Solar energy as a catalyst for green growth: A quantile regression analysis in G-20 economies

Abstract

The Group of Twenty (G-20) nations face a critical challenge in balancing economic growth, industrial development, and rising carbon emissions while advancing clean energy transitions. This study assesses how tourism, trade, industrialization, economic growth, and CO₂ emissions influence solar energy consumption in G-20 economies from 2000 to 2021. Using the method of moments quantile regression to capture heterogeneous sectoral impacts, the research reveals that tourism and carbon emissions positively and significantly influence solar energy adoption, reinforcing its role in sustainable development. However, it is negatively associated with trade and industrial output, pointing to structural or policy barriers within traditional sectors. The results vary across quantiles, highlighting that the influence of solar energy depends on the level of economic development and adoption capacity. These findings offer timely insights for policymakers aiming to design targeted, sector-specific renewable energy strategies that balance growth with environmental responsibility. This study aligns with Sustainable Development Goals 7 and 9, as outlined in the United Nations’ 2030 Agenda for Sustainable Development, by highlighting the role of clean energy and sustainable industry in transforming G-20 economies.

Keywords

Solar energy G-20 nations trade industrial output tourism

Introduction

The Group of Twenty (G-20) economies, representing 85% of global gross domestic product (GDP), 80% of trade, and two-thirds of the world's population, stand at the forefront of global sustainability challenges (International Energy Agency (IEA - n.d.), International Monetary Fund (IMF - n.d.), and World Bank). As engines of industrialization (IND), trade, and tourism, these nations face a critical dilemma: reconciling economic expansion with escalating environmental degradation driven by fossil fuel dependence (Ozparlak and Wang, 2025; Shobande et al., 2024). Solar energy, a rapidly scalable and abundant renewable resource, offers transformative potential to decouple growth from emissions (Bhattarai et al., 2022; Igliński et al., 2023; Reker et al., 2022). However, its adoption across key economic sectors, such as trade, tourism, and manufacturing, remains uneven and critically underexamined within the G-20's diverse economic landscape (Han et al., 2025). The G-20's unique mix of advanced and emerging economies, from industrial powerhouses (like Germany) to tourism-dependent nations (like Italy), offers a policy laboratory for global sustainability transitions. Their collective influence on climate governance (e.g. Conference of the Parties (COP) summits) underscores why solar adoption here is critical for planetary outcomes.

Moreover, solar energy is widely accessible and can be used anywhere in the presence of sunlight (Garraín and Lechón, 2023). However, the concept of transition to renewable energy sources is a smart way to combat climate change, and has been increasingly popular in recent discussions (Bhattarai et al., 2022; Igliński et al., 2023). The widespread use of fossil fuels has had detrimental effects on the environment and human life worldwide, including air pollution, increasing temperatures, deforestation, and desertification (Z. Li et al., 2023; W. Liu et al., 2023). Crucially, the potential for solar energy to transform key G-20 economic sectors, trade, tourism, and manufacturing, remains inadequately explored, hindering targeted policy. Understanding solar's role requires examining its interaction within the G-20's dominant sectors: burgeoning tourism reliant on infrastructure and transportation, and expansive industrial and trade activities driving growth and pollution.

Over the last century, the tourism sector has undergone a notable change, witnessing substantial growth and development. Tourism has grown into a significant worldwide sector as travel has become more accessible and reasonably priced, along with advancements in communication and transportation (Scarlett, 2021; Shang, Bi et al., 2023; Shang, Lian et al., 2023). Most importantly, the transition to renewable energy is essential to advancing a sustainable travel industry. Sustainable tourism objectives align with clean and abundant energy sources such as Solar, wind, and hydroelectric power. It relies heavily on nonrenewable resources and leaves a large carbon footprint, which can be reduced by integrating renewable energy solutions into its infrastructure (Thompson, 2022; Ye and Rasoulinezhad, 2023).

Additionally, every sector of the economy is stimulated by trade, but the industrial sector has grown significantly. Additionally, trade boosts the agricultural sector, particularly in both industrialized and non-industrialized countries. The influence of IND on pollution has been substantial. Numerous types of pollution in marine ecosystems have been caused by industrial activities, especially in the areas of manufacturing, transportation, energy generation, and mining (Mejjad et al., 2023). Heavy metals, poisonous compounds, pesticides, and industrial effluents are common chemical pollutants (Garg et al., 2022). Industrial pollution can also result from poor industrial waste disposal, particularly if the garbage contains toxic or non-biodegradable compounds. Industrial operations, particularly those in the oil and gas industry, have the potential to unintentionally release chemicals and oils into marine habitats (B. Zhang et al., 2019). Previous studies have shown that aquatic life, habitats, and the ecosystem's general balance can all be destroyed by an oil leak, which can destroy marine ecosystems (Kaiser, 2023; Kennish, 2019; Thakur and Koul, 2022). Industrial activities such as wastewater discharge and agriculture can contribute to eutrophication in marine environments.

IND leads to the construction of infrastructure, such as ports and coastal developments, which can damage or change ecosystems. The loss of important ecosystems such as coral reefs, seagrass beds, and mangroves can have a detrimental effect on marine ecosystems (Fanning et al., 2021; Nagelkerken et al., 2002). Climate change is exacerbated by greenhouse gas emissions from industrial processes, primarily the combustion of fossil fuels (Javed et al., 2023). Sea level rise, rising sea temperatures, and ocean acidification are just a few of the ways that climate change is having a major influence on marine ecosystems (Guinotte and Fabry, 2008; Hoegh-Guldberg et al., 2007). Furthermore, these alterations have the potential to harm coral reefs, alter marine species distribution, and upset the delicate equilibrium of marine ecosystems. Plastic manufacture, usage, and disposal are directly related to industrial operations. Inadequate recycling procedures and poor waste management can lead to plastic pollution of marine habitats (Rajmohan et al., 2019). The breakdown of plastics into microplastics, which can be consumed or build up in food chains, poses a danger to marine life and ecosystems (Mejjad et al., 2023). To overcome these problems, industries must invest in energy resources that create fewer pollutants and are non-toxic to the environment.

This study aims to examine how key economic and environmental factors, such as trade, IND, tourism, economic growth, and carbon dioxide (CO₂) emissions, influence solar energy consumption (SEC) in G-20 countries. By analyzing these interrelationships, the study seeks to identify the sectoral and macroeconomic conditions under which solar energy adoption can be enhanced to promote sustainable development.

The study tries to answer the following question:

RQ1. How do trade, IND, tourism, economic growth, and CO₂ emissions influence SEC in G-20 nations?

RQ2. How do the impact of trade, tourism, industrial activity, growth, and emissions on SEC vary across different levels of adoption in G-20 countries?

RQ3. Which influencing factors consistently support or limit solar energy uptake across varying levels of adoption in G-20 countries?

So, this study not only adds to the body of knowledge on the topic, but it also gives lawmakers useful information for finding ways to balance environmental protection with economic growth.

This study addresses the critical gap in understanding heterogeneous sectoral impacts by analyzing how the SEC influences trade, industrial output, tourism, and CO₂ emissions across the diverse G-20 economies. Utilizing annual data from 2000 to 2021 and the method of moments quantile regression (MMQR), we specifically capture how these relationships vary across different levels of solar adoption, providing granular insights missed by conventional methods. The findings offer actionable, sector-specific policy pathways for integrating solar energy to achieve sustainable development goals (SDGs) related to clean energy (SDG 7), climate action (SDG 13), and sustainable industry (SDG 9).

Literature review

A comprehensive and in-depth analysis of relevant literature has been carried out in the “Literature review” section. The goal of this thorough investigation is to carefully examine and analyze the main topics discussed in previous research on solar energy usage and its effects on trade, IND, pollution, and the tourism sector. To situate this study, we review the theoretical foundations underlying renewable transitions, followed by empirical studies linking solar energy to trade, tourism, and industry in G-20 economies. This section concludes by identifying key knowledge gaps.

Solar energy transitions in the G-20 context

Solar energy has quickly become a key component of the worldwide clean energy revolution. Growing awareness of climate change and the need to lessen reliance on fossil fuels are the main factors driving its increase, according to Güney (2022). Solar deployment is now much more feasible thanks to advancements in photovoltaic system technology, cost savings, and creative financing options (Yu et al., 2022). Particularly in high-impact economies, government policies and investment flows have further accelerated the rise of solar energy (Benavides-Franco et al., 2023; H. Zhang et al., 2023).

The unequal adoption of solar technology is still a big worry in G-20 nations. The adoption rates, legislative support, and sectoral integration of solar energy vary greatly among nations, despite shared responsibility for global emissions and climate governance (W. Liu et al., 2023). The structural and institutional elements impacting photovoltaic transitions in various economic contexts are called into question by this disparity. A recent study by Shobande et al. (2024) highlights that energy transition is a structural shift tied to governance capacity, not just a matter of technology.

Solar energy and the tourism industry

The tourism sector stands to gain considerably from integrating renewable energy. Studies show that renewable energy, including solar, enhances sustainability branding, reduces operating emissions, and appeals to environmentally conscious tourists (Hailiang et al., 2023; Shang, Bi et al., 2023). Initiatives such as solar-powered accommodations and electric vehicle (EV) infrastructure further embed green practices in tourism ecosystems (Shan and Ren, 2023). According to Guo et al. (2023), such adoption also stimulates local job creation and community engagement.

Sustainable tourism, widely promoted under the SDGs, emphasizes ethical and eco-conscious practices that protect ecological and cultural assets (Paul and Roy, 2023; Yuedi et al., 2023). It relies heavily on clean energy transitions to reduce the sector's carbon footprint (Javed and Shamas, 2024). Studies argue that tourism's long-term profitability and resilience depend on such green transformations (Talwar et al., 2022).

Solar energy, trade, IND, and pollution

G-20 economies continue to rely heavily on trade and IND, yet these activities also greatly exacerbate environmental damage. Trade openness frequently raises energy consumption, which raises emissions, particularly in industries that rely heavily on fossil fuels, according to earlier research (Hakimi and Hamdi, 2016; Shahbaz et al., 2013). Trade openness is a key determinant of IND and economic progress in Africa, according to Mignamissi and Nguekeng (2022). Through indirect resource exploitation as well as direct emissions, IND adds to environmental concerns (Garg et al., 2022; Y. Li et al., 2024; Mehmood et al., 2023).

To counteract its environmental costs, trade can also encourage the diffusion of green technologies (Aslam et al., 2021). This dichotomy is especially pertinent in G-20 countries, where climate pledges and trade liberalization coexist. According to recent research on plastic trash trade networks by X. Liu et al. (2022), trading can either increase or decrease pollution, depending on recycling methods and policies.

Solar technologies provide a greener alternative to typical energy systems, which rely on infrastructure that is largely made of plastic. These alternatives lower emissions and the secondary need for materials that are high in pollutants. This change is particularly critical in industrial areas where pollution has caused long-term climatic consequences, hazardous waste discharge, and harm to marine ecosystems (Mejjad et al., 2023; Rajmohan et al., 2019; B. Zhang et al., 2019).

Literature gap

Despite growing research on renewable energy, existing studies often treat solar adoption in isolation and overlook sectoral and quantile-specific effects. The interlinked dynamics of trade, tourism, IND, and pollution, especially in the diverse G-20 context, remain underexplored. Prior work rarely incorporates advanced distributional techniques such as MMQR, which capture heterogeneity in impact across levels of solar consumption.

Moreover, few studies offer an integrated framework that combines theoretical insights from the environmental Kuznets curve (EKC), ecological economics, and sustainable finance to explain sectoral variation in solar impacts. The current study addresses these gaps by applying MMQR and Driscoll-Kraay standard errors to explore these dynamics across various economic levels, to uncover quantile-based effects, and drawing on a multi-theoretical framework to explain how solar energy interacts with economic and environmental outcomes across the G-20.

This approach contributes to both academic and policy discourses, offering granular insights for targeted interventions that support the SDGs, especially SDG 7 (affordable and clean energy), SDG 9 (industry, innovation, and infrastructure), and SDG 13 (climate action).

Theoretical framework

This study is grounded in three interconnected theoretical frameworks that shape our understanding of the relationships between solar energy adoption and key economic-environmental variables. First, the EKC hypothesis (Dinda, 2004) suggests that as economies industrialize, environmental degradation initially increases due to fossil-fuel-intensive growth. However, after reaching a certain income threshold, cleaner technologies such as solar energy are more likely to be adopted, reducing emissions. Thus, in the early stages of IND, resistance to solar adoption is expected.

Second, ecological economics (Daly, 1992, 2000) conceptualizes the economy as embedded within environmental limits. This framework emphasizes solar energy as essential to decoupling economic activity from biophysical constraints, especially in high-impact sectors like manufacturing and tourism. Adoption of solar energy under this lens represents not just a technological shift but a structural change toward long-term sustainability. Third, sustainable finance theory (Shobande et al., 2024) highlights how targeted green financial instruments, such as carbon pricing and green bonds, can correct market failures and accelerate the renewable energy transition. Additionally, recent empirical studies further support these theoretical underpinnings. For instance, (Shobande and Enemona, 2021) emphasize the role of sustainable finance in addressing resource-driven environmental challenges, while (Shobande and Asongu, 2021, 2023) provide evidence that financial development, human capital, and Information and Communication Technologies (ICT) can play critical roles in reducing CO₂ emissions and advancing green transitions, particularly in developing and transitional economies. These findings reinforce the importance of integrating green finance, technological innovation, and institutional capacity within G-20 economies to drive solar energy adoption and sustainable sectoral transformation. These mechanisms are particularly relevant in G-20 economies, where both public and private finance can shape the pace of clean energy deployment.

These theoretical foundations inform our variable selection and the expected directional relationships in the model. For IND, the EKC suggests a short-run negative association with solar adoption due to fossil fuel dependency during early industrial phases. In contrast, tourism activity is expected to exhibit a positive relationship, as ecological economics highlights the role of solar energy in enhancing sustainability branding and energy self-sufficiency within the tourism sector. Trade openness presents an ambiguous effect: it may facilitate the transfer of clean technologies and renewable energy imports, or alternatively, contribute to carbon leakage and intensify pollution through expanded industrial activity. Lastly, consistent with the EKC framework, CO₂ emissions are expected to decline as solar energy adoption increases, particularly when economies transition away from fossil fuels toward renewable energy systems.

Materials and methods

Data sources and description

Table 1 presents the study's key variables, corresponding symbols, units of measurement, and data sources. SEC is measured in megawatt-hours and sourced from the British Petroleum database. Trade is represented as an index, and the data is obtained from the IMF data. IND is also expressed as an index; however, its data source is not specified. Tourist arrivals (TAs) are reported as the number of TAs, with data sourced from the OECD. CO₂ emissions are measured in metric tons per capita and obtained from the World Development Indicators (WDIs). Finally, economic growth (GDP) is measured as GDP per capita in constant 2015 US$, but the source is not explicitly mentioned. Additionally, six predictor variables are included in the study: GDP, Trade, SEC, IND, TA, and CO₂. The variable SEC is used to represent the dependent variable.

Table 1.

Variables and sources.

Parameters	Symbol	Unit	Sources
Solar energy consumption	SEC	Megawatt-hours	British Petroleum database (Energy Economics \| Home, n.d.)
Trade	TRADE	Trade (% of GDP)	IMF DATA (IMF Data, n.d.)
Industrialization	IND	Industry (including construction), value added (% of GDP)	World Bank
Tourist arrivals	TA	No tourist arrivals	OECD (Better Policies for Better Lives \| OECD, n.d.)
Carbon dioxide emissions	CO₂	Metric tons per capita	WDI Indicators (WDI - Home, n.d.)
Economic growth	GDP	GDP per capita (constant 2015 US$)	–

Building on Hakimi and Hamdi (2016) and Aslam et al. (2021), this study employs carefully selected proxies to capture complex economic relationships. IND is measured using industry value added (including construction) as a percentage of GDP, sourced from the World Bank's World Development Indicators. This variable reflects the overall contribution of industrial activity to the economy, including manufacturing, mining, utilities, and construction, and is a standard proxy for IND in cross-country panel analyses. Although this indicator is a single measure, it captures structural economic transformation beyond simple output figures. Guided by the EKC framework, we expect an initial negative association with solar adoption due to fossil fuel path dependency in manufacturing ecosystems. For our central variable, SEC, measured in megawatt-hours (BP STATS), is prioritized over production data. This operationalization reflects actual deployment efficacy, avoiding distortions from cross-border electricity trade and grid transmission losses that plague production metrics (Güney, 2022). Theoretically, we anticipate asymmetric sectoral relationships: a positive tourism linkage through sustainability branding effects, but a negative industrial association due to retrofit challenges in existing manufacturing infrastructure. For trade, prior literature suggests ambiguous effects, potentially positive through clean technology transfer or negative via carbon leakage, necessitating empirical verification.

Empirical framework

To investigate the heterogeneous effects of economic and environmental drivers on SEC across G-20 nations, we adopt the MMQR introduced by Machado and Santos Silva (2019). This technique is particularly well-suited for panel data with cross-sectional dependence and distributional heterogeneity, common features in multi-country datasets. Traditional mean-based estimators such as OLS or FMOLS focus on average effects and often overlook important variations across the distribution of the dependent variable. MMQR overcomes this limitation by estimating relationships at different quantiles, allowing us to examine how factors like trade, tourism, and IND impact SEC in countries with low, median, or high adoption levels.

Furthermore, MMQR accommodates unobserved individual-specific effects, which are essential when analyzing panels with country-level heterogeneity. The method also demonstrates robustness to outliers and potential endogeneity, enhancing the reliability of estimates. This makes MMQR preferable over alternatives like system-GMM or fixed-effects quantile regression, especially when the panel is unbalanced or the time dimension is limited. Previous studies also used this method in their research work (Gu and Javed, 2025; Sun et al., 2025).

The foundational econometric model is articulated as follows:

{SEC}_{i t} = α_{i} + X_{i t}^{'} β + (δ_{i} + Z_{i t}^{'} γ) U_{i t}

(1)

In this equation, ${SEC}_{i t}$ represents the SEC for country i at time t; $X_{i t}^{'}$ represents a vector of explanatory variables, including TA, IND, Trade, CO₂, and GDP. $(δ_{i} + Z_{i t}^{'} γ)$ account for heterogeneous covariance effects; and $U_{i t}$ is the error term capturing random disturbances. To address heteroskedasticity and enhance the robustness of estimations, all variables are log-transformed.

Before proceeding with model estimation, it is essential to evaluate the stationarity properties of the panel dataset to prevent misleading or spurious regression results. To ensure robustness, we apply several panel unit root tests: the cross-sectional dependence (CD) test as proposed by Pesaran (2004, 2007), the cross-sectionally augmented Im, Pesaran, and Shin (CIPS) test, and the cross-sectionally augmented Dickey-Fuller (CADF) test developed by Levin et al. (2002). The general form of the unit root testing equation is given as follows:

Δ Y_{i t} = μ_{i} + γ_{i} Y_{i, t - 1} + \sum_{j = 1}^{p} ρ_{j} Δ Y_{i, t - j} + ε_{i t}

(2)

Here,

Δ Y_{i t}

represents the variable under examination and

μ_{i}

is the country-specific intercept. Whereas

ε_{i t}

denotes the error term. If non-stationarity is detected, first-differencing is applied to achieve stationarity.

To examine cross-sectional dependence among the G-20 countries, Pesaran's CD test is employed, as formulated below:

the CD = \frac{\sqrt{2 T}}{N (N - 1)} \sum_{i = 1}^{N - 1} \sum_{j = i + 1}^{N} {\hat{ρ}}_{i j}

(3)

{\hat{ρ}}_{i j}

denotes the correlation coefficient of residuals between cross-sectional units i and j. The presence of significant cross-sectional dependence highlights the need for employing panel quantile regression techniques, which are capable of accommodating such interdependencies across units.

To ascertain the existence of long-term relationships between SEC adoption and other study variables, the Westerlund and Edgerton (2008) panel cointegration test is conducted, as represented by the following equation:

Y_{i t} = α_{i} + X_{i t}^{'} β + (δ_{i} + Z_{i t} γ) U_{i t}

(4)

In this expression, $α_{i}$ and $δ_{i}$ capture country-specific effects, while $U_{i t}$ accounts for short-term deviations. Confirming cointegration indicates that AI adoption and its determinants share a long-run equilibrium relationship, justifying the use of long-term estimation techniques.

Given the nonlinear and heterogeneous nature of SEC, the MMQR approach is implemented to estimate conditional quantiles while addressing outliers and asymmetric distributions. The MMQR model is specified as follows:

Q_{y} (τ / X_{i t}) = (α_{i} + δ_{i} (τ)) + X_{i t} β + Z_{i t} γ q (τ)

(5)

Here,

Q_{y} (τ / X_{i t})

represents the conditional quantile function of SEC adoption;

(α_{i} + δ_{i} (τ))

denotes the intercept capturing country-specific effects; and

X_{i t}

includes the explanatory variables TA, IND, Trade, CO₂, and GDP. The MMQR model is estimated by minimizing the following objective function:

\min_{q} Σ_{i} Σ_{t} ρ_{τ} (A I_{i t} - (δ_{i} + Z_{i t} γ) q)

(6)

ρτ(A) = (τ1) AI {A ≤ 0} + TAI {A > 0} is the assessment function, which may be obtained using equation (5). To ensure the reliability of our MMQR estimates, we conducted a robustness check using Driscoll-Kraay standard errors, which are designed to correct for cross-sectional dependence, autocorrelation, and heteroskedasticity in panel data. This method is particularly appropriate in settings where unobserved common shocks may affect all countries simultaneously, as is often the case in G-20 economies. By adjusting standard errors for these issues, the Driscoll-Kraay approach enhances the statistical precision of our results.

Results and discussion

The descriptive statistics table offers a comprehensive summary of essential data for G-20 countries as shown in Table 2. The average GDP is 2.045, with a standard deviation of 3.669, signifying sluggish economic development and considerable disparity among countries. The median GDP of 1.857 indicates that most nations exhibit less economic growth relative to the mean. The greatest GDP of 13.63 and the minimum of −11.84 illustrate considerable differences in economic success. The average IND score is 29.05, with a standard deviation of 9.314, demonstrating significant variability, seen by a maximum of 66.42 and a low of 16.39. The mean SEC is 0.096, exhibiting a pronounced skewness of 6.273, indicative of a significantly right-skewed distribution. This is evidenced by a maximum consumption of 3.080 and a minimum of 0.005, highlighting that a limited number of nations utilize substantially more solar energy than their counterparts. TAs have a mean of 4380 and a standard deviation of 5415, indicating considerable variability. The highest value of 2.18 × 10⁸ indicates extreme outliers in tourism, whilst the minimum value of 1828 reflects diminished visitor arrivals in some nations. The trade data has a mean of 51.85 and a standard deviation of 17.58, indicating variability in trade levels, with a maximum of 105.5 and a minimum of 19.55. CO₂ emissions exhibit a mean of 5.879 and a standard deviation of 4.805, with a high of 20.46 and a low of 0.799, indicating a variety in emissions among nations. The Jarque-Bera (JB) test findings demonstrate that all variables, except for trade, significantly depart from normality (p-value < 0.05), while SEC and TA exhibit extremely significant values (p-value 0.000), indicating non-normal distributions. The JB statistics for GDP, IND, and CO₂ are also noteworthy, signifying skewness or kurtosis in the dataset.

Table 2.

Descriptive statistics test.

	SEC	GDP	IND	TRADE	TA	CO₂
Mean	0.096	2.045	29.05	51.85	43800418	5.879
Median	0.010	1.857	26.95	51.75	21147000	4.312
Maximum	3.080	13.63	66.42	105.5	2.18E + 08	20.46
Minimum	0.005	−11.84	16.39	19.55	1828000	0.799
Std. Dev.	0.286	3.669	9.314	17.58	54154232	4.805
Skewness	6.273	−0.361	1.452	0.368	1.687	1.120
Kurtosis	51.53	4.430	5.238	2.863	4.780	3.429
Jarque-Bera	43770.16***	44.76***	234.2***	9.802***	253.66***	90.73***
Probability	0.000	0.000	0.000	0.007	0.000	0.000
Observations	418	418	418	418	418	418

SEC: solar energy consumption; IND: industrialization; TRADE: trade; TAs: tourist arrivals; CO₂: carbon dioxide; GDP: gross domestic product.

1%, 5%, and 10% are significant values, denoted by ***, **, and *, respectively.

The Westerlund cointegration test results for the G-20 nations in Table 3 indicate substantial correlations among certain variables. The GDP and trade (Gt) pair have a robust long-term correlation, evidenced by a significant statistic of −4.457 and a p-value of 0.000, suggesting that economic growth and trade are closely aligned. Likewise, SEC and trade (Pt) exhibit significant cointegration, with a statistic of −18.920 and a p-value of 0.000, indicating a positive temporal relationship between these variables. There is no cointegration between GDP and IND (Ga), as indicated by a p-value of 1.000, or between SEC and IND (Pa), with a p-value of 0.984, indicating an absence of long-term relationships between these pairings among the G-20 nations.

Table 3.

Westerlund cointegration test.

Statistic	Value	Z-value	p-value
Gt	−4.457***	8.914	0.000
Ga	−7.918	4.015	1.000
Pt	−18.920***	7.207	0.000
Pa	−7.506	2.144	0.984

1%, 5%, and 10% are significant values, denoted by ***, **, and *, respectively.

Table 4 shows highly significant CD-test results at the 1% level. All the factors are significantly dependent on each other across sections. As shown in the table, SEC, economic growth (GDP), trade, industry (IND), TAs, and carbon dioxide (CO) emissions exhibit strong connections between different groups across all countries. IND has a CD-test value of 63.69 and a correlation of 0.961, indicating a high level of dependence between the two variables. On the other hand, TAs have a CD-test value of 63.09 and a correlation of 0.952, indicating a global connection between the two variables. With a CD-test result of 18.28 and a correlation of 0.276, SEC also shows dependency. The variables are significantly dependent on each other; they share similar factors or trends across units. The strength of these connections underscores the importance of using more solar energy to promote economic growth, trade, development, environmentally friendly tourism, and sustainability.

Table 4.

CD test.

Variable	CD-test	p-value
SEC	18.28***	0.000
GDP	28.48***	0.000
TRADE	54.42***	0.000
IND	63.69***	0.000
TA	63.09***	0.000
CO₂	64.65***	0.000

SEC: solar energy consumption; IND: industrialization; TRADE: trade; TAs: tourist arrivals; CO₂: carbon dioxide; GDP: gross domestic product; CD: cross-sectional dependence.

1%, 5%, and 10% are significant values, denoted by ***, **, and *, respectively.

The CIPS test results for the G-20 countries demonstrate the stationary nature of all variables, as demonstrated in Table 5. The SEC, IND, trade, TA, and GDP are non-stationary at levels (I (0)), as their test statistics exceed the critical values for the 1%, 5%, and 10% significance levels, but achieve stationarity after the first differencing (I (1)), demonstrating significance at the 1% or 5% levels. The results suggest that the variables have temporal trends and require differencing to attain stationarity. This analysis gives a full look at the time-series features of the variables in our study, which is very important for making sure that econometric models are correctly defined.

Table 5.

CIPS test.

Variables	I (0)	I (1)
SEC	−1.441***	−1.364***
IND	−2.257***	−2.378***
Trade	−2.037 ***	−2.080**
TA	−1.846**	−2.013***
CO₂	−2.156***	−2.159***
GDP	−3.170**	−3.183***

CIPS: cross-sectionally augmented Im, Pesaran, and Shin; SEC: solar energy consumption; IND: industrialization; TRADE: trade; TAs: tourist arrivals; CO₂: carbon dioxide; GDP: gross domestic product.

1%, 5%, and 10% are significant values, denoted by ***, **, and *, respectively.

The MMQR results for the G-20 nations in Table 6 demonstrate diverse effects of multiple variables on SEC across distinct quantiles, offering insights into their interrelations and policy implications. These results directly address RQ1, which focuses on how trade, IND, tourism, economic growth, and CO₂ emissions influence SEC. At the 25th quantile, Trade exerts a negative impact on solar energy adoption, evidenced by a coefficient of −0.169 (p-value 0.045), indicating that nations with diminished trade volumes are more inclined to embrace solar energy. IND has a significant negative correlation (–0.761, p-value 0.000), suggesting that industrialized nations are disinclined to invest in solar energy. In contrast, TA demonstrates a positive association (0.196, p-value 0.001), showing that tourism-oriented countries prioritize the adoption of solar energy. CO₂ and GDP have weak negative correlations with solar energy adoption, with CO at −0.196 (p-value 0.003) and GDP at −0.022 (p-value 0.637). At the 50th quantile, trade exhibits a little positive impact (0.012, p-value 0.015), indicating that moderately trade-dependent nations are more predisposed to solar energy, but IND remains negative (–0.780, p-value 0.001). TA maintains a significant positive impact (0.281, p-value 0.000), while CO and GDP exhibit minor negative correlations with CO₂ at −0.152 (p-value 0.062) and GDP at −0.018 (p-value 0.748). These differences across quantiles demonstrate variation in how the same drivers affect solar energy adoption at different levels, thus directly responding to RQ2.

Table 6.

MMQR results.

log_SEC	Coefficient	Std.	Prob
Location
log_Trade	−0.110***	0.165	0.002
log_ind	−0.809**	0.335	0.016
log_TA	0.409***	0.104	0.000
log_CO₂	−0.006*	0.021	0.078
log_GDP	−0.012*	0.056	0.083
_cons	−7.328***	1.786	0.000
Scale
log_Trade	−0.263***	0.223	0.003
log_IND	−0.023**	0.181	0.028
log_TA	0.242***	0.088	0.006
log_CO₂	0.115**	0.101	0.017
log_GDP	0.011	0.072	0.879
_cons	−1.619	1.498	0.280
qtile__25
log_Trade	0.169**	0.145	0.045
log_IND	−0.761***	0.184	0.000
log_TA	0.196***	0.057	0.001
log_CO₂	−0.196***	0.066	0.003
log_GDP	−0.022	0.047	0.637
_cons	−5.906***	0.979	0.000
qtile_50
log_Trade	0.012**	0.080	0.015
log_IND	−0.780***	0.224	0.001
log_TA	0.281***	0.072	0.000
log_CO₂	−0.152**	0.081	0.062
log_GDP	−0.018	0.057	0.748
_cons	−6.472***	1.205	0.000
qtile_75
log_Trade	−0.288**	0.200	0.032
log_IND	−0.844**	0.499	0.091
log_TA	0.567***	0.160	0.000
log_CO₂	−0.004**	0.082	0.082
log_GDP	−0.005	0.128	0.966
_cons	−8.388***	2.676	0.002
qtile_90
log_Trade	−0.767***	0.578	0.002
log_IND	−0.815***	0.725	0.005
log_TA	0.886***	0.267	0.001
log_CO₂	0.161	0.310	0.603
log_GDP	0.003**	0.120	0.047
_cons	−10.52**	4.559	0.021

MMQR: method of moments quantile regression; SEC: solar energy consumption; IND: industrialization; TRADE: trade; TAs: tourist arrivals; CO₂: carbon dioxide; GDP: gross domestic product.

1%, 5%, and 10% are significant values, denoted by ***, **, and *, respectively.

At the 75th percentile for G-20 countries, trade and IND have negative impacts on solar energy adoption, with coefficients of −0.288 and −0.844, respectively, indicating that nations with significant trade dependence and industrialized economies are less likely to invest in solar energy. TA has a significant positive influence (0.567), suggesting that nations with vigorous tourism industries emphasize the deployment of solar energy. At the 90th quantile, trade and IND persist in exhibiting negative correlations with solar energy; however, TA remains the most potent positive influence, with a value of 0.886, indicating the substantial impact of tourism on solar energy uptake. At this quantile, CO₂ and GDP have little or negligible impacts on solar energy, indicating that environmental and economic issues are less impactful in these nations at elevated levels of adoption. These findings address RQ3 by identifying tourism as a consistent facilitator, while IND and trade as persistent barriers to solar energy adoption across different adoption intensities.

Overall, the MMQR results indicate complicated relationships between solar energy adoption and other parameters, including trade, IND, TAs, carbon emissions, and GDP, across various quantiles. Policymakers should tailor their strategies based on these findings, advocating for solar energy through programs that carefully consider national conditions, including industrial capacity, tourism levels, and the general economic framework. To visualize the distributional effects of the independent variables on SEC, Figure 1 presents a quantile regression coefficient plot. The figure highlights the heterogeneity in impacts across quantiles: tourism shows a consistently positive influence, while IND and trade exhibit stronger negative effects at higher quantiles. This further supports the quantile-based interpretation of RQ2 and validates the consistency of results tied to RQ3. These results reinforce the importance of sector-specific strategies for accelerating solar energy adoption in G-20 economies.

Figure 1.

Quantile regression estimates showing the distributional impact of Trade, Industrialization, Tourism, CO₂ emissions, and GDP on solar energy consumption across G-20 economies.

The Discroll-Kraay methodology was employed as a robustness test after the MMQR findings, validating the main conclusions. The Driscoll-Kraay findings emphasize many reasons that promote the use of solar energy in G-20 nations. The Driscoll-Kraay method results for G-20 countries, shown in Table 7, indicate that GDP exhibits a negative and statistically insignificant correlation with SEC (–0.012, p-value 0.918), suggesting that economic growth does not significantly influence the adoption of solar energy in these nations. Trade has a negative and statistically significant effect (–0.150, p-value 0.027), indicating that nations with elevated trade levels may be less inclined to embrace solar energy. IND has a significant negative impact (–0.809, p-value 0.005), indicating that industrialized nations are less predisposed to invest in solar energy. In contrast, TA demonstrates a positive and statistically significant correlation (0.409, p-value 0.012), suggesting that countries with elevated tourist activity are more inclined to prioritize the deployment of solar energy. CO₂ exhibits a negative yet significant impact (–0.082, p-value 0.039), indicating that nations with elevated carbon emissions may be less inclined to convert to solar energy. This study differs from those by Al-Mulali et al. (2015) and Ansari (2022), who found no significant link between using renewable energy and temporary CO₂ emissions in Vietnam and some ASEAN countries. The constant term (–7.328, p-value 0.000) implies that there are other factors influencing solar energy adoption beyond the variables considered in the model.

Table 7.

Driscoll-Kraay method.

log_SEC	Coefficient	Standard error	Prob
log_Trade	−0.150**	0.125	0.027
log_IND	−0.809***	0.258	0.005
log_TA	0.409***	0.148	0.012
log_CO₂	−0.082**	0.103	0.039
log_GDP	−0.012	0.122	0.918
_cons	−7.328***	1.302	0.000

SEC: solar energy consumption; IND: industrialization; TRADE: trade; TAs: tourist arrivals; CO₂: carbon dioxide; GDP: gross domestic product.

1%, 5%, and 10% are significant values, denoted by ***, **, and *, respectively.

Moreover, tourist locations with elevated visitor numbers exhibit a favorable correlation with solar energy adoption, indicating that these nations prioritize sustainability and green energy initiatives to attract environmentally concerned visitors. Moreover, despite the adverse effects of IND and trade, these nations possess substantial potential to engage in solar energy, particularly by transitioning from conventional energy sources. Our finding that tourism boosts solar adoption aligns with Shang, Bi et al. (2023), who attribute this to eco-tourism's market appeal. Conversely, industry's resistance mirrors observation of carbon lock-in in manufacturing is supported by the findings of Sadorsky (2014). Notably, solar energy's emission-reduction role validates the EKC hypothesis, suggesting G-20 economies are transitioning toward sustainable growth.

Conclusion

The research offers an extensive examination of the influences on solar energy uptake in G-20 countries, including diverse statistical techniques such as the Driscoll-Kraay method, MMQR, and cointegration tests. The results indicate that the adoption of solar energy in these countries is markedly affected by IND, tourism, trade, and carbon emissions. Industrialized nations typically exhibit an inverse correlation with solar energy adoption, largely attributable to their dependence on conventional energy sources and the difficulties inherent in shifting to renewable energy. In contrast, nations with increased visitor arrivals have a robust positive link with SEC, reflecting an escalating interest in sustainable energy solutions within their environmental and tourism objectives. Moreover, although trade-dependent countries exhibit a reluctance to engage in solar energy, there exists an opportunity to include green energy solutions to alleviate the environmental consequences of trade operations.

Trade can increase pollution, but also promote green technologies like solar energy. Development often leads to higher resource consumption, but renewable energy can decouple it from environmental harm. Tourism contributes to pollution, but can shift towards sustainability with solar energy adoption. Economic growth typically increases pollution, but integrating solar power allows for growth without proportional environmental damage. Pollution mainly stems from fossil fuels, but solar energy reduces emissions and environmental degradation.

Solar energy reduces the environmental impact of trade by decreasing emissions from transportation and energy-intensive industries. It also enables countries to produce and trade clean energy, facilitating a shift to a greener global market. By incorporating solar energy, trade can support economic growth while lowering carbon footprints. Solar energy boosts economic performance by lowering energy costs and creating job opportunities in the renewable energy sector. It also leads to environmental benefits by reducing CO₂ emissions and air pollution, supporting a cleaner energy grid and sustainable development.

The installation of solar energy plants in industries has led to a decline in pollution and more sustainable goals for the growth of the industry. By using solar energy, the cost of industry is almost reduced to half. Trade becomes cheaper, and it can cause energy cost reduction. It can help to stimulate industrial growth and reduce the production costs of manufacturing. Hence, solar energy is a source of sustainable industrial growth and declining pollution in developed and developing nations. However, solar energy can enhance industrial growth, attract more tourists, and create a pollution-free environment. Industries that choose solar energy are seen to be more sustainable and progressive, bringing environmentally conscious consumers.

The study emphasizes the significance of economic growth, indicating that GDP does not exert a substantial direct influence on solar energy adoption, implying that G-20 countries may not be constrained by economic factors in their transition to renewable energy. This implies that even lower-income or developing countries within the G-20 have an opportunity to leapfrog traditional energy infrastructure in favor of sustainable solutions like solar energy. The findings highlight the necessity of policy measures that emphasize solar energy investments, particularly in countries with significant industrial and commercial activity, as well as in tourism-dependent economies. Utilizing solar energy, G-20 nations may diminish their dependence on fossil fuels, alleviate carbon emissions, and advance global sustainability objectives.

Policy recommendations

This study reveals solar energy's asymmetric impacts across G-20 economies: tourism drives adoption, while industry/trade exhibits resistance. Solar deployment cuts emissions and energy costs, enabling green growth. However, these effects vary significantly across quantiles, underscoring the need for context-specific policies. The G-20 countries should think about the following policy suggestions to deal with the most important environmental problems:

Policy implications should prioritize balancing economic growth with environmental sustainability, focusing on integrating solar energy into the tourism and industrial sectors to reduce carbon emissions and reliance on fossil fuels. Investing in solar energy allows governments to diversify their energy portfolios, strengthen resilience against energy disruptions, and meet international climate commitments.

Considering the adverse impacts of trade and IND on solar energy use identified across many quantiles, governments ought to prioritize the advancement of renewable energy sources within these sectors. Facilitating the advancement of green technologies and renewable energy initiatives can mitigate the reliance on conventional energy sources, particularly in areas characterized by substantial trade volumes or vigorous industrial operations.

Tourism-driven economies such as Spain and Mexico, subsidize solar-powered hotels and airports.

Industrial economies like Germany and China, mandate renewable quotas for manufacturing zones.

All G-20 launch cross-border green finance initiatives (solar bonds).

Promote investments in solar energy infrastructure within developed nations to diminish reliance on fossil fuels and advance sustainable energy options.

Encourage the government to use solar energy in tourism-centric countries by merging renewable energy projects with eco-tourism and sustainable development methods.

Establish financial incentives, including tax credits or subsidies, for firms and people to embrace solar energy technology, especially in trade-dependent and industrial nations.

Facilitate research and development in solar energy technologies to enhance efficiency and decrease prices, hence increasing accessibility to solar energy for all G-20 nations.

Encourage international collaboration among G-20 nations to exchange best practices, technology, and expertise about solar energy adoption, benefiting both established and emerging economies.

Enhance public awareness and education on the environmental and economic advantages of solar energy to promote increased adoption at both individual and community levels.

Align solar energy policies with carbon reduction objectives to assist G-20 countries in achieving their climate commitments, therefore decreasing carbon emissions and promoting cleaner energy transitions.

Offer focused financial assistance to poor nations in the G-20 to bypass conventional energy infrastructure and embrace solar energy as a principal catalyst for economic and environmental sustainability.

Study limitations

This study is subject to certain limitations. First, due to data availability, it does not account for firm-level heterogeneity in the adoption of renewable energy technologies. The analysis relies on aggregated macro-level indices such as industrial development (IND), which may obscure sector-specific dynamics and adoption barriers. Consequently, important nuances related to technology diffusion, firm behavior, and policy responsiveness at the micro-level remain unexamined.

Future research directions

Future research should prioritize micro-level investigations into industrial decarbonization pathways to uncover firm-specific challenges and opportunities. Additionally, emerging financing mechanisms such as blockchain-enabled solar energy systems warrant deeper exploration. For instance, studies like (Shobande and Asongu, 2021) demonstrate how blockchain technologies can enhance transparency, traceability, and trust in solar energy financing. Integrating such innovative financial instruments into empirical models may offer new insights into accelerating clean energy adoption in diverse industrial contexts.

Footnotes

Acknowledgements

We thank the World Bank, BP STAT, IMF, and OECD for providing data on variables.

ORCID iDs

Jing Xu

Muhammad Adil Javed

Consent to participate

All the authors have participated, reviewed, and approved this research work for publication.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research on driving mechanism and promotion path of market-oriented allocation of scientific and technological innovation industry factors under the background of digital economy (No.22BJL140).

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The data can be obtained from the corresponding author upon reasonable request.

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