The Dynamic Impact of Renewable Energy Consumption, Trade, and Financial Performance on Carbon Emissions Worldwide in Low, Middle and High Income Countries: A Quantile Regression Approach

doi:10.21203/rs.3.rs-2220335/v1

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The Dynamic Impact of Renewable Energy Consumption, Trade, and Financial Performance on Carbon Emissions Worldwide in Low, Middle and High Income Countries: A Quantile Regression Approach

https://doi.org/10.21203/rs.3.rs-2220335/v1

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published 16 Mar, 2023

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The present study confronts potential theoretical argument of dynamic and non-linear relationship between CO2 emissions, renewable energy consumption, trade, and financial performance by using quantile regression that accounts for the role of development in explaining the stated nexus. The results show that renewable energy consumption reduces CO2 emissions in the short run in low, middle, and high-income countries. CO2 emissions plumet as country open up for trade and expand financial services for their people. It is found that trade openness and financial development decrease CO2 emissions at upper quantile in low-income countries. In the middle-income countries the findings are not much different as reported in case of low-income countries. In the high-income countries, renewable energy consumption and trade openness lead to decrease in CO2 emissions at all income quantiles. The D-H causality test draws a sturdy support of bi-directional causation between renewable energy and CO2 emissions in low- income countries.

Based on this analysis, some important policy implications can be drawn. First, in advanced countries restrictions on renewable energy does not have significant effect on environmental condition. However, in low-income countries adoption of renewable energy can significantly reduce CO2 emissions. Second, low-income countries may combat rise in CO2 emissions by introducing new technologies in exploiting trade potentials that are necessary to acquire resources to adopt clean energy. Third, energy policies should be framed based on the stage of development of a country, share of renewable energy in its total energy mix, and environmental condition of the country.

Trade openness

Financial development

Carbon emissions

Quantile regression

Renewable Energy

CO2 emissions

In recent years, the threating effects of global warming-ranging from environmental damage to human health challenges- have attracted considerable attention of environmentalists and policy makers. Paris climate conference 2015 is considered a watershed mark in the context of combating global warming through reduction in GHGs (greenhouse gas emissions) backed by a serious pledge of international community, Glasgow Summit the most recent. Scientists believe that despite determined efforts on the part of governments spread over decades, the planet will not cool down as the threshold level of temperature set at 2°C will be missed. Furthermore, the projection that temperature will reach 30°C by 2050 warns about impending catastrophes including natural disasters (UNFCCC, 2018). Intergovernmental panel on climate change, IPCC (2007) mentions that developing countries will face decrease in their GDP by 2–4% and 10% by 2040 and 2100 respectively. The common global goal agreed by nations compels developed nations to transfer energy-efficient technologies to developing countries with the objective to combat CO2 emissions without compromising their economic development. The measures including environmental fund transfer from developed nations to low-income countries and transition towards renewable, sustainable, and clean energy will enable poor nations to participate in the global efforts to address environmental challenges (UNFCCC, 2015).

Despite its catastrophic implications, the global warming impacts are not uniform for all regions in the world, as few regions are more prone to these events due to many socio-economic factors including trade openness, level of development and financial performance. To protect environmental damage without compromising on increased human activity aimed at economic growth is a challenge for environmentalists and policy makers. Energy consumption is the crucial input in the production process in modern economy (Rafiq & Salim, 2009) and all the developments are in and around energy especially fossil fuel. According to an estimate, until 2025 energy consumption will grow at 1.1 and 3.2 percent in developed and developing countries respectively (Asif & Muneer, 2007). The contribution of fossil fuels in global energy demand is about 80–95%. This is alarming as fossil fuel consumption has huge damaging effect on the environment including air pollution, climate change and global warming (Nejat, Jomehzadeh, Taheri, Gohari, & Majid, 2015). Therefore, it is mandatory to use renewable, clean, and sustainable energy sources to grow economies without compromising the ecological footprint (Saidi & Omri, 2020). The share of renewable energy such as wind power, biomass, solar, hydropower, nuclear, tidal, and geothermal is increasing in total energy mix in industrialized countries due to rapid decrease in renewable energy technology costs (Bulut & Inglesi-Lotz, 2019). However, developing countries are struggling to exploit renewable energy sources to meet their energy demand and hence causing massive CO2 emissions which raises various concerns about global environmental conditions (WDI, 2018). The continuous increase in energy demand to power economic activities in developing nations has elevated pollution levels (Zafar, Ullah, Majeed, & Yasmeen, 2020).

In the light of energy economic literature, it is argued that renewable energy can help reduce CO2 emissions, ensure sustainable development, and enhance environmental quality (Bulut, 2017; Swain, Kambhampati, & Karimu, 2020) especially in developing countries (Shafiei & Salim, 2014). According to Fang (2011), policy makers expect that renewable energy can outweigh environmental challenges created by fossil fuel energy consumption and meet energy needs for economic development. Therefore, many countries have reshaped their energy policies to incentivize renewable energy through provision of investment and sectoral subsidies and other supports (Koçak & Şarkgüneşi, 2017). To draw meaningful conclusions and frame suitable policies regarding renewable energy, trade openness and financial development, we have selected the sample of low, middle, and high-income countries. We mention in this study that the impact of financial development, trade openness and renewable energy consumption on CO2 emissions is different across countries. Trade openness and economic growth are strongly correlated, and we are investigating whether trade openness and environmental quality are associated in the ensemble countries. In studies of environmental quality, the major concern has been the association between environmental quality and trade openness as later may affect environment positively (Ferrantino, 1997) or negatively (Khan, Weili, Khan, & Khamphengxay, 2021). Trade openness improves economic development but at the same time pollutes environment through export and import activities (Khan et al., 2021). Poor policies in developing countries fail to increase economic growth without rise in CO2 emissions. This study considers role of trade in determining CO2 emissions due to increase in global human activity and changing environmental standard. We expect that the trade effects on environmental quality are not uniform across countries but vary according to development level of a country.

This study also expects that like trade openness, financial development can have impact on environmental quality. A plethora of studies record the negative impact of financial development on environmental quality (Muhammad, Tiwari, & Muhammad, 2011) through different channels including foreign direct investment which enhances economic growth and subsequently increases energy consumption that causes global warming, reduction in financial cost because of development of the stock market that attracts new installations leading to increase in energy consumption, and consumerism based on easy and affordable loans that increase purchase of luxurious items (Ahmed, Kousar, Pervaiz, & Ramos-Requena, 2020). On the other hand, a handful of studies conclude that financial development improves environmental quality (Komal & Abbas, 2015; Tamazian & Rao, 2010) through the channel of increase in energy efficiency (Gokmenoglu, Ozatac, & Eren, 2015). This can be attributed to the easy provision of modern technologies that are environmentally friendly. Hence, it is believed that financial development is key to sustainable environment (Boutabba, 2014).The inconsistency in findings of the exiting empirical literature on financial development-environmental nexus requires further examination of the potential impact of financial development on environmental sustainability.

Therefore, a thorough investigation of the literature reveals several limitations to which we want to address in this study: first, the non-existence of a consensus on the possible outcomes of renewable energy, trade openness, financial development in the context of environmental pollution. For example, the impact of trade openness and financial development on CO2 emissions might be positive as well as negative (Abbas et al., 2020; Honma, 2015; Khobai & Le Roux, 2017; Solarin, Al-Mulali, Musah, & Ozturk, 2017). In addition to these contradictory findings, some studies reveal non-linearity/asymmetric association between the stated nexus (Ahmed et al., 2020). Second, as per our knowledge scarce literature exists that discusses the environmental implications of financial development and trade openness (Boutabba, 2014).Third, no empirical evidence can be found on the relationship of trade openness, financial development, and CO2 emissions worldwide. Therefore, this study contributes on the several fronts: First, the study used the quintile regression in addition to panel ARDL to account for the income level in explaining trade openness-financial development-CO2 emissions nexus. Furthermore, D-H causality test has been conducted to determine the direction of relationship between the variables. The quintile regression with its unique characteristics can handle outliers lying across percentiles of the data series while other regression techniques estimate mean effects, which usually overestimate, underestimate, or fail to detect significant dependencies (Binder & Coad, 2011). In addition, along with conditional distribution, the quantile regression help find more detailed and comprehensive relationship among the series (Zhang, Peng, Liu, & Tan, 2015).

Second, the study targets all groups of countries e.g., low, middle, and high-income countries to provide reliable insights for policy purpose as all the previous investigated relationships are either country specific (Ahmed et al., 2020) or region specific (Aruga, 2019). Third, long data ensures the credibility of estimation results that are essential for effective policy framework related to renewable energy in all group of countries. This study uses data from 1960 to 2019 that is significantly larger than any other data set used in previous empirical literature on the subject as (Ahmed et al., 2020) [1996–2018: Pakistan]; (Aluko & Obalade, 2020) [1985–2014: 35 SSA countries]; (Shobande & Ogbeifun, 2021) [1980–2014: 24 OECD countries]; (Li, Zhang, & Ma, 2015) [1980–2010: 102 countries]; (Ye, Khan, Wu, Shah, & Abbas, 2021) [1987–2020: Malysia].

Third, country specific studies have some constraints regarding estimation as these studies use time-series approaches (Baltagi, 2008). This demands a panel data study that allows the heterogeneity across economies and increases the estimation power by combining time-series and cross-sectional data. In addition, studies conducted on panel data can potentially handle limitations associated with time-series analysis. Finally, the previous empirical literature has yielded un-conclusive findings on CO2 emissions-renewable energy- trade openness- financial development nexus thus necessitating further analysis of the nexus. To our best knowledge, not a single study can be mentioned that investigates the dynamic impact of renewable energy, trade openness, and financial development on CO2 emissions globally.

The energy-environmental literature has been growing rapidly over the last few decades. Many of the studies have examined the relationship between energy consumption (renewable and non-renewable) and several environmental indicators including CO2 emissions, ecological footprint, and coal consumption. No consensus can be found on the linkage between environmental degradation and energy consumption especially renewable energy consumption. The role of renewable energy consumption in context of environmental standard has been relatively a less studied area of research. Here, we document only those studies that are directly linked to environmental quality in the context of renewable energy, trade openness, and financial development.

Saidi and Omri (2020) used data from 15 major renewable energy consuming economies to study the relationship between renewable energy consumption and CO2 emissions. They found effective role of renewable energy consumption in mitigating the effects of CO2 emissions; however, this relationship vanishes in the long run. Qi, Zhang, and Karplus (2014) report reduction in cumulative CO2 emissions due to renewable energy installation for the period 2010–2020 in China but in each year through 2025 the increased renewable effects are offset. A modest reduction in CO2 emissions is observed when only supply side in energy sector is targeted. Long, Naminse, Du, and Zhuang (2015) used data from China over the period from 1952 to 2012 to examine the nexus between energy consumption (renewable and non-renewable) and carbon emissions. Their findings reveal weak impact of renewables in reducing CO2 emissions and improving economic growth; however, reduction in cool consumption significantly improves environmental standards. Bilgili, Koçak, and Bulut (2016) revisited environmental Kuznets curve hypothesis by employing data set from 17 OECD countries for the period 1977–2010. They report the negative association between renewable energy consumption and carbon emissions and conclude that global warming problem can be combat through improved renewable energy technologies. Nachrowi (2012) investigated the impact of renewable energy on CO2 emissions using panel data of G-20 countries for the period 2001–2010 and reach the conclusion that renewable energy use reduces CO2 emissions per capita. Zoundi (2017) used data from 25 selected African countries and conclude that use of renewable energy plummet CO2 emissions and remains an important substitute for the traditional fossil energy. However, this effect is outweighed in the short run as well as in the long run by primary energy consumption which require large scale adoption of clean energy to outpace environmental challenges. Bölük and Mert (2015) recorded the positive impact of renewable and non-renewable energy consumption on CO2 emissions in European Union countries. In Vietnam, renewable energy has no impact on CO2 emissions (Al-Mulali, Saboori, & Ozturk, 2015).

Trade openness expounds the level of engagement of a country with its trading partner in terms of exports and imports. To enhance domestic production, countries increase exports by expanding industries that in turn pollute the environment (Jun, Mahmood, & Zakaria, 2020). Through technological and economic growth effect, trade openness can reduce and increases CO2 emissions at the same time making the efficient estimate of environmental quality difficult (Yu, Nataliia, Yoo, & Hwang, 2019). No consensus has been developed on the environmental impact of trade openness: a strand of empirical studies state increase in pollution levels because of increase in trade openness e.g., (Husnain, Nasrullah, & Khan, 2021; Lin, 2017; Wen & Dai, 2020); however, a handful of studies conclude that environmental quality improves due to increase in trade openness based on technology effect which states adoption of cleaner practices because of technology transfer amongst trading economies (Ghazouani, Boukhatem, & Sam, 2020; Kohler, 2013). Wang and Zhang (2021) noted that in low-income countries trade openness leads to increase in pollution levels but improves environmental condition in middle and high-income countries. Likewise, Sajeev and Kaur (2020) mention that with fewer and lax environmental regulations, trade openness increases GHGs emissions in developing nations. On the other hand, negative association between environment and trade openness was observed in the case of India (Jayanthakumaran, Verma, & Liu, 2012).

The theoretical relationship between financial development and environmental quality is backed by the so-called wealth effect, business effect, and household effect. The wealth effect states that financial development spurs economic growth which leads to more energy consumption (Acheampong, 2019). Business effect channel of financial development can improve environment quality as firms may adopt environmentally friendly technologies based on their easy access to funds while it may have negative implications for the environment if firms utilize easy funds to expand their business and acquire more inputs leading to increased energy consumption and hence deterioration of the environment. Under household effect, cheap credit ensures availability of energy-consuming items which negatively affects environmental quality (Koçak & Şarkgüneşi, 2017; Zhang, 2011).

Many studies have explored the association between carbon emissions and financial development. This includes but not limited to Ye et al. (2021) work in which they investigated the potential environmental implications of financial development in case of Malaysia using data from 1987–2020. It was observed that financial development deteriorates environmental quality in the short run as well as in the long run. Mesagan and Nwachukwu (2018) apply the ARDL bounds testing approach on the Nigerian data for the period 1981–2016. They found no evidence of any causal relationship between financial development and environmental deprivation. Ali et al. (2019) analyze Nigerian data from 1971–2010 with the help of ARDL bound testing method and conclude that financial development increases carbon emissions both in the short run and the long run. Using data from 24 OECD countries for the period 1980–2019 and applying the Arellano-Bover/Bundell Bond dynamic panel technique, Shobande and Ogbeifun (2021) state that financial development does not deteriorate environment and increases energy consumption in the ensemble counties. In Asia Pacific region environment degradation is negatively associated with financial development (Zaidi, Zafar, Shahbaz, & Hou, 2019) while in 5 ASEAN countries, financial development degrades the environment (Nasir, Huynh, & Tram, 2019). In a sample of 9 Belt and Road economies, Baloch, Zhang, Iqbal, and Iqbal (2019) report that financial development is a cause of decline in environmental quality. Green finance in China improves environmental quality (Zhou, Tang, & Zhang, 2020) while Nguyen, Pham, and Tram (2020) reach the conclusion that financial development exerts negative impact on environmental quality in G20 countries.

The following two points lie behind motivation of this work. First, despite the existence of few studies on the energy-environmental Kuznets curve, none of them demonstrates the role of renewable energy in mitigating carbon emissions while controlling for the effect of trade openness and financial development at international level. Second, this study scrutinizes the role of income level while testing the validity of energy-environmental Kuznets curve by dividing sample based on income level. This study bridges this gap by investigating the dynamic relationship between renewable energy, CO2 emissions, trade and financial development using data from 187 countries and employing most recent estimation techniques including the panel ARDL, the quintile regressions and D-H causality test.

The EKC framework assumes that income accelerates pollution at early stages of economic growth and improves environmental quality at higher level of development (Grossman & Krueger, 1991).Researchers are putting their tremendous efforts to empirically understand and analyze the probable factors influencingCO₂ emissions without compromising economic growth (Bölük & Mert, 2015; Dogan & Turkekul, 2016; Jebli, Youssef, & Ozturk, 2016). Dogan and Seker (2016) additionally added financial development to further modify the basic EKC framework. The primary objective of the study is to examine the influence of renewable energy (RE), trade openness (TO) and financial development (FD) on CO₂ emissions in low, middle, and high-income countries. Domestic credit to private sector represents financial development (Bui, 2020), and results in funds effective allocation Greenwood and Jovanovic (1990) which ultimately boost the market (McKinnon, 1974), thus used as a proxy to represent financial development. Ibrahim and Alagidede (2017) characterized domestic credit to have a straight edge over other monetary aggregate proxies due to its accuracy for channelization of total funds toward private sector. Logarithmic baseline model for the current study can be framed as under;

$${lnCO2}_{t,ij}={f(lnRE}_{t,ij}+ ln{\text{T}\text{O}}_{\text{t},\text{i}\text{j}}+\text{l}\text{n}{\text{F}\text{D}}_{\text{t},\text{i}\text{j}}+\text{e})$$

Where, t represents time, i represents cross-section (country) and j represents panel (low, middle, and upper-income countries).${ CO}_{2}$is carbon emissions while$\text{R}\text{E}$, $\text{T}\text{O}$ and $\text{F}\text{D}$ represent renewable energy consumption, trade openness and financial development respectively. We obtain country level annual data from WDI (2021) for the period of 1960 to 2019. We produce 3 panels i.e., low-, middle- and high-income countries grouped on the basis of GNI per capita based on World Bank criteria. Out of total 212 countries, data was missing for 25 countries. The remaining 187 countries were grouped to low (23), middle (102), and high income (62) countries.

3.1. Cross Sectional Dependence

Our systematic methodology begins with the testing of cross-sectional dependence (CD).Avoiding the issue of cross-sectional independence may lead us to forecasting inaccuracies (Dogan & Seker, 2016). Thus, to confirm CD in each panel, this study employs panel CD test of Pesaran (2004). Breusch and Pagan (1980) propose LM test in the essence of apparently distinctive regression models applicable in case of finite N where T belongs to ∞, while Pesaran (2004) suggest an alternative advance estimator with the null of CD applicable in case of sufficiently large T and infinite N given as;

As$\widehat{{\text{k}}_{\text{i}\text{j}}}$ represents the residual correlation (pairwise).In case of fixed values of N and T, unlike LM estimate, CD estimate of Pesaran (2004) has precisely zero mean applicable for a wide class of panels i.e., dynamic/static, heterogeneous/homogeneous or also for the panels with complex breaks in their variances (error) and slopes (coefficients).

3.2. Panel Unit Root Test

In the essence of cross-sectional dependence, the first-generation tests for unit root such as Levin-Lin-Chu of Levin, Lin, and Chu (2002) and Im Pesaran-Shin of Im, Pesaran, and Shin (2003)fail to tackle CD (Dogan & Seker, 2016). Thus, the current study employs CIPS (cross sectionally augmented Im Pesaran-Shin) and CADF unit root tests as they are robust to both CD and heterogeneity.Both the CADF and CIPS perform the null of non-stationarity for all the cross sections within the panel versus at least one stationary cross section as an alternative hypothesis. The CADF estimator can be calculated from a general regression.

$${\varDelta Y}_{it}={\propto }_{i}+{\beta }_{it}+{\phi \stackrel{-}{Y}}_{it}+{\vartheta \varDelta \stackrel{-}{Y}}_{it}+\epsilon$$

Where, the OLS ratio for ${\rho }_{i}$is represented by ${t}_{i}$.(Herzer & Vollmer, 2012)

3.3. Panel ARDL

To examine both the long and short run influence of renewable energy, trade and financial development on CO₂ emissions and avoid inappropriateness of static models i.e., fixed, random or pooled OLS in case of heterogeneous panels, we use PMG (pooled mean group) based panel ARDL (auto-regressive distributed lag) model of Pesaran, Shin, and Smith (1999). PMG based ARDL is a good substitute to other panel estimators such as 3SLS (3 stage least square), dynamic OLS and GMM (general method of moments) because it is a transitional estimator and takes account of averaging and pooling. Further advantages of PMG-ARDL over DOLS and OLS is that, it restraints long term estimates to be the same while allows short run coefficient to be heterogeneous for the cross sections (Bildirici, 2014). Empirical specification of the PMG framework is as follows;

$${\text{l}\text{n} {\text{C}\text{O}}_{2}}_{ijt}= \sum _{s=1}^{p}{\lambda }_{ijs}{\text{l}\text{n} \text{R}\text{E}}_{i,j,t-s}+\sum _{s=1}^{q}{\beta }_{ijs}{\text{X}}_{i,j,t-s}+{\mu }_{i}+{\varepsilon }_{ijt}$$

Where CO₂represents CO₂ emissions, ${\text{X}}_{i,j,t-s}$ is a(K X 1) vector of independent variables consisting renewable energy consumption, trade openness and financial development and their interactions. ${\lambda }_{ijs}$, ${\mu }_{i}$and ${\beta }_{ijs}$represent coefficient of the lagged dependent variable, coefficient of the explanatory variables and the fixed effect respectively,$i$ ,$j$ and $t$ indicate cross sections, panels, and time respectively while $\varepsilon$ is the error term.

3.4. Quantile Regression

For policy analysis employing traditional models such as OLS methods are not appropriate to reach the target implications under diverse market conditions (Kaza, 2010; Koenker & Hallock, 2001). The selection of quantile regression is motivated by the fact that the distribution of CO₂ emissions is properly confined by different quantiles (Hammoudeh, Nguyen, & Sousa, 2014). Panel quantile regression is preferred with its unique characteristic of dealing with outliers associated with different quantiles of the data series while other econometric frameworks focus only on the mean influence (Binder & Coad, 2011). The quantile regression can study the diverse influence of explanatory variables on the entire distribution of CO₂ emissions. The τ^th conditional quantile of a response variable can be written as follow;

$${QR}_{i}\left(\tau ⃓{ E}_{i}\right)={x}_{i}^{m}{\lambda }_{\tau }$$

Koenker (2004) and Galvao (2011) extend Eq. 3 to capture the unperceivable heterogeneity of the panel units and apply panel quantile model as;

$${QR}_{i,j,t}\left(\tau ⃓{E}_{i,j,t}\right)= \omega {\left(\tau \right)}^{΄}{E}_{i,j,t}+{\lambda }_{ij}, i=\text{1,2},\dots ,N, T=\text{1,2},\dots ,N$$

Where, ${R}_{i,j,t}$ and ${E}_{i,j,t}$indicateresponse (CO₂ emissions) and the explanatory factors (renewable energy, trade, and financial development) at time t for country j in panel i,$\lambda$representcross-sectional (country wise) unperceivable effect while $\omega \left(\tau \right)$ represent a quantile varying vector of the probable parameters in the framework. But incompatibility of PQR (panel quantile regression) with linear methodologies makes solving Eq. 4 ambiguous. Koenker (2004) introduces penalty term i.e., “L_− 1 norm” for avoidance of unobservable fixed effect in his proposed shrinkage method. The crucial importance of Koenker’s shrinkage framework is its capability to organize individual coefficients inconsistency^[1]. The model after introduction of penalty term can be written as;

$${}_{\beta }{}^{min}\sum _{t=1}^{K}\sum _{I=1}^{T}\sum _{n=1}^{C}{w}_{n}{p}_{\tau n}({\text{R}}_{\text{i},\text{j},\text{t} }– \beta {E}_{i,j,t}^{T}\xi {(\tau }_{n}))+\alpha \sum _{i,j}^{C}⃓{\beta }_{i,j}⃓$$

Where, i indicates the countries index “C” of panel j in time t. T represents total number of observations, K is the number of observations per τ^th conditional quantile,$\text{R}$ is response variable,$E$is a matrix of explanatory variables and$p$denotes quantile loss function. Each n^th quantile is weighted by${ w}_{n}$, and (${w}_{n}=1/n)$, $\alpha$is arotating parameter to shrink the individual effect with $\xi$ as a performance parameter.

$${QR}_{i,j,t}\left(\tau ⃓{E}_{i,j,t}\right)={\beta }_{1,\tau }{{CO}_{2}}_{i,j,\tau }+{\beta }_{2,\tau }{RE}_{i,j,\tau }+{\beta }_{3,\tau }{TO}_{i,j,\tau }+{\beta }_{4,\tau }{FD}_{i,j,\tau }+{\beta }_{5,\tau }{GNI}_{i,j,\tau }$$

Where, i, j and t is the cross-section, panel and time respectively (I and t = 1… N, j = 1, 2, 3).

3.5. Dumitrescu and Hurlin Panel Causation

Once long run integration and the diverse influence in the observed variables is established and reported, it becomes a key interest of the researchers to investigate causal relationships between the variables. Understanding of the causal linkages assists in policy making regarding economic growth and sustainable environment (Dogan, Seker, & Bulbul, 2017). Several studies rely on VECM (vector error correction model) based Granger causality test but Dumitrescu and Hurlin (D-H) (2012) causality test is the most prominent test to examine panel causality (Bilgili, Ozturk, Kocak, & Bulut, 2017).D-H estimate is specified for its unique characteristic of taking account of the CD (Dogan et al., 2017). Furthermore, causality estimator of Granger (1969) uses past observations of x on current observations of y for detection of causality which is unable to account for individuals in the panel (Lopez & Weber, 2017). Thus, we employ D-H in order to determine direction of causality among the subject variables.D-H estimator for a given y and x can be written as;

$${Y}_{it}=\propto +{\sum }_{m=1}^{M}{Y}_{i}^{m}{Y}_{it-m}+ {\sum }_{m=1}^{M}{\delta }_{i}^{m}{X}_{it-m}+{\epsilon }_{it}$$

Where, $\propto$ and $\delta$ are the constant term and coefficient of X respectively, $M$ is the optimal lag while$\epsilon$ is the error term.

[1]For further details and explanations quantile regression and shrinkage method see work of Koenker (2004)

First, we report test statistics and P-value based on panel CD test of Pesaran (2004) to confirm CD dependence in each panel time series (Table 1). Based on the p-values, the null of CD for all the 3 panels i.e., low, middle, and high-income countries is rejected. In the essence that the observed variables have CD, one may proceed with non-homogenous panel techniques Dogan and Seker (2016). Therefore, we use CIPS (cross sectionally augmented Im Pesaran-Shin) unit root test as it is robust to both heterogeneity and CD.

Table 1

Cross Sectional Independence
Pesaran's test of cross-sectional independence
Low Income countries		Middle Income countries		High Income countries
test stat	P value	test stat	P value	test stat	P value
41.886	0.000	290.476	0.000	143.544	0.000

[Table 1]

Given that the observed variables show CD, Table 2 contains cross sectionally augmented Im Pesaran-Shinand CADF unit root test results which are equally capable to take account of both heterogeneity and CD. The results show thatCO₂emissions, renewable energy, trade, domestic credit and GNI per capita are not stationary at their both levels and first difference in case of low-income countries. In case of middle- and high-income countries, all the variables are not stationary at their levels, while stationary at their first difference since the null hypothesis of stationarity is rejected with 99% confidence level. Table 2 exposes stationarity for all the series as all the subject series are integrated either of order I (0) or I (1).In other words, the integration of series in orderI (0) or I (1) allows for further long-run analysis procedure (Abdul Hadi, Zafar, Iqbal, Zafar, & Iqbal Hussain, 2018; Sinaga, Saudi, Roespinoedji, & Jabarullah, 2019). Thus, the current study uses PMG based panel ARDL to examine both the short and long run influence of renewable energy, trade, and domestic credit on CO₂ emissions of low-, middle-, and high-income countries.

Table 2

CIPS and CADF Unit Root
	CIPS		CADF
	Level	lag1	level	lag1
Lower Income Countries
CO2 Emissions	-6.082***	-6.082***	-10.895***	-2.744***
Renewable Energy	-6.163***	-6.163***	-18.224***	-12.628***
Trade	-5.039***	-5.039***	-15.299***	-10.075***
Domestic Credit	--	--	-13.193***	-7.227***
GNI	-5.19***	-5.19***	-11.134***	-8.658***
Middle Income Countries
CO2 Emissions	-2.726***	-2.635	2.908	1.372
Renewable Energy	-2.468	-2.466	-0.359	-0.471
Trade	-2.398	-2.364	0.226	0.461
Domestic Credit	-2.529***	-2.501	-1.985**	0.359
GNI	-2.045***	-2.535	-1.523**	-1.632
Upper Income Countries
CO2 Emissions	-1.951	-1.934	-3.287***	2.263
Renewable Energy	-1.90	-1.93	5.544	6.545
Trade	-2.27	-2.39	1.06	2.595
Domestic Credit	-1.715	-2.114	3.617	3.166
GNI	-2.519**	-2.462	2.171***	2.632

[Table 2]

Table 3 shows that in the long run renewable energy, financial development and GNI reduce CO2 emissions in all the sample countries. However, trade openness is positively associated with CO2 emission in low- and middle-income countries while it affects CO2 emissions negatively in upper income countries. In the short run, results are somehow mix and differ from one group of countries to other group of countries. Overall, the findings suggest diversified influence of the subject variables on CO₂ emissions of low-, middle- and high-income countries. This diversified influence of renewable energy, trade openness and domestic credit on CO₂ emissions are due to their differential characteristic associated with their income levels and diversified demographics of the panels.

Table 3

Panel ARDL
	Low Income Countries					Middle Income Countries			Upper Income Countries
Variables	Coef	Std Er			Z	Coef	Std Er	Z	Coef	Std Er	Z
Long run analysis
Renawable Energy	-0.452***		0.048	9.31		-1.684***	0.086	19.37	-6.028***	0.347	17.33
Trade	0.327***		0.088	-3.68		0.467***	0.085	5.44	-1.086***	0.149	-7.28
Domestic Credit	-0.014		0.138	-0.1		-1.198***	0.103	-11.57	-1.586***	0.131	-12.04
GNI	-0.113*		0.060	-1.88		-0.744***	0.059	-12.52	-0.117**	0.058	-2.00
Short run analysis
_ec	-0.593***		0.048	-12.32		-0.170***	0.009	-17.71	-0.116***	0.018	-6.17
Renawable Energy	-0.135***		0.040	-3.37		-0.285***	0.058	-4.91	-0.573***	0.100	-5.71
Trade	-0.123**		0.048	2.54		-0.180	0.114	-1.58	-0.449**	0.215	-2.09
Domestic Credit	0.046		0.087	0.53		0.368***	0.088	4.17	0.435**	0.215	2.03
GNI	0.090**		0.045	2.00		0.745***	0.14	5.29	1.496***	0.328	4.56
Constant	34500***		292000	11.83		1.579***	0.121	12.98	0.998***	0.261	3.82
Log Likelihood	-392969.67					-7778.69			-4112.628

[Table 3]

In the essence of diversified results derived from the panel ARDL, we use panel quantile regression which deals with outliers associated with τ^th conditional quantile of the data series while the other econometric models focus only on the average influence (Binder & Coad, 2011). Table 4 presents results based on quantile regression at the τ = 25th, τ = 50th and τ = 75th quantiles. The findings reveal that trade openness and financial development at their upper quantiles decrease while renewable energy at their all quantiles increases the level of CO₂ emissions in lower income countries. In case of middle-income countries, renewable energy, and trade openness at their τ = 25th and τ = 50th while financial development at their τ = 50th and τ = 75th significantly increase the level of CO₂ emissions. Furthermore, renewable energy and trade at their τ = 25th and financial development at their 50th and τ = 75th significantly increase while renewable energy and trade at their τ = 75th decrease the level of CO₂ emissions in case of upper income countries.

Table 4

Panel Quantile Regressions Results
	Lower Incom Countries			Middle Incom Countries			Upper income countries
CO2_emissions ~ y	Coef.	Bootstrap Std. Err.	T	Coef.	Bootstrap Std. Err.	t	Coef.	Bootstrap Std. Err.
q25
Renewable Energy	0.059***	0.016	3.62	0.775***	0.057	13.46	0.533***	0.049	10.71
Trade	-0.007	0.032	-0.24	0.575***	0.093	6.13	0.533***	0.041	12.75
Domestic Credit	0.028	0.026	1.09	-0.270***	0.055	-4.88	0.016	0.041	0.41
GNI	-0.211	0.169	-1.25	0.344***	0.067	5.07	0.532***	0.028	18.74
Cons	14.006***	0.481	29.06	-0.00**	######	-2.48	0.000	0.000	-0.84
q50
Renewable Energy	0.029***	0.006	4.47	0.058**	0.024	2.37	0.034	0.044	0.78
Trade	-0.055***	0.011	-5.03	0.339***	0.044	7.62	0.089	0.105	0.85
Domestic Credit	-0.002	0.014	-0.19	0.135***	0.030	4.45	0.075***	0.022	3.44
GNI	0.091	0.092	0.99	0.429***	0.029	14.73	0.405***	0.037	10.89
Cons	16.100***	0.089	180.18	3.697***	0.145	25.35	5.799***	0.329	17.58
q75
Renewable Energy	0.031***	0.002	11.29	-0.220***	0.037	-5.96	-0.143***	0.027	-5.27
Trade	-0.051***	0.003	-15.06	0.07	0.097	0.73	-0.305***	0.107	-2.84
Domestic Credit	-0.008**	0.004	-2.00	0.1035**	0.05	2.06	0.236***	0.048	4.91
GNI	0.0645**	0.027	2.31	0.4220***	0.053	7.89	0.196***	0.026	7.29
Cons	16.970***	0.116	145.48	7.222***	0.479	15.07	10.731***	0.301	35.55

[Table 4]

Once long and short-run and different quantiles influence of the studied variables is observed, it is interesting to exploit causal relationship among the variables (Dogan et al., 2017). The existence of causal relationship facilitates policymakers regarding economic development and sustainable environment. Results of causal direction are reported in Table 5. We have a sturdy support of bidirectional causation among renewable energy and CO₂ emissions of low-income countries. In case of middle-income countries, bidirectional causality is observed between CO₂ emissions and renewable energy, trade openness and financial performance. Furthermore, bidirectional causality of CO₂ emissions with trade openness and financial performance is also observed in case of upper income countries.

Table 5

Dumitrescu and Hurlin (2012) Causality Results
		level	LAG2	LAG3
Panel 1 Low Income countries	renewable energy does not cause Co2 emission	84.866***	217.802***	447.379***
	financial performance does not cause Co2 emission	2.939***	4.458***	6.404***
	trade does not cause Co2 emission	--	--	--
	Co2 emission does not cause renewable energy	2.44**	3.898***	12.228***
	Co2 emission does not cause financial performance	0.769	0.215	0.597
	Co2 emission does not cause trade	--	--	--
Panel 2 Middle Income countries	renewable energy does not cause Co2 emission	2556.513***	5526.367***	4509.409***
	financial performance does not cause Co2 emission	2.617***	3.520***	2.376**
	trade does not cause Co2 emission	6.595***	4.640***	5.065***
	Co2 emission does not cause renewable energy	-2.311**	0.368	10.394***
	Co2 emission does not cause financial performance	3.480***	159.504***	100.654***
	Co2 emission does not cause trade	5.388***	42.424***	26.775***
Panel 3 Upper Income countries	renewable energy does not cause Co2 emission	--	--	--
	financial performance does not cause Co2 emission	115.288***	187.829***	209.440***
	trade does not cause Co2 emission	3.479***	1.683*	0.773
	Co2 emission does not cause renewable energy	--	--	--
	Co2 emission does not cause financial performance	8.668***	0.656	4.741***
	Co2 emission does not cause trade	4.820***	777.794***	837.093***

[Table 5]

The findings reveal that renewable energy plays a crucial role in reducing CO2 emissions in low-, middle- and upper-income countries. This conclusion is in line with the Nachrowi (2012) who reports that due to its non-carbon chemical characteristics, renewable energy is expected to be an important substitute of fossil energy and can reduce carbon emissions substantially. Dogan and Seker (2016) show that renewable energy mitigates carbon emissions in the European Union and there is unidirectional causality running from renewable energy to carbon emissions. Karimi et al. (2021) report that increase in renewable energy significantly decreases carbon emissions in Iran. However, the findings of this study contrast with the conclusion reached by Bilgili et al. (2017) who find validity of the energy-environmental Kuznets curve regardless of income level of individual county in ensemble economies.

The trade theory put forward by Heckshers and Ohlin expect positive impact of trade openness on CO2 emissions (Halicioglu, 2009) because CO2 emissions are stipulated by expansion of manufacturing activities due to larger open trade. Sharma (2011) observed positive association between trade openness and CO2 emissions in case of 69 countries sub-paneled based on national income level, namely low-income, middle-income, and high-income. Using data of 33 countries selected from all parts of the world over the 1971–1991 period, Suri and Chapman (1998) find that increase in manufactured goods imports significantly reduces energy consumption in a country and hence improve environmental standard. Trade openness has dual effect as it promotes economic growth but deteriorates environmental quality. Hossain (2011) find that trade openness causes CO2 emissions in new industrialized countries during the period 1971–2007. Radmehr, Henneberry, and Shayanmehr (2021) indicates that trade openness and carbon emissions are significantly and positively associated in the European Union countries.

In contrast, Karedla, Mishra, and Patel (2021) point out that trade openness reduces carbon emissions in the sample countries. The technology effect justifies the negative association between trade openness and carbon emissions. Therefore, developing countries should opt preferential trade policies through trade liberalization with especial focus on technological value addition. Eventually, because of trade openness a virtuous cycle emerges that enhances economic growth through intensification of competition, encouragement of capital flows and increase in employment opportunities. Faster and larger information flow because of expanded global trade will lead to decrease in pollution level (Karedla et al., 2021). The inconsistency of trade effects on environmental pollution may be attributed to difference in its direct and indirect effect (Dean, 2002).

Financial development has been considered a solution to the environmental challenges spurred by GHGs emissions. It is argued that increase in financial development enhances economic growth which ends in increased energy use leading to degradation of environment (Zhang, 2011). Conversely, through the provision of easy finance to adopt energy-efficient technology, financial development ameliorates deleterious effect of GHGs (Charfeddine & Kahia, 2019). Furthermore, by improving corporate governance, financial development could increase the quality of the environment (Claessens & Feijen, 2007). The seminal work of Frankel and Romer (1999) reveals that because of financial development multinational’s investment enter that are closely linked with extensive R&D activities that end up in improvement of environmental quality. The observed diversified influences of renewable energy, domestic credit, and trade on CO₂ emissions of countries with different income levels are consistent with (Gozgor, Lau, & Lu, 2018; Jebli, Farhani, & Guesmi, 2020; Kahsai, Nondo, Schaeffer, & Gebremedhin, 2012)).

Future research can extend the scope of this study by considering foreign direct investment, urbanization, population growth, and renewable energy generation as determinants of CO2 emissions. Fossil fuel prices are also crucial and should be accounted for when exploring the interplay between CO2 emission and its determinants. Furthermore, different sources of renewable energy generation can also be considered to have source-specific understanding on the role of renewable energy consumption in improving environmental standard in the ensemble countries.

The effectiveness of clean energy, trade openness and financial development in environmental protection has been the subject of great debate in the energy-economics literature. The prime objective of this study is to extend this literature by providing international evidence on the role of renewable energy, trade, and financial performance in the context of CO2 emissions by using reliable and robust estimation methods. Applying panel ARDL and Quantile regressions on data of 187 countries for the period 1960–2019, this study provides some important results and hence policy implications. The panel ARDL results show that renewable energy consumption reduces CO2 emissions in the short run in low, middle, and high-income countries. CO2 emissions plumet as country open up for trade and expand financial services for their people. The quantile regression results verify the findings reached in the panel ARDL estimation. It shows that trade openness and financial development decrease CO2 emissions at upper quantile in low-income countries. However, at all income quantiles renewable energy increases CO2 emissions in low-income countries. In the middle-income the findings are not much different as reported in case of low-income countries. In the high-income countries, renewable energy consumption and trade openness lead to decrease in CO2 emissions at all income quantiles. The D-H causality test drawsa sturdy support of bi-directional causation between renewable energy and CO2 emissions in low-income countries. However, bi-directional causality is observed between trade openness, financial development, and financial development in middle and high-income countries.

Based on this analysis, some important policy implications can be drawn. First, in advanced countries restrictions on renewable energy do not have significant effect on environmental condition. However, in low-income countries adoption of renewable energy can significantly reduce CO2 emissions. Second, low-income countries may combat rise in CO2 emissions by introducing new technologies in exploiting trade potentials that are necessary to acquire resources to adopt clean energy. Third, energy policies should be framed based on the stage of development of a country, share of renewable energy in its total energy mix, and environmental condition of the country. In low-income countries, incentive base mechanism should be introduced for the production, accessibility, and consumption of the renewable energy. Public-private partnership in renewable energy market could be a viable solution as under-developed countries are resource constrained and struggle to develop clean energy infrastructure. Finally, availability and accessibility to microcredit need widespread expansion to enable people to adopt to clean sources of energy that will improve environmental standards in the country. A policy approach including emissions trading system will help mitigating CO2 emissions effects over the long term. Fair and easy access to renewable, sustainable, and clean energy sources is crucial to combat CO2 emissions and hence global warming problem. Governments in low-income countries should relocate subsidies from non-renewable energy sources to renewable energy by showing commitment to build power plants that encapsulate renewable energy raw materials. The research based on renewable energy development should be incentivized.

Acknowledgment: Not Applicable

Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflict of Interest: Authors declare no conflict of interest.

Author Contribution: All authors contributed to the study conception and design. Material preparation, data collection and literature review were performed by Muhammad Iftikhar ul Husnain. Muhammad Aamir Khan wrote results, discussion and conclusion of the Manuscript. Nasrullah did the data collection and analysis and improved the previous versions of the manuscript. All authors read and approved the final manuscript.

Ethical Approval: Not Applicable

Consent to Participate: All authors have read the manuscript carefully and gave explicit consent to submit it to Environmental Science and Pollution Research.

Consent to Publish: All authors whose names appear on the submission approved the version to be published.

Availablity of Data and Material: The data that support the findings of this study are available from the corresponding author (MIH) upon reasonable request.

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The Dynamic Impact of Renewable Energy Consumption, Trade, and Financial Performance on Carbon Emissions Worldwide in Low, Middle and High Income Countries: A Quantile Regression Approach

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Abstract

1. Introduction

2. Literature Review

3. Data And Methodology

3.1. Cross Sectional Dependence

3.2. Panel Unit Root Test

3.3. Panel ARDL

3.4. Quantile Regression

3.5. Dumitrescu and Hurlin Panel Causation

4. Results

5. Discussion

6. Conclusion And Policy Implication

Declarations

References

Status:

Journal Publication

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