Quantifying Environmental Degradation through Renewable Energy,Financial Development and Economic Sustainability: Insights from Panel Quantile Regression Analysis

Abstract

The degradation of the environment is a global concern that needs serious attention, including the environmental Kuznets curve (EKC) hypothesis. This article examines the effects of renewable energy, financial development and economic sustainability on the environmental quality of newly industrialized countries (NICs) from 1998 to 2021 in light of the increasing severity of environmental problems associated with industrialization. This study utilized different panel cointegration estimation techniques and panel quantile regression (PQR) estimates to obtain robust findings by examining the variance of each quantile. The results of the cointegration tests confirm the long-run relationship among the variables. Nevertheless, the outcomes from the PQR unveiled that renewable energy negatively and significantly influences CO₂ emissions in NICs, namely in the lower and middle quantiles (20th–50th). Financial development showed heterogeneity in the results of all the quantiles. It results in an increase in CO₂ emissions from the 70th to 90th quantiles in NICs. The EKC hypothesis is relevant to these findings as this study presents the economic sustainability index, which postulates that when NICs achieve sustainability, they give greater importance to environmental preservation and sustainability. This shift is characterized by reduced emissions throughout all quantiles, ranging from the 10th to the 90th. Economic sustainability corresponds to the falling part of the U-shaped curve of the EKC, wherein sustainability gets priority, resulting in reduced CO₂ emissions. Analogous results have been confirmed by comparing heterogeneous panel estimators; nonetheless, there was a significant variation in the intensity of their parameters. Moreover, the robustness analysis through quantile slope equality and symmetric quantiles tests proved legitimate results. The study’s findings offer policymakers with valuable policy recommendations.

JEL Codes: C1, F36, F43, O44, Q56

Keywords

economic sustainability financial development P-I-F-T model panel quantile regression renewable energy

Introduction

Environmental degradation is the process by which the natural environment’s condition and vitality decline due to diverse human activities (Adebayo & Kirikkaleli, 2021; Kaewsaeng-on & Mehmood, 2024). The phenomenon encompasses reducing, eradicating or modifying vital constituents of the natural surroundings, leading to adverse repercussions on ecosystems, biodiversity and the welfare of current and future populations. Environmental degradation, concerning carbon dioxide (CO₂) emissions, pertains primarily to the adverse consequences of CO₂ emissions on the ecological system (Anwar et al., 2022; Sharif et al., 2020; Wu et al., 2020). It is classified as a greenhouse gas, predominantly emitted into the atmosphere due to the ignition of fossil fuels, including oil, natural gas and coal, for transportation, industrial purposes and energy generation. CO₂ significantly exacerbates climate change (Mehmood & Kaewsaeng‐on, 2024a; Opoku & Aluko, 2021). Upon its release into the Earth’s atmosphere, this particular substance functions as a greenhouse gas, effectively capturing solar heat and inducing a phenomenon known as the greenhouse effect, subsequently leading to an increase in the Earth’s surface temperature. This phenomenon gives rise to several environmental repercussions (Rahman et al., 2021; Yilanci et al., 2024). Elevated CO₂ levels in the atmosphere have contributed to the escalation of global temperatures, inducing a more significant occurrence and intensity of heat waves, alterations in precipitation distribution and modifications in climate zones. However, it can cause disturbances within ecosystems, adversely affect agricultural activities and pose a significant risk to water resources (Anwar et al., 2021; Bilgili et al., 2021; Gupta, 2021).

In 2022, there was a 0.9% increase in global energy-related CO₂ emissions, amounting to 321 million metric tons (Mt). As a result, there was a notable increase, reaching an unprecedented peak of almost 36.8 billion metric tons (Gt). In 2022, emissions originating from the emerging markets and developing economies in Asia, excluding China, experienced the highest growth rate compared to other regions. These emissions witnessed a significant increase of 4.2%, equivalent to 206 Mt CO₂. More than 50% of the region’s rise in emissions can be attributed to generating electricity through coal combustion (IEA, 2022). Compared to it, renewable, clean or green energy comes from natural, replenishable sources. These sources are renewable because they refill on a human timescale and have a lower environmental impact than fossil fuels. Renewable energy sources help fight climate change and cut greenhouse gas emissions. It is relevant to mitigating the effects of CO₂ emissions and fostering environmental sustainability (Charfeddine & Kahia, 2019; Prempeh, 2024; Sharif et al., 2020).

The present study focuses on newly industrialized countries (NICs) distinguished by a period of swift industrialization and urbanization. This commonly entails a rise in manufacturing, building and infrastructure expansion, necessitating substantial energy utilization. This energy is often derived from fossil fuels, including coal, oil and natural gas, which emit CO₂ upon combustion. As a result, industrialization in NICs frequently results in increased CO₂ emissions (Opoku & Aluko, 2021; Rahman et al., 2021; Zeren & Hizarci, 2023). The selection of NICs is based on the assumption that these economies have experienced rising energy consumption as they pursue economic growth during the past 20 years. The financial progress often increases energy use, much of it from fossil fuels. CO₂ emissions from fossil fuel combustion cause climate change and air pollution. Establishing infrastructure, transportation networks and structures linked to economic progress can lead to the loss of habitats, deforestation and modifications to natural landscapes, all of which can contribute to the deterioration of the environment (Tamazian & Rao, 2010). Numerous research studies have examined the effects of financial development on environmental degradation (Chen & Lei, 2018; Wolde-Rufael & Mulat-Weldemeskel, 2022; Zeren & Hizarci, 2023; Zhu et al., 2016); however, these analyzes have been constrained by their focus on a single proxy or individual consequences. The current study has devised a financial development index to encompass its wide-ranging effects. However, the present study also follows the conceptual framework of economic sustainability, which means an economic system or activity can survive long without harming the environment, society or future generations. It entails balancing economic progress, social well-being and environmental protection. Economic sustainability is a broader perspective, and economic growth and development are its subsequent parts. Many studies focused on economic growth and development in the case of CO₂ emissions and environmental degradation (Chandia et al., 2018; Charfeddine & Kahia, 2019; Mondal, 2023; Radmehr et al., 2021; Rahman et al., 2021). The present study targets a novel idea to capture the effects of economic sustainability on environmental deterioration. However, the main objective of the present study is to develop a comprehensive framework for quantifying environmental degradation while considering renewable energy, financial development and economic sustainability in NICs. Moreover, the present study considers that phase of environmental Kuznets curve (EKC) when the U-shaped curve starts to fall, income levels continue to rise and economies move up to higher levels of development. This is called the falling part of the U-shaped curve. The logical reasoning behind the consideration of this specific part is that the present study is the pioneering effort to account for economic sustainability through the economic sustainability index rather than conventional economic growth or economic development measured through gross domestic product (GDP) or GDP square.

Based on the evidence and arguments presented above, the present study explores the linkage concerning renewable energy, financial development, economic sustainability and environmental degradation using numerous reliable and robust panel data estimations for NICs from 1998 to 2021. This study contributes to the empirical literature in the following avenues: First, this study introduces economic sustainability into the analysis of environmental degradation nexus for NICs, marking a novel contribution to the existing literature. The present work utilized the P-I-F-T model to assess economic sustainability. Furthermore, this model integrates population, inflation, final consumption expenditures and trade variables. Second, the present study considers financial development to account for its impact on CO₂ emissions. In contrast to the current body of work examining the finance–CO₂ emissions nexus, the present study incorporates the financial development index by considering three distinct components that broadly represent financial development. These components are broad money, domestic credit to the private sector and domestic credit to the private sector by banks. Third, there are contingent impacts of different measures on the response variable; however, the present study considers renewable energy along with the financial development index and economic sustainability index to account for the combined response that CO₂ could take from them in NICs. Fourth, in contrast to conventional econometric methods, the current study utilized panel quantile regression (PQR) estimations, which offer policymakers the ability to consider the effects of each quantile to make informed decisions. PQR is regarded as a cutting-edge method currently in use. A thorough understanding of the complex relationship can be attained by applying this methodology, which incorporates fixed effects. The utilization of this methodology makes it easier to set up links between heterogeneous components at multiple levels/ranges of quantiles. Furthermore, this research also employs several robustness tests to validate the consistency of the results across different methodologies. Last, NICs are working towards achieving United Nations (UN) Sustainable Development Goals (SDGs) 7 and 8. They aim to ensure universal access to affordable, sustainable energy (SDG 7) and promote inclusive and sustainable economic growth (SDG 8). Nevertheless, this study has profound implications for the concerned UN SDGs.

The current study structure follows: Section ‘Literature Review’ includes a comprehensive literature review, Section ‘Materials and Methods’ describes the data and methods employed, Section ‘Results and Discussion’ presents the findings and encourages discussion, and Section ‘Conclusion and Policy Implications’ concludes the study and underscores its policy implications.

Literature Review

This literature review delves into the multifaceted dimensions of environmental degradation, exploring the intricate interplay between renewable energy utilization, financial development and economic sustainability. By synthesizing findings from diverse scholarly works, this review aims to shed light on the complex mechanisms underlying environmental degradation across different economies and provide insights for policymakers, researchers and practitioners seeking to foster sustainable development pathways.

Renewable Energy and Environmental Degradation Nexus

Growing economies typically result in a greater demand for resources and energy, which can greatly stress the environment (Acheampong, 2019; Bhattacharya et al., 2017). Using renewable energy sources is a crucial approach to addressing and alleviating the impacts of climate change (Acheampong, 2019; Kaika & Zervas, 2013; Saqib, 2022). However, using renewable energy sources offers an essential position in minimizing the rapid progression of global warming by effectively reducing CO₂ emissions. This, in turn, helps to mitigate the severity of climate-related consequences, including but not limited to extreme weather events, rising sea levels and disturbances to ecosystems (Belaïd et al., 2021; Bhattacharya et al., 2017; Miao et al., 2022). The adoption of renewable energy is frequently linked to the implementation of energy efficiency measures (Dogan & Turkekul, 2016). Enhanced energy efficiency refers to the ability to do a specific activity with less energy input, diminishing reliance on fossil fuels and mitigating CO₂ emissions. Using renewable energy sources to reduce CO₂ emissions can yield additional advantages concerning public health (Adebayo & Kirikkaleli, 2021; Wu et al., 2020). The mitigation of pollution and subsequent improvement in air quality can potentially yield reduced healthcare expenditures and enhanced overall welfare. The relationship between the adoption of renewable energy sources and the emission of CO₂ is a crucial factor in the worldwide effort to address climate change (Bilgili et al., 2021; Chandia et al., 2018; Zhu et al., 2016). Renewable energy sources are concerned with impacting greenhouse gas emissions positively, promoting environmental sustainability and yielding other economic and health advantages (Chen & Lei, 2018; Wu et al., 2020). Nevertheless, the efficacy of this correlation is contingent upon the magnitude and rate at which renewable energy is embraced, as well as the more comprehensive framework encompassing policies and technologies. According to the study conducted by Shafiei and Salim (2014), the utilization of non-renewable energy sources is associated with a rise in CO₂ emissions. Conversely, adopting renewable energy sources was found to have a mitigating effect on CO₂ emissions. Charfeddine and Kahia (2019) found that Middle East and North Africa (MENA) countries have made limited progress in financial development and renewable energy, limiting their ability to improve environmental quality and economic growth. Mehmood and Kaewsaeng‐on (2024a) conclude that renewable energy is crucial in decreasing carbon footprints throughout a wide range of quantiles. However, the strength of these effects varies among different quantiles.

Financial Development and Environmental Degradation Nexus

The influence of financial development on CO₂ emissions is contingent upon contextual elements, including regulatory structures, policy choices and the sustainability focus of financial institutions (Ahmed et al., 2017; Tamazian & Rao, 2010). Financial development, suitable regulations and incentives are crucial in effectively supporting sustainable practices and clean energy, achieving emissions reduction goals, and mitigating adverse environmental effects (Amin et al., 2022; Salahuddin et al., 2015). The advancement of financial systems can facilitate the expeditious growth of infrastructure, including establishing roadways, skyscrapers and transportation networks (Hung, 2023; Opoku & Aluko, 2021). During the initial phases of industrialization and economic growth, there tends to be a heightened dependence on energy sources characterized by a significant carbon intensity, such as coal and oil (Acheampong, 2019; Ahmed et al., 2017). This phenomenon may lead to increased CO₂ emissions, especially in cases where nascent industries are not sufficiently motivated or regulated to embrace more environmentally friendly technologies (Dinda, 2004; Rani et al., 2023). Failure to prioritize sustainability in infrastructure projects may heighten energy demand and emissions, especially if reliant on fossil fuels (Mehmood & Kaewsaeng‐on, 2024a). Economic and financial development frequently encompass the augmentation of industrial activities, urban expansion and heightened energy utilization, all of which might engender elevated degrees of environmental deterioration, notably in CO₂ emissions (Prempeh, 2024; Sharif et al., 2020). In regions lacking modern energy resources, early financial development stages prioritize improving energy access, potentially leading to increased reliance on inefficient conventional biomass and worsening emissions, particularly in cooking and heating applications (Salahuddin et al., 2015; Shahbaz et al., 2016). The study conducted by Rani et al. (2023) establishes a U-shaped correlation between financial development and carbon emissions observed across different quantile groups. However, Kaewsaeng-on and Mehmood (2024) conclude financial development reduces carbon effects and improves environmental quality.

Economic Sustainability and Environmental Degradation Nexus

The concept of a U-shaped relationship within the framework of the EKC hypothesis pertains to a visual depiction of the association between environmental deterioration and economic development (Dinda, 2004; Yilanci et al., 2024). The proposition posits that during the early stages of economic development characterized by low-income levels, there is a tendency for environmental degradation to intensify; consequently, this results in the left-hand, upward-sloping portion of the U-curve (Miao et al., 2022; Prempeh, 2024; Rani et al., 2023). Nevertheless, as the economy undergoes additional growth and income levels experience an upward trajectory, environmental degradation gradually diminishes, giving birth to the descending right segment of the U-curve. This phenomenon has a U-shaped curve (Dogan & Turkekul, 2016; Halliru et al., 2020). The EKC hypothesis posits an economic theory that postulates a curvilinear association, precisely an inverted U-shaped pattern, between the level of economic development, commonly gauged by GDP per capita, and the extent of environmental deterioration. Considering EKC theory, it is common practice to elevate GDP to a higher power, such as squaring it, due to many underlying rationales (Rahman et al., 2021; Shafiei & Salim, 2014; Yilanci et al., 2024). Nevertheless, in the nascent phases of economic development, nations frequently emphasize industrialization, heightened energy utilization and economic activity that requires substantial resources. These activities can potentially result in increased pollution levels, deforestation and several other manifestations of environmental deterioration. Consequently, throughout the early stages of economic development, there exists a bias for exacerbating environmental degradation, resulting in the upward trajectory witnessed on the leftward side of the U-shaped curve (Rani et al., 2023; Shafiei & Salim, 2014).

Inflection point—the apex of the U-shaped curve exhibits a point of inflection, typically occurring at elevated income levels. At this critical juncture, a notable transformation occurs in the interplay between economic advancement and the deterioration of the natural environment. Nations may adopt more rigorous environmental legislation, allocate resources towards developing and adopting cleaner technology and shift towards businesses that are less reliant on finite resources. As a result of these efforts, environmental degradation decreases, pointing to the peak of the U-shaped curve (Miao et al., 2022; Saqib, 2022; Shafiei & Salim, 2014). The advanced stages of development, namely the falling phase of the U-shaped curve, are characterized by a further increase in income levels and the progression of economies towards higher levels of development. Consequently, there is a corresponding decrease in environmental deterioration (Kaika & Zervas, 2013). This phase corresponds to the descending portion of the U-shaped curve, characterized by a downward slope. In this context, nations often possess superior resources and capabilities to tackle environmental issues and effectively prioritize sustainability. The findings of Rani et al. (2023) reveal a U-shaped linkage between financial development and carbon emissions nexus. The study posits that adopting energy-efficient technologies and the increased involvement of the financial sector in globalization efforts can potentially augment environmental quality in nations belonging to the South Asian Association for Regional Cooperation (SAARC). At the same time, Zeren and Hizarci (2023) determined no evidence of cointegration or causality between energy conception, financial development and economic growth. Bilgili et al. (2021) considered 36 Asian nations from 1991 to 2017 and concluded the EKC hypothesis in the Asian context.

Upon reviewing the literature, the following gaps emerge: (a) There is a lack of consensus on EKC due to conflicting findings and proxy parameters used to measure environmental degradation. However, the contradictory results are due to underlying study periods, econometric methods and control variables. (b) Approximately every study focused on economic growth and development while explaining CO₂ emissions and environmental issues; however, there is a need to address economic sustainability issues, which is approximately entirely missed in the current literature. (c) The impact of financial development on the environment’s future is significant. Previous studies consider a single representative of financial development; hence, there is a need to evaluate the comprehensive and joint impacts of different proxies of financial development by creating its index to assess environmental aspects that have received the least attention in the literature. (d) Different studies concluded empirically the environmental issues, but the in-depth analysis concerning PQR is uncommon where the findings got their robustness across different quantiles.

Materials and Methods

Materials

The present study aims to contribute significantly to the empirical literature and intends to work on the following model, presented in Equation (1).

COE = f (REN, FINX, ESU)

(1)

The acronyms COE, REN, FINX and ESU correspond to the terminologies of CO₂ emissions, renewable energy, financial development and economic sustainability, respectively. However, the present study defines environmental degradation as the process of CO₂ emissions quantified in kilotons (kt). Renewable energy consumption is measured as a share of overall final energy consumption. Financial development is measured through a comprehensive financial development index. However, it is created to poll three proxies of financial development: Broad money (% of GDP), domestic credit to the private sector (% of GDP) and domestic credit to the private sector by banks (% of GDP). Economic sustainability is calculated through the P-I-F-T index to gauge economic sustainability using four components inspired by the studies of Hosan et al. (2022), Hung (2023) and Mehmood and Kaewsaeng‐on (2024a). Nevertheless, these components are population growth (annual %); inflation, consumer prices (annual %); final consumption expenditure (current USD); and trade (% of GDP). The rationale for choosing these variables as the indicators of economic sustainability is due to their direct and indirect impacts on various facets of the economy. Population growth influences labour markets, consumption patterns and resource utilization. Inflation and consumer prices affect price stability, purchasing power and the cost of living, crucial for economic confidence and welfare. Final consumption expenditure reflects household welfare, economic activity and resource allocation, serving as a key driver of GDP growth and societal well-being. Trade openness indicates global integration, competitiveness and resilience but also exposes economies to external vulnerabilities. These variables collectively provide insights into economic stability, resource management and policy effectiveness, making them essential for assessing and promoting economic sustainability (Hosan et al., 2022; Hung, 2023). Nevertheless, the principal component analysis (PCA) was employed in the development of the financial development and economic sustainability indices. Annual panel data from 1998 to 2021 for NICs—Brazil, China, Turkey, Indonesia, Malaysia, India, Mexico, Thailand, South Africa and the Philippines—were used for empirical research. The data were collected from The World Bank’s World Development Indicators, and the Z-score normalized the variables. However, Z-score calculation is a versatile tool that aids in standardizing data, making comparisons, identifying outliers and assessing relative positions within a population or data set (Altman et al., 2017). Table 1 provides a comprehensive summary of the data, with elevated kurtosis readings indicating the presence of outliers, exhibiting nonlinearity in the data set. Nevertheless, the Jarque–Bera estimates are sizeable, and the associated probability values are also considerable. This implies that the series being examined deviates from a normal distribution.

Table 1.

Descriptive Statistics.

Consideration	COE	REN	FINX	ESU
Mean	1,141,433.0	23.04174	–0.012978	0.029907
Median	369,387.8	20.68000	–0.305290	0.041945
Maximum	10,944,686	50.05000	2.872320	2.653670
Minimum	69,305.50	1.960000	–1.362820	–4.083590
Std. dev.	2,306,117.0	14.03644	0.990804	0.966751
Skewness	3.083638	0.340634	0.704809	–0.539315
Kurtosis	11.55713	1.819626	2.476895	6.227038
Jarque–Bera	1,066.239	17.80016	22.60661	115.7722
Probability	0.000000	0.000136	0.000012	0.000000

Note: COE, CO₂ emissions; ESU, economic sustainability; FINX, financial development; REN, renewable energy.

PCA

PCA stands as one of the most favoured dimension reduction techniques in multivariate data analysis due to its ability to distil complex data sets into more manageable forms while preserving essential information (Khan et al., 2020; Sun et al., 2021). In essence, PCA works by transforming a data set comprising numerous variables into a smaller set of uncorrelated variables, known as principal components (PCs) (Kaewsaeng-on & Mehmood, 2024; Shahbaz et al., 2016). These PCs are linear combinations of the original variables, ordered in a way that the first component captures the maximum amount of variation present in the data. Subsequent components capture decreasing amounts of variation, with each successive component being orthogonal to the previous ones. By retaining only the PCs that contribute significantly to the overall variance, PCA reduces the dimensionality of the data while preserving as much relevant information as possible. Moreover, by focusing on the PCs, analysts can identify underlying patterns, trends or relationships that may not be readily apparent in the original high-dimensional data, thereby facilitating insightful and informed decision-making processes. Thus, PCA serves as a powerful technique for uncovering the intrinsic structure of multivariate data sets and extracting meaningful insights from them (Bano et al., 2021; Khan et al., 2020). The rationale for utilizing financial development and economic sustainability indices stems from the need for more than a single metric to adequately encompass the multifaceted character of financial development and economic sustainability. This necessitates the utilization of an assortment of measures to effectively capture the influence of financial development and economic sustainability. Figure 1 demonstrates the scree plots for financial development and economic sustainability indices. However, eigenvalues represent the degree of variability associated with each variable. A higher eigenvalue indicates that the variable has a substantial role in the total variability of the data. Therefore, the basic indicator variables of this study are quantifiable and can be used to create indices.

Model Specification and Methodology

The current investigation focuses on quantifying environmental degradation, renewable energy, financial development and economic sustainability. In light of this objective, the statistical version of the present study is proposed in Equation (2).

{COE}_{i t} = β_{0} + β_{1} {REN}_{i t} + β_{2} {FINIX}_{i t} + β_{3} {ESU}_{i t} + ϵ_{i t}

(2)

Whereas i, t and $ϵ_{i t}$ denote cross sections, time and the error term, respectively.

Before doing an econometric analysis, the data undergo an examination for the presence of a unit root using the Im–Pesaran–Shin (IPS), augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests. Moreover, the cointegration tests explain a long-term relationship between the variables. The enduring association between variables is investigated using fully modified ordinary least square (FMOLS) and dynamic-OLS (DOLS) estimators suitable for cointegrated panels. The DOLS technique has demonstrated superior results in cases when cointegration is observed. However, it is essential to note that DOLS does not consider cross-sectional heterogeneity. The FMOLS method tacks many statistical obstacles encountered in econometric analysis. These issues encompass cross-sectional heterogeneity, serial correlation and endogeneity concerns (Anwar et al., 2021; Mehmood & Kaewsaeng‐on, 2024b). The current study utilizes PQR, a statistical methodology initially proposed by Koenker and Bassett (1978) and subsequently expanded upon by Koenker (2004). The PQR analysis method offers numerous advantages in comparison to OLS regression. This technique is highly effective in comprehending the interactions between variables, mainly when the data exhibit heteroscedasticity, outliers or varying effects across the conditional distribution (Bilgili et al., 2021; Zhu et al., 2016). PQR can estimate the distribution’s lower, middle and upper quantiles (Mehmood & Kaewsaeng‐on, 2024a; Rani et al., 2023). This method reduces the impact of extreme values on parameter estimates. However, the sequence of economic variables does not need to adhere to a normal distribution (Koenker, 2004; Opoku & Aluko, 2021).

Equation (3) represents the quantile regression version of the model.

y_{i, t} = α_{i} + β (q) x_{i t} + δ_{i t}

(3)

The vector x represents all exogenous variables, while the vector y represents the endogenous variable. The conditional distribution’s quantile is denoted by the letter q. Conversely, $α$ means the fixed  effects. The fixed effects $α_{i}$ remain unchanged, whereas the influence of the x drivers depends on the quantile q.

Putting it all together, a PQR model with fixed effects could be written as presented in Equation (4):

Q (y_{i t}| x_{i t}, α) = {\overset{´}{x}}_{i t} β_{q} + α_{i}

(4)

Whereas $Q (y_{i t}| x_{i t}, α)$ is the $q th$ conditional quantile of $y_{i t}$ , $x_{i t}$ is the transpose of the vector of independent variables for the $i th$ individual at time t. $β_{q}$ is a vector of coefficients for the quantile regression, and $α_{i}$ represents individual-specific fixed effects.

Equation (5) has been formulated to estimate several quantiles concurrently. The minimizing problem is addressed by following the guidelines provided by Koenker (2004).

{min}_{α β} = \sum_{k = i}^{ϑ} \sum_{j = i}^{ω} \sum_{i = 1}^{δ} w_{k} ρ_{k} (y_{i j} - α_{i} - β (q_{k}) x_{i j}^{'})

(5)

Implementing a penalty can be considered a means to regularize or reduce the variations in individual impacts to converge toward a standard value. The approach of PQR can be described as follows in Equation (6):

{min}_{α β} = \sum_{k = i}^{ϑ} \sum_{j = i}^{ω} \sum_{i = 1}^{δ} w_{k} ρ_{k} (y_{i j} - α_{i} - β (q_{k}) x_{i j}^{'}) + λ Φ (α)

(6)

Whereas $Φ (α) = \sum_{i = 1}^{ω} |α_{i}|$ is the penalty considered.

This study performed robust tests of PQR estimates using symmetric quantile (SQ) and quantile slope equality (QSE) tests. However, regression line slope similarity between groups or conditions is assessed using the QSE test. In contrast, the SQ test determines whether significant differences or the quantile of interest is the same for all groups or conditions (Bilgili, 2021; Kaewsaeng-on & Mehmood, 2024; Halliru et al., 2020).

Results and Discussion

The present study explains the empirical outcomes and their logical reasoning in this section. However, the descriptive statistics described in Table 1 allow us to account for the issue of unit root in the series considered for this study.

Testing of Unit Root

Researchers often combine data from various entities to study shared trends or relationships in panel data analysis (Mehmood & Bilal, 2021). It is critical to check each series for unit roots before pooling and estimation. Nevertheless, pooling non-stationary series can lead to biased and erroneous results (Charfeddine & Kahia, 2019; Chaudhry et al., 2013; Singh et al., 2023). This study performed three robust unit root tests before econometric analysis—the IPS, ADF and PP. The findings are displayed in Table 2, where the series are assessed using both a level and first differenced operator. Nevertheless, the results indicate that the variables exhibit non-stationarity in their level form but become stationary when differenced, except for the IPS test. The results suggest that the null hypothesis could be rejected almost entirely at the 5% significance level. Based on the findings presented, this study asserts that all variables exhibit stationarity when differenced once.

Table 2.

Unit Root Testing.

	I(0)			I(I)
Variables	IPS	ADF	PP	IPS	ADF	PP	Decision
COE	0.28887	16.1303	17.2233	–3.60712***	52.3202***	106.320***	I(I)
REN	2.85792	11.2576	10.7259	–1.09007	31.7913**	58.4654***	I(I)
FINX	2.33588	12.5385	24.5799	–5.50663***	69.3356***	143.323***	I(I)
ESU	4.52424	13.3603	17.7132	–5.87993***	76.9089***	161.381***	I(I)

Notes: *** and ** show significance at 1% and 5% significance levels, respectively. However, this criterion is also applicable in Tables 3, 6 and 7 as well.

Panel Cointegration Analysis

Based on the outcomes of the panel unit root test, which has verified that all variables exhibit stationarity at the I(I) level, it is now possible to proceed with the examination of cointegration. The current work utilized the Johansen–Fisher panel cointegration test, explicitly addressing the analysis of the I(I) integrated series (Chen & Lei, 2018; Zhu et al., 2016). Its results significantly rely on the vector autoregressive (VAR) lag-order system. However, Table 3 displays data with one lag, revealing seven cointegrating vectors, thus confirming cointegration.

Table 3.

Johansen–Fisher Panel Cointegration Test.

Tests	λ^¥ (Trace Test)	Prob.	λ^¥ (Max-eigen Test)	Prob.
r = 0	136.6***	0.0000	105.0***	0.0000
r ≤ 1	53.34***	0.0001	35.01**	0.0201
r ≤ 2	36.17**	0.0147	24.74	0.2116
r ≤ 3	46.94***	0.0006	46.94***	0.0006

Note: ^¥Asymptotic chi-square distribution calculates probabilities.

Panel Long-run Estimates

Panel co-integration tests are vital in determining the long-run relationship among the variables within a panel data set (Singh et al., 2023). However, the present study employed FMOLS and DOLS estimators to derive estimates of panel co-integration vectors and ascertain long-run relationships. To effectively implement the proposed solution, it is necessary to handle some formalities: (a) All variables should have a unit root and be stationary at the first difference. (b) Ensure long-term co-integration of all variables (Halliru et al., 2020; Rahman et al., 2021). After meeting these parameters, this study aimed to evaluate the long-term impact of renewable energy, financial development and economic sustainability on environmental degradation. Table 4 presents the results of panel long-run estimations using the FMOLS and DOLS methodologies. However, when examining the results of FMOLS, it becomes evident that renewable energy and financial development fulfil their intended roles in addressing environmental degradation. Nevertheless, as the renewable energy utilization increases, there will be a corresponding reduction in CO₂ emissions. Conversely, it has been observed that a definitive rise of 1% in the use of renewable energy sources is correspondingly linked to a reduction of approximately 0.1577% in CO₂ emissions. This supports the findings of Radmehr et al. (2021) and Sharif et al. (2020), who advocate for the utilization of renewable energy sources as a means to mitigate CO₂ emissions. The financial development is proving its positive impacts on CO₂ emissions. It could be inferred that a 1% increase in financial development could cause a 0.3775% rise in environmental degradation in NICs. The role of economic sustainability is considerable because its impacts are negative but are statistically insignificant, which means that economic sustainability negatively and insignificantly impacts the environmental degradation in NICs. In the case of DOLS, all three repressors are statistically significant, but the direction of causality is the same as that of FMOLS estimates. Financial development describes CO₂ emissions with a 10% significance level. Renewable energy and economic sustainability are both significant at the 5% level of significance.

Table 4.

Panel Long-run Estimates.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
FMOLS estimates
REN	–0.157767**	0.079632	–1.981217	0.0488
FINX	0.377546**	0.159321	2.369722	0.0187
ESU	–0.110493	0.095099	–1.161871	0.2466
DOLS estimates
REN	–0.218177**	0.086400	–2.525193	0.0130
FINX	0.184710*	0.102542	1.801314	0.0745
ESU	–0.196168**	0.083854	–2.339391	0.0212

Note: ** and * show significance at 5% and 10% significance levels, respectively.

PQR Estimates

The PQR with fixed effects by Koenker (2004) accounts for distributional heterogeneity. As indicated, typical time series studies without time-period fixed effects may be biased. This study emphasizes employing PQR with a fixed effect to improve statistical power, making this bias relevant (Chen & Lei, 2018; Bilgili et al., 2021). However, Table 5 shows PQR results on lower, middle and upper quantiles to identify the impact of renewable energy, financial development and economic sustainability on environmental degradation in NICs. Nevertheless, renewable energy, particularly at the 10th quantile, significantly and positively impacts CO₂ emissions. This implies that a 1% rise in renewable energy can increase CO₂ emissions in NICs. This outcome is permissible as it pertains to an initial stage in which the significance of renewable energy’s consequences begins to manifest. However, it can be deduced that the renewable energy negatively and significantly impacts CO₂ emissions in NICs, namely in the lower and middle quantiles. These findings indicate that a marginal increase of 1% in renewable energy is associated with a reduction in CO₂ emissions that varies from 0.005059 to 0.023459 in the 20th–50th quantiles. NICs frequently encounter decisions regarding their prospective energy composition. However, renewable energy can mitigate the increase in emissions and foster sustainability principles. In upper quantiles, renewable energy positively but insignificantly impacts CO₂ emissions. The initial adoption of renewable energy sources may have a more substantial impact on reducing CO₂ emissions, especially among NICs with lower initial levels of renewable energy use. These countries increase their renewable energy consumption, and the marginal benefit of emissions reduction may diminish, making the relationship weaker in the upper quantiles. Nevertheless, the results mentioned above align with the research conducted by Belaïd et al. (2021), Wolde-Rufael and Mulat-Weldemeskel (2022) and Mehmood and Kaewsaeng-on (2024b), which also shows heterogeneity in renewable energy outcomes.

Table 5.

Panel Quantile Regression (PQR) Estimates.

Consideration	Quantile	Quantile Levels	Coefficient	Std. Error	t-Statistic	Prob.
REN	10th	Lower quantiles	0.040541***	0.010006	4.051541	0.0001
	20th		–0.005059	0.008905	–0.568172	0.5705
	30th		–0.011813*	0.006906	–1.710652	0.0885
	40th	Middle quantiles	–0.021982***	0.005970	–3.682304	0.0003
	50th		–0.023459***	0.006390	–3.671226	0.0003
	60th		0.007706	0.029907	0.257658	0.7969
	70th	Upper quantiles	0.048780	0.044280	1.101617	0.2718
	80th		0.012265	0.044098	0.278125	0.7812
	90th		0.040429	0.047572	0.849844	0.3963
FINX	10th	Lower quantiles	0.026217**	0.012584	2.083336	0.0383
	20th		–0.004114	0.008907	–0.461879	0.6446
	30th		–0.006723	0.007789	–0.863121	0.3889
	40th	Middle quantiles	–0.008308	0.006922	–1.200264	0.2312
	50th		–0.000860	0.009771	–0.088020	0.9299
	60th		0.107662	0.130205	0.826861	0.4092
	70th	Upper quantiles	0.472232***	0.107619	4.387988	0.0000
	80th		0.584518***	0.045438	12.86418	0.0000
	90th		0.673226***	0.054856	12.27258	0.0000
ESU	10th	Lower quantiles	–0.034544**	0.015440	–2.237241	0.0262
	20th		–0.075490***	0.015589	–4.842650	0.0000
	30th		–0.079753***	0.015704	–5.078464	0.0000
	40th	Middle quantiles	–0.082003***	0.015494	–5.292520	0.0000
	50th		–0.088279***	0.017539	–5.033304	0.0000
	60th		–0.157811	0.110010	–1.434512	0.1527
	70th	Upper quantiles	–0.534101***	0.110602	–4.829026	0.0000
	80th		–0.659998***	0.034609	–19.07030	0.0000
	90th		–0.677836***	0.051063	–13.27440	0.0000
Constant	10th	Lower quantiles	–0.431685***	0.010199	–42.32640	0.0000
	20th		–0.381672***	0.006872	–55.53756	0.0000
	30th		–0.366819***	0.005998	–61.15692	0.0000
	40th	Middle quantiles	–0.349050***	0.005921	–58.95185	0.0000
	50th		–0.334427***	0.008628	–38.76080	0.0000
	60th		–0.208616	0.145135	–1.437398	0.1519
	70th	Upper quantiles	0.309779**	0.126399	2.450806	0.0150
	80th		0.534631***	0.054090	9.884134	0.0000
	90th		0.794856***	0.061782	12.86542	0.0000

Notes: Quantiles from 10th to 30th, 40th to 60th and 70th to 90th represent lower, middle and upper quantiles, respectively. ***, **, & * show significance at 1%, 5%, and 10% significance levels, respectively.

The findings of the study conducted by PQR indicate that there is a relationship between financial development and environmental degradation. Specifically, the impact of financial development on environmental degradation was found to be negative, but not statistically significant, throughout the 20th–60th quantiles. Financial development and emissions depend on environmental laws and regulations. Limited or lax environmental policies in lower quantile NICs may impair the relationship between financial development and emissions reduction. NICs are diverse and at different levels of development. Some NICs in the lower quantiles may still rely on carbon-intensive industries and fossil fuels, while others may be switching to cleaner energy sources.

Financial development and emissions in the lower quantiles may vary due to this diversity. The destined impact of financial development can be seen in upper quantiles where it positively and significantly impacts CO₂ emissions. This means that more financialization and carbon-intensive sectors may be more prevalent at the upper quantiles of NICs. Financial development can facilitate investments in heavy industries, boosting manufacturing, mining and fossil fuel extraction. However, this may contribute to increased carbon emissions, posing challenges to environmental sustainability. Increased access to cash and financial services can boost industrial growth and emissions. The results indicate that a 1% increase in financial development may result in a 0.472232–0.673226% increase in CO₂ emissions from 70th to 90th quantiles in NICs. In these quantiles, the results contradict the study of Shahbaz et al. (2013) and Mehmood and Kaewsaeng‐on (2024b), which concludes that financial development is a source of reduction in CO₂ emissions. However, the present findings are aligned with the outcome of Acheampong (2019), Amin et al. (2022) and Shahbaz et al. (2016), which endorses that the positive shocks generated by financial development in the banking sector contribute to the increase in CO₂ emissions and differ with and Zeren and Hizarci (2023), which supports the neutrality hypothesis, respectively.

In the case of economic sustainability, this study was focused on the falling phase of the U-shaped curve, characterized by greater income levels and economic sustainability; thus, environmental degradation decreases because our study considered economic sustainability rather than economic growth. This phase is the downward slope of the U-shaped curve. However, at the sustainability stage, environmental challenges are addressed. Our results reliably match this concept, and economic sustainability showed negative and highly significant impacts on CO₂ emissions in NICs. In the 60th quantile, the results follow the same pattern with negative impacts, but this is the only quantile from the 10th to the 90th, which is statistically insignificant. However, it could be inferred that with the 1% increase in economic sustainability, the CO₂ emissions will decrease with 0.034544, 0.075490, 0.079753, 0.082003 and 0.088279 in the 10th, 20th, 30th, 40th and 50th quantiles, respectively. The coefficient associated with the 60th quantile is also negative (–0.157811) but is statistically insignificant. However, in higher quantiles, it could be seen that the coefficients associated with economic sustainability have high negative values and are significant at a 1% significance level. It could be inferred that a 1% increase in economic sustainability causes a reduction in CO₂ emissions from 0.534101 to 0.677836 at the 70th–90th quantiles.

Economic sustainability pushes NICs to switch from carbon-intensive to greener businesses. As industries embrace cleaner technologies and processes, emissions may decrease. The EKC hypothesis relates to these results; initially, economic growth may lead to increased environmental degradation, including CO₂ emissions. However, as economies progress and attain sustainability, they prioritize environmental protection and sustainability. Reduced emissions and other forms of environmental degradation characterize this shift. Economic sustainability aligns with the latter phase of the EKC, where sustainability becomes a priority, decreasing CO₂ emissions. The present study negates the outcome of Rahman et al. (2021) and endorses Miao et al. (2022), specifically in the case of NICs and generally negates Acheampong (2019) and Dogan and Turkekul (2016).

Figure 2 displays the graphical depiction of Table 5, showcasing the various fluxes and impacts exerted by the significant ingredients of the current study on environmental deterioration. Nevertheless, estimates are attained through employing ten processes at a 95% confidence level. Quantile regression is a statistical technique that facilitates the identification and analysis of tail effects (Bilgili et al., 2021; Mehmood & Kaewsaeng-on, 2024a). The behaviour of the relationship can be discerned in the lower and upper tails of the distribution of the dependent variable. This particular method is very advantageous in comprehending extreme numbers and outliers. The utilization of visual representations in quantile regression analysis can effectively draw attention to the existence of outliers within the data set. Outliers possess the potential to exert a disproportionate influence on the findings of OLS regression. However, their impact on quantile regression estimates may need to be improved due to the latter’s emphasis on specific distribution segments. Quantile regression graphs depict the conditional effects of the regressors on environmental degradation over various quantiles. This allows for examining the relationship between variations in the regressors and corresponding variations in environmental degradation across multiple quantiles of the data set. The quantification of relationships can be accomplished by analyzing the slope of the quantile regression lines, thereby permitting the measurement of the amplitude and direction of correlations across different quantiles. Steep slopes are indicative of more pronounced impacts, while flat slopes are suggestive of less pronounced effects.

Notably, a prevailing pattern exists wherein environmental degradation tends to escalate throughout the initial phases of economic development. However, it is crucial to recognize that this correlation has the potential to transform as time progresses. As nations attain elevated levels of prosperity and witness the maturation of their financial systems, they frequently exhibit heightened consciousness regarding environmental concerns and demonstrate an increased propensity to allocate resources toward sustainable practices and technology. The reduction of CO₂ emissions is a crucial factor in the effort to address climate change and limit the resulting environmental degradation. Implementing this reduction is necessary to mitigate the far-reaching impacts of climate change on ecosystems, human health and general welfare. The configuration of the U-shaped relationship, the income threshold at which the inflection point occurs and the extent of environmental amelioration can vary depending on the environmental metric, regional or national policy frameworks, and cultural, technological and economic factors.

Robustness Tests

The present study utilized the QSE and the SQ as robustness tests to investigate the presence of heterogeneity in the outcomes to authenticate the PQR estimates concerning environmental degradation. These findings are presented in Tables 6 and 7. The robustness of these tests is assessed using estimates derived from the 10 quantile process estimations. Quantile coefficients for the QSE and SQ tests are compared using the Wald test statistic based on estimated equations. The findings of Wald’s QSE test are presented in Table 6, indicating a value of 1,054.162, which is statistically significant at the 1% level. This could endorse the heterogeneous findings to conclude environmental degradation at different quantiles. Wald’s SQ test results in Table 7 indicate that the estimates for the 10 quantiles process exhibit a 740.0124 value of x², which is also statistically significant at 1%. This corresponds to quantile asymmetry. Nevertheless, the results of both tests support the application of PQR modelling.

Table 6.

Wald’s Quantile Slope Equality Test.

Consideration		Chi-sq. Statistic	Chi-sq. d.f.	Prob.
Wald test		1,054.162***	24	0.0000
Restriction detail: b(tau_h) – b(tau_k) = 0
Quantiles	Variable	Restr. Value	Std. Error	Prob.
10th, 20th	REN	0.045601***	0.008275	0.0000
	FINX	0.030331***	0.009550	0.0015
	ESU	0.040946***	0.012780	0.0014
20th, 30th	REN	0.006754	0.005878	0.2506
	FINX	0.002609	0.006077	0.6676
	ESU	0.004262	0.010874	0.6951
30th, 40th	REN	0.010169**	0.004359	0.0196
	FINX	0.001585	0.005097	0.7558
	ESU	0.002251	0.009999	0.8219
40th, 50th	REN	0.001477	0.003778	0.6959
	FINX	–0.007448	0.005842	0.2024
	ESU	0.006275	0.010222	0.5393
50th, 60th	REN	–0.031165	0.028911	0.2810
	FINX	–0.108522	0.123352	0.3790
	ESU	0.069532	0.099784	0.4859
60th, 70th	REN	–0.041074	0.047934	0.3915
	FINX	–0.364570***	0.080979	0.0000
	ESU	0.376290***	0.073871	0.0000
70th, 80th	REN	0.036515	0.033568	0.2767
	FINX	–0.112286	0.086452	0.1940
	ESU	0.125897	0.092990	0.1758
80th, 90th	REN	–0.028164	0.038242	0.4614
	FINX	–0.088707**	0.042980	0.0390
	ESU	0.017838	0.039875	0.6546

Table 7.

Wald’s Symmetric Quantiles Test.

Consideration		Chi-Sq. Statistic	Chi-Sq. d.f.	Prob.
Wald test		740.0124***	16	0.0000
Restriction detail: b(tau) + b(1 – tau) – 2 × b(0.5) = 0
Quantiles	Variable	Restr. Value	Std. Error	Prob.
10th, 90th	REN	0.127888***	0.047142	0.0067
	FINX	0.701163***	0.053901	0.0000
	ESU	–0.535823***	0.053931	0.0000
	Constant	1.032025***	0.059700	0.0000
20th, 80th	REN	0.054123	0.042046	0.1980
	FINX	0.582125***	0.041593	0.0000
	ESU	–0.558932***	0.035507	0.0000
	Constant	0.821813***	0.049383	0.0000
30th, 70th	REN	0.083884**	0.040316	0.0375
	FINX	0.467229***	0.098767	0.0000
	ESU	–0.437297***	0.098416	0.0000
	Constant	0.611814***	0.118230	0.0000
40th, 60th	REN	0.032642	0.028892	0.2586
	FINX	0.101074	0.120299	0.4008
	ESU	–0.063257	0.097339	0.5158
	Constant	0.111187	0.135442	0.4117

Conclusion and Policy Implications

Given the growing severity of environmental issues linked with industrialization, this study aimed to investigate the implications of renewable energy, financial development and economic sustainability on NICs’ environmental quality between 1998 and 2021. However, the present study pioneered the introduction of the economic sustainability and financial development indexes while concluding environmental degradation. This study aimed to generate reliable and conclusive results for the stakeholders, and the findings were rigorously tested using multiple approaches to ensure robustness. The PQR estimates conclude that renewable energy is a source of energy that reduces CO₂ emissions and improves environmental quality in the lower and middle quantiles. As far as financial development is concerned, it showed heterogeneous results among different quantiles. Nevertheless, it has been demonstrated that achieving economic sustainability can be a reliable means of reducing CO₂ emissions for NICs. These findings further support the fall phase U shape of EKC, which is determined by economic sustainability and environmental quality. The results are also endorsed by FMOLS and DOLS estimates. However, different robustness tests also validate the estimations. The current study derived a series of policy recommendations for the stakeholders based on the prudent results and conclusions. •

Policy 1: Given the negative and significant influence of renewable energy on CO₂ emissions observed in lower and middle quantiles, policymakers in NICs should prioritize initiatives encouraging the adoption and utilization of renewable energy sources. This could include implementing incentives such as subsidies, tax breaks and feed-in tariffs to promote investment in renewable energy technologies. The primary advantage of renewable energy sources is their ability to provide electricity without emitting greenhouse gases. Long-term sustainability is assured because they do not consume finite resources like fossil fuels. Nevertheless, renewable energy solutions are being promoted worldwide to mitigate climate change and minimize fossil fuel use. Governments, corporations and individuals should invest in solar farms, wind turbines and hydropower facilities to transition to a low-carbon energy future. Integrating renewable energy sources into the energy portfolios of NICs has the potential to bolster their energy security and mitigate their reliance on imported fossil fuels. It will also align with the UN SGD 7.

•

Policy 2: Recognizing the heterogeneous impact of financial development on CO₂ emissions across different quantiles, policymakers in NICs should consider implementing regulations and guidelines to ensure that financial development activities do not exacerbate environmental degradation. This could involve integrating environmental sustainability criteria into financial sector regulations and encouraging green financing practices. Financial progress and environmental degradation are complicated and affected by many factors. Today’s authorities and corporations must balance financial and economic development with environmental sustainability. Economic measures like the International Organization for Standardization (ISO) certification, heavy taxes for polluting sectors, fines and subsidies for ecologically responsible activities can promote clean production and green financing. It is recommended that the government take measures to incentivize lenders to facilitate funding for the energy sector and prioritize allocating financial resources towards environmentally sustainable firms rather than expending them on consumer financing.

•

Policy 3: The concept of economic sustainability, as indicated by the reduced CO₂ emissions across all quantiles for NICs, suggests that prioritizing sustainability in economic policies can lead to environmental benefits. Policymakers should, therefore, integrate economic sustainability goals into national development strategies, ensuring that economic growth is pursued in a manner that is environmentally sustainable in the long term. Sustainable development is fundamental to seeking a harmonious equilibrium between economic advancement, environmental preservation and societal welfare. Environmental degradation can have an extensive adverse impact on ecosystems, human health and overall quality of life. The potential consequences include the depletion of ecosystem services, such as providing clean air and water, maintaining fertile soils and managing climate. The mitigation of environmental degradation commonly provides for the adoption of sustainable practices, the conservation of natural resources, the reduction of pollution and the implementation of laws and activities aimed at mitigating climate change. Diversification has the potential to enhance the resilience of energy systems as well. This study suggests decoupling to the policymakers, which involves economic growth without environmental harm. Moreover, it aligns with one of the objectives outlined in UN SDG 8, which emphasizes promoting sustainable economic growth.

•

Policy 4: Developed countries have traditionally been the primary contributors to CO₂ emissions, but the findings of this study conclude that NICs have a significant role in this regard owing to their rapid economic expansion. To effectively address the issue of global climate change, it is imperative to implement plans that encompass assistance for NICs in their transition towards cleaner and more sustainable energy systems. This must be done with their pursuit of economic growth and development.

This study has certain limitations that offer further investigation opportunities in this subject. First, the study focuses specifically on NICs from 1998 to 2021 as per the availability of the data. However, this narrow temporal and geographical scope may restrict the generalizability of the findings and overlook significant variations across regions and periods. Second, environmental degradation is a multifaceted phenomenon influenced by various socio-economic, political and environmental factors. This study focuses on CO₂ emissions as a proxy for environmental degradation, which may oversimplify the complexity of environmental dynamics and overlook other critical environmental indicators or dimensions such as biodiversity loss, land degradation or air and water pollution. Third, economic sustainability was calculated based on P-I-F-T ingredients due to data and analysis constraints. However, neglecting factors of production limits the study’s ability to capture the full spectrum of economic dynamics and their interplay with sustainability outcomes. However, future research should be focused on addressing these limitations.

Footnotes

Acknowledgements

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Saqib Mehmood

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