportive institutions, regulatory frameworks, and market conditions are already established. These findings are consistent with the Porter hypothesis, which argues that well-designed environmental regulations can stimulate innovation and encourage cleaner energy investments46,47. They also reinforce previous evidence that the effectiveness of environmental policies depends on institutional quality and implementation capacity48. Consequently, strengthening environmental governance remains an essential policy instrument for accelerating renewable energy deployment, particularly in countries with expanding renewable energy systems.

The results further demonstrate that GHG emissions are positively associated with renewable energy capacity across all quantiles, with the estimated coefficients increasing from 0.048 at the 10th quantile to 0.082 at the 90th quantile. This pattern suggests that countries experiencing greater environmental pressure tend to invest more heavily in renewable energy, and that this response becomes stronger among countries with higher levels of renewable energy capacity. These findings support the view that increasing emissions create stronger incentives for governments to pursue cleaner energy policies and expand renewable energy infrastructure49. However, the panel causality analysis indicates that causality runs from renewable energy capacity to GHG emissions rather than the opposite, implying that while emission pressures are positively associated with renewable energy expansion, they do not independently trigger renewable energy development. Instead, emissions reductions are achieved through the subsequent expansion of renewable energy capacity. This distinction highlights the importance of complementary policy interventions that translate environmental pressures into concrete renewable energy investments, consistent with previous evidence of nonlinear relationships between emissions and renewable energy development50.

Eco-friendly technology (EFT) demonstrates the strongest positive relationship with renewable energy capacity among the explanatory variables, with coefficients rising consistently from 0.131 at the 10th quantile to 0.221 at the 90th quantile. The increasing magnitude of the coefficients indicates that technological innovation becomes progressively more important as countries advance along the renewable energy transition. This finding suggests that technological capabilities reinforce existing renewable energy systems by improving efficiency, reducing deployment costs, and facilitating the integration of renewable technologies into national energy systems. Furthermore, the bidirectional causal relationship identified between EFT and renewable energy capacity supports the existence of a reinforcing innovation–deployment cycle, whereby technological innovation stimulates renewable energy expansion, while increasing renewable energy capacity further encourages innovation51. Although EFT was measured using environment-related patent applications, future studies may incorporate broader indicators, such as digital green finance and financial technology, to capture additional mechanisms by which innovation supports renewable energy development. Nevertheless, the consistently positive and increasing coefficients across the conditional distribution underscore the importance of sustained investment in research and development, innovation incentives, and international technology transfer for accelerating renewable energy deployment52.

Overall, the findings demonstrate that the determinants of renewable energy capacity exhibit clear distributional heterogeneity across OECD countries. Environmental policy performance, greenhouse gas emissions, and eco-friendly technology all exert progressively stronger positive effects as renewable energy capacity increases, whereas the negative influence of Green GDP gradually weakens across higher quantiles. These results highlight that countries with more advanced renewable energy sectors benefit more strongly from environmental policies, technological innovation, and responses to environmental pressures than countries at earlier stages of the transition. Consequently, the study extends conventional mean-based analyses by demonstrating that the effectiveness of these determinants depends on the stage of renewable energy development, emphasizing the need for policy frameworks that are tailored to countries' positions along the renewable energy transition pathway.

Several limitations should be acknowledged. First, the analysis is restricted to OECD countries and the period 2000–2020, which may limit the generalizability of the findings to developing economies and more recent policy environments. Second, eco-friendly technology is measured using patent applications and therefore does not capture emerging dimensions of digital green finance, financial technology, or private-sector innovation. Third, renewable energy capacity is examined as an aggregate measure without distinguishing among individual renewable technologies such as solar, wind, hydroelectric, or biomass energy. Future research should incorporate broader geographical coverage, more recent datasets, technology-specific renewable energy indicators, and measures of digital green finance and cross-market financial spillovers to provide a more comprehensive understanding of renewable energy transitions.

In conclusion, this study demonstrates that renewable energy capacity in OECD countries is shaped by heterogeneous effects of environmental policy performance, greenhouse gas emissions, eco-friendly technology, and Green GDP. Environmental policy and technological innovation consistently promote renewable energy capacity, with their effects becoming progressively stronger across higher quantiles, whereas Green GDP exhibits a negative but diminishing association that reflects short-run adjustment during the transition toward a greener economy. Panel causality analysis further reveals reciprocal relationships between renewable energy capacity, environmental policy performance, Green GDP, and eco-friendly technology, while renewable energy capacity exerts a unidirectional causal influence on greenhouse gas emissions. These findings highlight the importance of deployment-specific policy frameworks, sustained investment in green innovation, and complementary policy instruments that support long-term decarbonization. By integrating long-run equilibrium analysis, quantile regression, and panel causality testing within a unified analytical framework, this study provides new evidence that the renewable energy transition is fundamentally stage-dependent and that policy effectiveness varies according to the maturity of renewable energy deployment.

Disclosures

All authors declare no conflicts of interest.

Acknowledgements

This work is supported by the Philosophy and Social Science Research Project of Jiangsu Provincial Department of Education (2022SJYB2268).

Materials

List of materials used in this article
NameCompanyCatalog NumberComments
CIPS unit root testStataCorpStata 18Implemented using the xtcips or multipurt command for the Cross-sectionally Augmented Im-Pesaran-Shin panel unit root test.
Cross-sectional dependence (CD) testStataCorpStata 18Implemented using the xtcsd command to perform the Pesaran CD test.
Dumitrescu–Hurlin panel Granger non-causality testStataCorpStata 18Implemented using the xtgcause command for panel causality analysis.
Eco-friendly technology (EFT)OECD StatisticsN/AData on environment-related patent applications retrieved from the OECD Statistics database (https://stats.oecd.org/).
Ecological Performance Index (EPI)OECD StatisticsN/AData retrieved from the OECD Statistics database (https://stats.oecd.org/).
EViewsIHS Global Inc.Version 14Alternative statistical analysis software for panel econometrics.
Green GDPWorld BankN/AData retrieved from the World Bank DataBank (https://databank.worldbank.org/). Specifically, data on Adjusted Net Savings, which is used to calculate Green GDP.
Greenhouse gas (GHG) emissionsOECD StatisticsN/AData retrieved from the OECD Statistics database (https://stats.oecd.org/).
Method of Moments Quantile Regression (MMQR)StataCorpStata 18Implemented using the mmqreg command, which must be installed from the Statistical Software Components (SSC) archive.
Pesaran–Yamagata slope heterogeneity testStataCorpStata 18Implemented using the xttest3 or xthetero command.
RR FoundationVersion 4.6.0 or higherOpen-source alternative for statistical computing.
Renewable energy capacity (REC)OECD StatisticsN/AData retrieved from the OECD Statistics database (https://stats.oecd.org/).
StataStataCorpVersion 18Primary software used for all statistical analyses, including diagnostic testing, cointegration, quantile regression, and panel causality tests.
Westerlund panel cointegration testStataCorpStata 18Implemented using the xtwest command, which must be installed from the SSC

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Environmentrenewable energy capacityecological performance indexeco friendly technologygreen GDPmethod of moments quantile regression
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