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. 2026 Jul 5;21:122. doi: 10.1186/s13021-026-00483-9

Can better institutions drive cleaner energy in the United States? insights from wavelet quantile regression

Qingzi Gu 1,2, Babatunde Sunday Eweade 3, Cathrine Banga 4,✉, Mohamed Djafar Henni 5
PMCID: PMC13637300  PMID: 42402089

Abstract

The transition to clean energy in the United States remains insufficient despite rising environmental concerns and increasing renewable energy adoption. This study investigates whether better institutional quality can effectively drive cleaner energy outcomes by examining the impact of governance alongside key macroeconomic factors. Using quarterly data from 1990 to 2024, the study employs a wavelet quantile regression approach to capture nonlinear and time-varying dynamics across short-, medium-, and long-run horizons. The findings reveal that economic growth, foreign direct investment, and trade openness positively influence renewable energy consumption, particularly over longer time horizons. In contrast, carbon emissions exhibit a negative relationship with renewable energy adoption. Surprisingly, institutional quality shows a predominantly negative effect, suggesting that stronger institutions may reinforce existing fossil fuel-based energy structures rather than accelerate transition. These results highlight the complexity of institutional roles in energy transformation and emphasize the need for targeted regulatory reforms to support renewable energy expansion in the United States.

Keywords: Renewable energy, Foreign direct investment, Institutional quality, Trade openness, Economic growth, Wavelet quantile regression

Introduction

The increasing severity of climate change and environmental degradation has made the transition to clean and sustainable energy systems a global priority [1, 2]. Greenhouse gas emissions, largely driven by fossil fuel consumption, continue to accelerate global warming and pose serious threats to ecological systems, human health, and economic stability [3, 4]. Despite substantial investments in renewable energy technologies worldwide, global carbon emissions remain at historically high levels, highlighting the urgent need to identify the factors that can effectively accelerate the transition toward cleaner energy systems1.

Recent evidence demonstrates both progress and persistent challenges in global decarbonization efforts. Globally, energy-related CO₂ emissions increased by 0.8% in 2024, reaching a record 37.8 gigatons (Gt), while atmospheric CO₂ concentrations rose to 422.5 parts per million (ppm), approximately 50% above pre-industrial levels2. Although emissions growth remained below global economic growth (3.2%), suggesting partial decoupling between economic activity and carbon emissions, the pace of renewable energy deployment remains insufficient to offset rising emissions. These trends underscore the need to better understand the institutional and macroeconomic conditions that facilitate clean energy transitions.

Among the factors influencing renewable energy development, institutional quality has emerged as a particularly important yet insufficiently understood determinant. Institutions—through governance effectiveness, regulatory quality, rule of law, and political stability—shape the policy environment and investment climate necessary for renewable energy expansion. Strong institutional frameworks reduce uncertainty, enhance regulatory efficiency, and encourage long-term investments in clean technologies. Existing empirical evidence supports the importance of institutions for environmental sustainability. For example [5], show that renewable energy, biocapacity, and economic complexity contribute positively to environmental sustainability, while financial development, economic growth, and population growth exert adverse effects. Similarly [6], demonstrate that lower perceived corruption significantly promotes public renewable energy investment, particularly in middle-income countries.

In addition to institutional quality, several macroeconomic and environmental factors influence renewable energy consumption. Economic growth affects both energy demand and investment capacity. Higher income levels may facilitate renewable energy adoption through greater financial resources and technological innovation, yet they can simultaneously increase overall energy consumption and emissions [7–12]. Foreign direct investment (FDI) contributes to renewable energy development through capital inflows and technology transfer, especially when supported by favorable institutional conditions [13, 14]. Trade openness can facilitate access to advanced clean-energy technologies and promote knowledge diffusion across countries [15–17]. Moreover, rising carbon emissions often stimulate renewable energy adoption by increasing environmental awareness and strengthening policy incentives for cleaner energy sources [9, 18, 19].

Despite the growing literature on renewable energy determinants, several important gaps remain. First, existing studies have predominantly employed cross-country analyses, providing limited insights into country-specific dynamics in advanced economies such as the United States. Second, conventional econometric approaches largely focus on average effects and therefore may overlook nonlinearities, asymmetries, and heterogeneous relationships that vary across different economic conditions and time horizons. Third, the joint influence of institutional quality and other critical drivers—including GDP, FDI, trade openness, and CO₂ emissions—has rarely been examined within a unified analytical framework. As a result, the complex mechanisms through which institutions interact with economic and environmental factors to shape renewable energy consumption remain insufficiently understood.

Addressing these gaps is particularly important in the context of the United States. As one of the world’s largest economies, major energy consumers, and significant carbon emitters, the United States plays a pivotal role in shaping global climate outcomes. The country possesses strong institutional capacity, advanced financial markets, and considerable technological innovation capabilities that can support clean energy transformation. However, it also faces persistent challenges, including high carbon emissions, continued dependence on fossil fuels, and fragmented energy policies across federal and state levels. This combination of strengths and challenges makes the United States an especially relevant case for examining whether institutional quality can effectively promote renewable energy consumption and support long-term sustainability goals. The present study contributes to the literature in three important ways. First, it provides a comprehensive country-specific analysis of the United States, addressing the limited evidence available for advanced economies. Second, it integrates institutional quality with key macroeconomic and environmental determinants—GDP, FDI, trade openness, and CO₂ emissions—within a single analytical framework, thereby offering a more holistic understanding of renewable energy drivers. Third, and most importantly, the study employs the Wavelet Quantile Regression (WQR) approach developed by [20], which enables the examination of dynamic relationships across different time scales and distributional conditions. Unlike conventional methods that estimate average effects, WQR captures nonlinear, heterogeneous, and time-varying dynamics, providing richer insights into how institutional and economic factors influence renewable energy consumption under different circumstances [21, 22]. These methodological and empirical contributions enhance the study’s relevance for policymakers seeking effective strategies to accelerate the clean energy transition. Against this background, the study addresses the following research questions: (i) Does institutional quality significantly influence renewable energy consumption in the United States? (ii) How do GDP, foreign direct investment, trade openness, and CO₂ emissions affect renewable energy consumption? (iii) Do these relationships vary across different time horizons and distributional conditions? To answer these questions, the study pursues three objectives: (i) To examine the impact of institutional quality on renewable energy consumption in the United States; (ii) To analyze the role of GDP, FDI, trade openness, and CO₂ emissions as key drivers of renewable energy consumption; (iii) To investigate the nonlinear and time-varying dynamics of these relationships using a Wavelet Quantile Regression framework. The remainder of the paper is organized as follows. Section "Literature review" reviews the relevant literature. Section "Data sources and methods" presents the data and methodology. Section "Findings and discussion" discusses the empirical findings. Finally, Sect. "Conclusion and policy recommendations" concludes the study and provides policy recommendations.

Literature review

Institutional quality and renewable energy

In a related study [23], analysed 27 EU countries (2000–2020) Using a combination of factor analysis and fixed-effects panel regression. They found that financial development significantly promotes renewable energy consumption. They further show that strong institutional quality enhances renewable energy adoption and improves its effectiveness in reducing carbon emissions. [24] examined the relationship between institutional quality and renewable energy consumption in 38 countries over the period 1990–2015. The study found that institutional quality positively influenced renewable energy consumption in the long run. Additionally, CO₂ emissions are shown to drive renewable energy adoption, while economic growth has a negative effect [25]. analyzed the effects of institutional quality and political risk on renewable energy consumption in 32 OECD countries from 1997 to 2019. Their findings showed that higher institutional quality and per capita income increase renewable energy consumption.

[26] examined the impact of economic policy uncertainty, institutional strength, and sustainable policies on renewable energy adoption in Nigeria. The findings indicate that short-term economic uncertainty has mixed effects, while strong institutions and sustainable policies consistently promote renewable energy use [27]. examined the effects of energy policy uncertainty, climate policy uncertainty, institutional quality, and ICT on ecological quality and renewable energy consumption in China over the period 2003M1–2022M10. Using advanced quantile-based time series methods, the study found that energy and climate policy uncertainties reduce ecological quality, while institutional quality and ICT improve it. The results further show that policy uncertainties and institutional quality increase renewable energy consumption.

[28] investigated the role of institutional quality in promoting renewable energy transition in MIKTA countries over the period 2000–2023. Using advanced panel econometric techniques, the study found that institutional quality significantly enhanced renewable energy consumption [29]. employed the moment quantile regression (MMQR) technique to examined the impact of renewable energy promotion on energy poverty across 36 Sub-Saharan African countries from 2000 to 2022. The findings show that renewable energy promotion reduces energy poverty; however, this effect is conditioned by the misery index and institutional quality.

Other drivers of renewable energy consumption

Recent studies have further examined the role of GDP in shaping renewable energy consumption. For instance [30], analysed the dynamic relationships among GDP, renewable energy, and other macroeconomic variables and found that economic growth promoted renewable energy use in the long run despite increasing emissions in the short run. Similarly [31], reported that GDP per capita positively influenced renewable energy consumption in MIKTA economies, while [32] showed that higher income levels increased renewable energy demand in OECD countries. In contrast [33], found that economic growth negatively affected renewable energy consumption, suggesting that the impact of GDP remains context-dependent.

Foreign direct investment had also been widely explored in recent literature [30] found that FDI increased carbon emissions when considered independently but contributed to environmental improvement when combined with renewable energy adoption, supporting both the pollution haven and halo hypotheses [34]. Likewise [35], reported that FDI had a shade impact, promoting growth but also contributing to emissions depending on regulatory conditions. In another study [16], observed that FDI exerted mixed effects on sustainability depending on whether investments were aligned with green practices.

Similarly, recent ASEAN-based evidence showed that the long-run benefits of FDI depended on institutional quality and policy frameworks [36]. Carbon emissions had also been identified as a key factor influencing renewable energy consumption [37]. found that GDP growth increased CO₂ emissions in the short run but encouraged renewable energy adoption in the long run, indicating a transition effect. Similarly, Huang (2025) showed that renewable energy significantly reduced environmental pollution and mitigated the adverse effects of FDI and trade openness on emissions. In another study [38], confirmed that carbon emissions remained closely linked with energy structure and economic activities. Likewise [39], found that energy-related factors continued to drive emissions, reinforcing the need for cleaner energy transitions.

[40] examined the asymmetric effects of eco-innovation on carbon emissions inequality in G7 economies over the period 1990–2023. Using the method of moments quantile regression (MMQR), the study found that eco-innovation reduced emissions inequality in low-inequality contexts but produced uneven benefits in high-inequality settings, where gains were concentrated among wealthier groups [41]. found that environmental policies significantly mitigated the negative environmental impact of FDI on biocapacity in high-income countries. The study further showed that burden-based policies, such as taxes and fees, were more effective than subsidies in reducing adverse effects, emphasizing the role of strong policy frameworks in aligning FDI with sustainability goals.

[42] found that eco-innovation, environmental taxes, and renewable energy consumption significantly reduced carbon emissions and ecological degradation in OECD countries [43]. Used panel data from 30 cities in the Yangtze River urban agglomeration from 2000 to 2020, the study found that industrial rationalization and specialization agglomeration reduced carbon emissions, while industrial upgrading and diversification agglomeration increased them. The effects also varied across regions, time periods, and levels of economic development [44]. employed data from listed Chinese new energy vehicle manufacturing firms between 2006 and 2021, the study found that digital innovation significantly reduces regional carbon intensity. This effect operates through technological spillovers and industrial agglomeration, with stronger impacts in regions with higher digital innovation capabilities and in western China [45]. employed panel data from Asian countries between 1991 and 2020, the study found that financial development increases consumption-based CO₂ emissions, while green technological innovation significantly reduces them. Additionally, green innovation helps offset the environmental impacts of financial growth, whereas urbanization and trade openness have limited effects on emissions.

Theoretical framework and hypotheses development

This study is grounded in institutional theory, political economy of energy transition, and ecological modernization theory, which collectively explain how governance structures and macroeconomic dynamics influence renewable energy consumption. Institutional theory emphasizes that institutions—comprising formal rules, regulations, and governance systems—shape economic performance and environmental outcomes by reducing uncertainty, improving policy effectiveness, and strengthening regulatory enforcement [46, 47]. In the energy sector, strong institutional frameworks are expected to facilitate renewable energy development by creating a stable investment climate and ensuring the implementation of environmental policies. Additionally, governance-oriented perspectives argue that effective institutions enhance environmental sustainability through better policy coordination and enforcement [48].

However, the political economy of energy transition highlights that institutions may also reinforce existing energy systems. In economies with deeply embedded fossil fuel infrastructures, institutional arrangements may sustain carbon-intensive industries through subsidies, regulatory inertia, and infrastructure lock-in. This concept, often described as carbon lock-in, suggests that institutional strength does not automatically translate into cleaner energy outcomes [49]. In the United States, where strong institutions coexist with long-standing fossil fuel dependence and fragmented energy governance, institutional quality may exert complex and even counterproductive effects on renewable energy adoption.

Ecological modernization theory provides further insight by linking economic development and globalization to environmental improvement. According to this theory, economic growth promotes technological innovation and structural transformation, enabling a shift toward cleaner energy systems [50]. Similarly, globalization—through foreign direct investment and trade openness—facilitates the diffusion of advanced technologies and environmentally efficient production processes. The Environmental Kuznets Curve (EKC) hypothesis also suggests that environmental degradation may initially increase with economic growth but eventually decline as economies adopt cleaner technologies [51].

At the same time, carbon emissions represent the environmental cost of fossil fuel dependence and provide an indication of the extent to which an economy is locked into carbon-intensive energy systems. While rising emissions may generate pressure for cleaner energy transition, persistently high emissions often signal structural barriers that hinder renewable energy adoption. Based on these theoretical perspectives, renewable energy consumption is modeled as a function of institutional quality, economic growth, foreign direct investment, trade openness, and carbon emissions. Given the potential for nonlinear and time-varying relationships, this study employs a wavelet quantile regression framework to capture heterogeneous effects across different time horizons and distributional conditions.

Institutional theory suggests that strong institutions enhance environmental governance and support renewable energy transition through improved regulatory quality and policy enforcement [52]. However, the political economy perspective indicates that institutions may also reinforce fossil fuel dependence through regulatory inertia and path dependence. In the context of the United States, where fossil fuel systems remain deeply embedded, institutional quality may exert a constraining effect on renewable energy adoption.

Economic growth is expected to promote renewable energy consumption by increasing financial capacity, supporting technological innovation, and facilitating structural transformation toward cleaner energy systems. Ecological modernization theory and the EKC framework both suggest that higher income levels enable investments in sustainable energy technologies [50, 51]. Foreign direct investment contributes to renewable energy development by providing capital inflows, advanced technologies, and managerial expertise. According to globalization theory and the pollution halo hypothesis, FDI can enhance environmental performance through the transfer of cleaner technologies and production processes [51].

Trade openness facilitates access to renewable energy technologies and promotes knowledge diffusion across countries. Ecological modernization theory suggests that increased integration into global markets accelerates the adoption of cleaner technologies and enhances environmental efficiency [50]. Carbon emissions reflect the degree of reliance on fossil fuel-based energy systems. According to carbon lock-in theory, high emission levels are associated with entrenched energy structures that hinder renewable energy transition. Although environmental pressure may encourage cleaner energy adoption, persistent emissions often indicate structural constraints. Drawing from the above theoretical foundations, the following hypotheses are formulated:

H1

Institutional quality has a significant negative effect on renewable energy consumption in the United States.

H2

Economic growth has a significant positive effect on renewable energy consumption in the United States.

H3

Foreign direct investment has a significant positive effect on renewable energy consumption in the United States.

H4

Trade openness has a significant positive effect on renewable energy consumption in the United States.

H5

Carbon emissions have a significant negative effect on renewable energy consumption in the United States.

Data sources and methods

Data sources

The study examines the drivers of renewable energy consumption (REN) in the U.S., with particular focus on the roles played by foreign direct investment (FDI), economic growth (GDP per capita), carbon emissions (CO2), institutional quality (INQ), and trade openness (TRO). The data span the period from 1990 to 2024 and are transformed into quarterly frequency to address the issue of limited observations. Furthermore, some are variables are log-transformed to ensure normality and stabilize variance. The sources of the variables are presented in Table 1.

Table 1.

List of variables and sources

Symbols Variables Measurement(s) Sources
GDP GDP per capita constant US$ 2015 WB
FDI Foreign direct investment Net inflows (% of GDP) WB
REN Renewable Energy (% of total final energy consumption) OWID [53]
CO2 Carbon Emissions Per Capita OWID
INQ Institutional Quality Law and Order (Index) ICRG
TRO Trade Openness (% of GDP) WB
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Note: International country risk guide (ICRG); Our world in data (OWID); World bank dataset (WB)

Methodology structure

Wavelet Quantile Regression (WQR) integrates wavelet decomposition with quantile regression to capture multi-scale and distributional dynamics in time-series data. Wavelet analysis decomposes the series into frequency-based components corresponding to different time horizons-namely short-, medium-, and longrun dynamics. This approach improves upon the Quantile-on-Quantile Regression (QQR) framework proposed by [22], as it explicitly accounts for time-scale variations in relationships. As noted by [54], the correlation between two time series can differ significantly across time scales; thus, WQR is employed to overcome this limitation by jointly considering quantiles and time-frequency structures. The decomposition process begins by expressing the original series using wavelet filters. The first-level approximation and detail coefficients are given as:

graphic file with name d33e590.gif 1

To enhance the decomposition, modified filters obtained through dyadic up-sampling are applied iteratively across different levels Inline graphic, where Inline graphic. The approximation and detail coefficients at higher levels are computed as:

graphic file with name d33e604.gif 2

Here, the filters satisfy the conditions Inline graphic and Inline graphic, where Inline graphic denotes the up-sampling operator applied between successive decomposition levels [55]. After decomposing the series into multiple time scales, the WQR model is estimated for each component [20]. This allows the conditional relationship between the dependent variable  Y and explanatory variable  X to be analyzed at a given decomposition level  j and quantile  q. The model is expressed as:

graphic file with name d33e645.gif 3

where Inline graphic denotes the conditional quantile function, and Inline graphic and Inline graphic are quantile-specific coefficients [56]. By combining wavelet decomposition with quantile regression, WQR captures both time-frequency variations and distributional heterogeneity, making it suitable for identifying nonlinear and state-dependent relationships. This framework provides richer insights than conventional methods by simultaneously accounting for scale-dependent correlations and quantile-specific effects. Figure 1 provides a summary of the study’s analytical procedures.

Fig. 1.

Fig. 1

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Flowchart of analysis

Findings and discussion

Descriptive statistics

The descriptive statistics of InGDP, InFDI, REN, InCO₂, INQ, and InTRO are presented in Fig. 2. The results indicate that InGDP records the highest mean value (11.69), followed by REN (1.781) and INQ (1.338), while InFDI (0.225), InTRO (0.795), and InCO₂ (0.659) exhibit relatively lower mean values. This reflects the large economic size of the United States alongside moderate levels of trade openness and carbon emissions. The standard deviation results reveal that REN (0.606) is the most volatile variable, followed by InFDI (0.253), whereas InTRO (0.057) and InCO₂ (0.060) are the least volatile, indicating greater stability in these indicators. In terms of distribution, the skewness results show that InFDI (−0.288), InTRO (−0.145), and InCO₂ (−0.615) are negatively skewed, suggesting left-tailed distributions. Conversely, InGDP (0.084), INQ (0.536), and REN (0.327) are positively skewed, indicating right-tailed distributions. Furthermore, the kurtosis values indicate that InTRO (2.109) and InCO₂ (1.92) are relatively leptokurtic, while InGDP (1.488), InFDI (1.748), INQ (1.393), and REN (1.591) are platykurtic, implying flatter distributions. Finally, the Jarque-Bera (JB) statistics—InGDP (13.498), InFDI (11.075), InTRO (5.125), InCO₂ (15.632), INQ (21.772), and REN (14.075)—along with their corresponding low p-values (mostly < 0.05), confirm that all variables deviate from normal distribution.

Fig. 2.

Fig. 2

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Descriptive Statistics

Scatter plots and correlation analysis

Figure 3 presents the scatter plots and pairwise correlations among InGDP, InFDI, REN, InCO₂, INQ, and InTRO. The results reveal both positive and negative relationships across the variables, indicating varying degrees of association. The correlation coefficients show that InGDP is positively correlated with InFDI, InTRO, and INQ, suggesting that economic growth in the United States is associated with higher investment inflows, trade openness, and better institutional quality. In contrast, InCO₂ exhibits a negative relationship with REN, indicating that higher renewable energy consumption contributes to lower carbon emissions. Additionally, a strong negative correlation (− 0.745) is observed between INQ and REN, implying that improvements in institutional quality are associated with reductions in renewable energy usage (or vice versa). The scatter plots further confirm these relationships. Upward-sloping patterns are evident between InGDP and InFDI, InTRO, and INQ, reflecting positive associations, while downward-sloping patterns are observed between InCO₂ and REN, as well as between INQ and REN, confirming inverse relationships. Some plots also suggest mild non-linearity, indicating that the relationships may not be strictly linear. Overall, the results indicate moderate to strong associations among the variables, but not at levels that suggest severe multicollinearity. Therefore, all variables—InGDP, InFDI, REN, InCO₂, INQ, and InTRO—are suitable for inclusion in subsequent econometric analysis.

Fig. 3.

Fig. 3

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Scatter Plots and Correlation Analysis

Normality test (QQ plots analysis)

Figure 4 presents the Quantile–Quantile (QQ) plots for InCO₂, InFDI, InGDP, INQ, InTRO, and REN to assess the normality of the data. The results show that most variables deviate from the 45-degree reference line, indicating departures from normal distribution. Specifically, InFDI, InGDP, and InTRO exhibit points that are relatively close to the diagonal line in the central region but deviate at the tails, suggesting approximate normality with slight tail distortions. In contrast, InCO₂ and REN display noticeable deviations from the line, particularly at the upper and lower quantiles, indicating non-normality and the presence of skewness or outliers. Similarly, INQ shows clear step-like deviations from the reference line, reflecting limited variation and potential non-normal distribution. These patterns suggest that the data for INQ may be more discrete or clustered. Overall, the QQ plots confirm that the variables —InCO₂, InFDI, InGDP, INQ, InTRO, and REN — do not strictly follow a normal distribution, supporting the earlier Jarque-Bera test results. This implies that the dataset may contain skewness and kurtosis effects, which should be considered in subsequent econometric modelling.

Fig. 4.

Fig. 4

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QQ Plots Analysis

Wavelet-based unit root test results

Since this study employs wavelet-based methods, stationarity is examined on decomposed series rather than the raw data. The original variables are decomposed into multiple frequency components using the Maximal Overlap Discrete Wavelet Transform (MODWT), and the Augmented Dickey–Fuller (ADF) test is applied at each scale. Figure 5 presents the wavelet-based ADF results. The findings show that for all variables — InFDI, InGDP, InTRO, InCO₂, INQ, and REN—the estimated ADF statistics across all decomposition levels (D1–D5) lie below the 5% critical value. This leads to the rejection of the null hypothesis of a unit root at each scale. These results confirm that all variables are stationary across short-, medium-, and long-run frequencies, supporting the suitability of the data for subsequent wavelet-based time–frequency analysis.

Fig. 5.

Fig. 5

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WADF Outcome

Wavelet-based BDS test results

To further assess the dynamic structure of the series, the nonlinear characteristics of the variables are evaluated using the wavelet BDS test, following the approach of [57]. Figure 6 illustrates that the computed BDS Z-statistics for all variables—InFDI, InGDP, InTRO, InCO₂, INQ, and REN—consistently lie above the dotted black line representing the 5% critical value across all wavelet scales (D1–D5) and embedding dimensions. As a result, the null hypothesis of independent and identically distributed (i.i.d.) behavior is rejected for all variables. This finding confirms the presence of nonlinear dependence in the wavelet-decomposed components of InFDI, InGDP, InTRO, InCO₂, INQ, and REN, indicating that their dynamics are complex and cannot be adequately captured by purely linear models.

Fig. 6.

Fig. 6

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WBDS Outcome

Wavelet quantile regression

Building on the above framework, this study employs wavelet quantile regression (WQR) to effectively capture tail dependence structures. Figure 7 presents the results of the WQR analysis through heatmaps, where the estimated slope coefficients are represented by a color gradient ranging from light green to red. Panels (a–e) of Fig. 7 illustrate how improvements in institutional quality and other factors influences clean energy across different time horizons and quantiles in the United States. The right vertical axis reflects the magnitude of the estimated coefficients, while the left vertical axis denotes the time scales (short-, medium-, and long-run). The horizontal axis represents the distribution of quantiles. The results show a consistently positive relationship between REN and InGDP across all quantiles and periods (see Fig. 7a). The effect is strongest in the short- and medium-run, with slight weakening in the long-run at lower quantiles, indicating that economic growth promotes renewable energy adoption in the United States. This suggests that economic expansion enhances investment capacity and technological progress in clean energy. These findings resonate with recent studies, such as [58] and [59] which highlight the significant role of economic growth in driving renewable energy development.

Fig. 7.

Fig. 7

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Wavelet Quantile Regression plots between REN and InGDP, InFDI, InTRO, InCO₂, INQ. (a) WQR between REN and InGDP. (b) WQR between REN and InFDI. (c) WQR between REN and InTRO. (d) WQR between REN and InCO₂. (e) WQR between REN and INQ

The results indicate a generally positive relationship between REN and InFDI and across most quantiles and periods (see Fig. 7b). The effect is more noticeable in the medium- and long-run, while relatively weaker in the short-run, suggesting that foreign direct investment contributes to renewable energy development over time. This implies that inflows of foreign capital facilitate technology transfer, enhance investment in renewable infrastructure, and support the transition toward clean energy in the United States. These findings resonate with recent studies, such as [60, 61] which report that FDI significantly promotes renewable energy consumption through technological diffusion and financial development. The results reveal a predominantly positive relationship between REN and InTRO across most quantiles and periods (see Fig. 7c).

The effect is more noticeable in the medium- and long-run, while relatively moderate in the short-run, suggesting that trade openness supports renewable energy development over time. This indicates that increased trade openness facilitates access to advanced clean technologies, promotes international cooperation, and enhances the diffusion of renewable energy innovations in the United States. These findings resonate with existing studies, such as [10, 62] which highlight that trade openness plays a significant role in boosting renewable energy consumption through technology transfer and global market integration.

The results reveal a predominantly negative relationship between REN and InCO₂ across most quantiles and periods (see Fig. 7d). The negative effect is more noticeable in the long-run, while relatively weaker in the short- and medium-run, indicating that higher carbon emissions are associated with lower renewable energy adoption over time. This suggests that rising environmental degradation may initially hinder clean energy transitions, but over time, it reinforces the need for renewable energy as a mitigation strategy. These findings resonate with prior studies, such as [63–65], which document an inverse relationship between carbon emissions and renewable energy consumption, emphasizing the role of clean energy in reducing environmental pollution.

The results indicate a predominantly negative relationship between institutional quality INQ on renewable energy REN across most quantiles and periods (see Fig. 7e). The negative effect is more noticeable in the long-run, while relatively weaker in the short- and medium-run, suggesting that stronger law and order conditions are associated with lower renewable energy adoption over time. This may reflect that well-established institutional frameworks initially support existing conventional energy systems, potentially slowing the transition toward renewable energy. These findings resonate with prior studies, such as [66–68] which emphasize that institutional structures play a critical role in shaping energy consumption patterns and environmental outcomes.

Conclusion and policy recommendations

Conclusion

This study examines whether better institutions can drive cleaner energy in the United States, using renewable energy consumption as the dependent variable and quarterly data from 1990 to 2024 within a wavelet quantile regression framework. The findings reveal that economic growth, foreign direct investment, and trade openness generally promote renewable energy consumption, particularly over the medium and long term, by enhancing financial capacity and facilitating technology transfer. In contrast, carbon emissions exhibit a negative relationship with renewable energy, indicating that higher environmental degradation is associated with lower adoption of clean energy. The role of institutional quality is mixed, suggesting that stronger institutions do not automatically accelerate renewable energy development and may, in some cases, sustain existing energy structures.

Policy recommendations

Grounded in the wavelet quantile findings, the following targeted policy recommendations are proposed to enhance renewable energy consumption in the United States:

First, institutional reforms should move beyond general governance improvements and focus specifically on energy-sector regulatory redesign. The evidence that institutional quality may slow renewable adoption suggests regulatory inertia. Therefore, federal and state authorities should streamline permitting processes for renewable projects, reduce approval timelines, and revise legacy policies that implicitly favor fossil fuel infrastructure (e.g., subsidies and grid priority rules). Second, given that economic growth promotes renewable energy but also increases emissions, growth policies must be conditionally linked to clean energy deployment. This can be achieved by tying industrial expansion incentives and infrastructure spending to mandatory renewable energy quotas or clean energy integration benchmarks, particularly in high-emission sectors and regions identified in upper quantiles.

Third, since foreign direct investment shows stronger positive effects in the medium and long run, policymakers should target sector-specific FDI into renewables rather than general inflows. This includes establishing green investment corridors, prioritizing FDI in solar, wind, and storage technologies, and screening investments to ensure technological spillovers rather than carbon-intensive activities. Fourth, trade openness should be leveraged strategically by reducing tariffs and non-tariff barriers specifically on renewable energy technologies and components (e.g., solar panels, batteries, smart grid systems). Additionally, trade agreements should incorporate environmental clauses that facilitate clean technology transfer rather than broad liberalization without environmental safeguards. Fifth, the negative association between carbon emissions and renewable energy indicates the need for stronger price-based environmental instruments. Implementing a more binding and predictable carbon pricing mechanism—either through a federal carbon tax or expanded emissions trading—would directly incentivize a shift toward renewables, especially in high-emission quantiles where the effect is more noticeable.

Limitations and future studies

This study has some limitations that suggest directions for future research. First, the focus on the United States limits generalizability; future studies could use cross-country data for broader comparison. Second, the model does not explicitly capture structural breaks such as major policy changes or economic shocks, which could be incorporated in future analyses. Third, the use of aggregate indicators may hide sectoral differences; future research could examine specific renewable energy sources and more detailed institutional measures. Finally, incorporating variables such as technological innovation and environmental policies (e.g., carbon pricing) would provide deeper insights into the drivers of renewable energy consumption.

Acknowledgements

Nil.

Author contributions

GU - Conceptualization, BS - Methodological construction, Writing and Editing, CB - Writing and Editing, MD - Writing and Editing.

Funding

This Manuscript did not receive any funding from any organization.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval

This article does not contain any studies with human participants performed by any authors.

Informed consent

All authors have read and agreed to the published version of the manuscript.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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