Cogitating the role of Technological Innovation and Institutional Quality in Formulating the Sustainable Development Goal Policies for E7 Countries: Evidence from Quantile Regression

Abstract

After the approval of the Sustainable Development Goals (SDGs) by the United Nations, all the countries have tried to achieve these goals. But unfortunately, the emerging economies are still far away from this ideal track. Because, mostly, the prime focus of emerging economies is just to increase the economic growth, while ignoring its environment-related negative externalities. Following this, the present study highlights the importance of designing the SDG framework for seven emerging (E7) economies, which can be used as a benchmark for other developing and emerging economies. For this purpose, this study uses quantile regression to scrutinize the heterogeneous impact of technological innovation, institutional quality, per capita income, trade openness and population on CO₂ emission by using the data of E7 countries from 1996 to 2018. The empirical outcomes confirm that technological innovation and institutional quality have performed a major role in reducing CO₂ emissions. Results also reveal that the current patterns of economic growth in E7 countries are environmentally unsustainable. Based on the results, this study suggests a comprehensive policy framework for attaining SDG 8, SDG 9, SDG 16 and SDG13.

Keywords

E7 countries Institutional quality Quantile regression Technological innovation

Graphical Abstract

Introduction

From the past few decades, global warming and climate change have become major hindrances in attaining social welfare and sustainable development (United Nations, 2019). Greenhouse gases (GHG) are responsible for ever-increasing CO₂ emissions (CO₂E) and global warming (Dong et al., 2018), as the global CO₂E in 1990 was 21,331 million tons, which reached 34,169 million tons in 2019 (British Petrolium (BP) Statistical Review, 2021). Therefore, the impact of anthropogenic and economic activities on CO₂E is becoming an attractive topic for researchers and policymakers (Cohen et al., 2018). In developing economies, the trade-off exists between economic growth (EGR) and environmental quality. As economic activities demand more energy, most of the time, emerging economies meet their energy requirements from fossil fuels, which increase environmental degradation.

Technological innovation (TIN) is one of the primary methods to increase EGR, save resources and impede CO₂E through applying sustainable business- and energy-efficient practices (Khan et al., 2020; Razzaq et al., 2021). Saudi et al. (2019) suggest that TIN is considered a key indicator to attain sustainable development. TIN can reduce CO₂E by transferring fossil fuel-based technologies into green, renewable and environment-friendly ones (Hopwood et al., 2005). TIN can reduce CO₂E through several methods, such as carbon capture technology, the development of electric vehicles, the improvement of energy efficiency and the development of renewable energy (Cheng et al., 2019; Kwon et al., 2017; Salem et al., 2021). TIN can help countries to enhance their environmental quality by improving the production process (Gozgor, 2017). Most of the empirical literature provides evidence that TIN decreases environmental degradation. However, the quality of government institutions plays a vital role in the adoption of green and environment-friendly technologies through administering environment-related rules and regulations.

Figure 1.

Source: BP-Statistics (2021).

Government institutions can also reduce environmental pollution through the implementation of policies such as the removal of fossil fuel subsidies, feed-in tariffs and carbon taxing (Anwar et al., 2021a; Wong et al., 2010). Frankel and Romer (1999) proposed that institutions can improve GDP growth and environmental quality through a strong judicial system, better allocation of capital and attracting foreign investment. Better institutions can reduce CO₂E through enhanced resource allocation (Dal Bo & Rossi, 2007; Ebeke et al., 2015). Strong institutions are a prerequisite for implementing policies of conversion from fossil fuel-based energy towards green or renewable energy (Mahjabeen et al., 2020). On the other hand, Shah et al. (2019) believe that weak institutions are unable to control the environmental damage, which arises due to the growth process. Consequently, the role of institutional quality (INQ) on environmental degradation needs serious consideration.

Figure 2.

Source: BP-Statistics (2021).

This study investigates some major determinants of CO₂E for E7 (Turkey, Indonesia, Mexico, Brazil, Russia, India and China) countries for the period from 1996 to 2018. These seven economies may surpass the Group of Seven—G7 (the USA, the UK, France, Japan, Germany, Italy and Canada) countries till 2032 (Safi et al., 2021). Hence, the primary reason to carry this research on E7 countries was their emerging role in the world economies. Just before the Coronavirus Disease 2019 (COVID-19) pandemic, the total GDP of E7 countries was US$22.377 trillion as compared to US$38.468 trillion (constant US$) of the world’s most advanced G7 countries (World Bank, 2021). The consistent increase in the growth rate of E7 countries is causing a rapid increase in energy consumption, which leads to additional CO₂E. The share of E7 countries in total energy consumption was almost 42% in 2018. According to BP-Statistics 2021, the share of E7 economies in global total CO₂E is almost 46% as presented in Figure 1, and country wise share of E-7 countries in their total CO₂E is presented in Figure 2. Therefore, in this study, we investigate the determinants of CO₂E to suggest a comprehensive policy framework for attaining Sustainable Development Goal (SDG)-9, SDG-8, SDG-16 and SDG-13.

The contribution of our study in the existing literature is twofold—first, several studies examined the impact of TIN on CO₂E (Govdeli, 2019; Hussain et al., 2020; Tao et al., 2021) but it is very hard to find any study that incorporates the role of INQ in innovation–emission nexus, as INQ has a major role in the adoption of modern technology through the enforcement and implementation of environment-related rules and regulations. Second, this study uses panel quantile regression (QR). This technique has several advantages as compared to classical techniques. For instance, (a) it provides a series of coefficients across the conditional distribution. (b) As compared to traditional mean regression, QR may provide comprehensive statistical outcomes. (c) QR is more suitable in the presence of outliers.

This section discussed the need, importance and background of the study. The second section presents the literature review. The third section discusses the data, model specification and methodology. The fourth section describes the results and discussion. The fifth section provides the conclusion and policy recommendations of the study.

Literature Review

This section is divided into two parts: the first part is about the theoretical development, which discusses the different channels through which macroeconomic indicators affect the environment, whereas the second part presents the empirical literature.

Theoretical Development

Attaining sustainable development has been documented as one of the important agendas across the globe. In addition, the United Nations has also stressed the significance of assuring a sustainable environment along with socio-economic sustainability worldwide. In this study, we tried to address some of the 17 SDGs (Cf, 2015) of the United Nations (e.g., SDG 13, SDG 9, SDG 12, SDG 16 and SDG 8) for suggesting policies to achieve sustainable development, which can safeguard the environment. For this purpose, this study uses the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model of Dietz and Rosa (1994). Numerous studies have already used STIRPAT for investigating the association between environmental quality and macroeconomic variables.

Several studies have documented that demand for energy and natural resources increases with the growth in the national output (EGR). Environmentally unfriendly ways are used to meet the increasing demand, eventually causing environmental degradation (Anwar et al., 2021d; Brizga et al., 2013; Farooq et al., 2021). On the other hand, various studies claim that affluent economies can finance technological development, which can increase EGR as well as mitigate environmental deterioration (Khattak et al., 2020). Based on these contrasting outcomes, most researchers claim that the growth–environment relationship is non-linear. This relationship is generally highlighted in the Environmental Kuznets Curve hypothesis of Grossman and Krueger (1991), which proposed that EGR initially increases environmental degradation, but after achieving a certain level of growth, it reduces environmental degradation (Aziz et al., 2020; Jun et al., 2021; Sharif et al., 2020). Thus, green growth policies are essential not only to protect against environmental degradation but are also supportive for the overall well-being of the environment (Ikram et al., 2020).

On the other side, INQ of the countries performs a dominant role in environmental policies. Bernauer and Koubi (2009) have reported that democratic regimes can implement environmental policies in a better way as compared to autocratic countries. Bhattacharya et al. (2017) argued that better institutions can reduce the CO₂E through subsidizing green energy and strict implementation of suitable tax (e.g., carbon tax) rates. Nasir et al. (2021) reported that a better institutional framework helps in controlling pollution through formulating effective policies, incentives, regulations and management systems to guide the stakeholders, consumers and industries about renewable energy sources. Padda and Asim (2019) reported that better institutions can help to reduce environmental pollution through efficient allocation of capital, and by monitoring investment and commercial and manufacturing activities.

Moreover, TIN is considered to be a critical factor in mitigating environmental deterioration (Li et al., 2021; Balsalobre-Lorente et al., 2018). Developing economies with limited access to technology are mostly using fossil fuel-based energy, which has become a cause of environmental degradation, while TIN is mitigating environmental degradation through the use of green and environment-friendly energy resources (Ozcan & Ulucak, 2021). Likewise, prior studies have found TIN as one of the most important variables to develop such instruments, which may restrict, control and monitor the use of polluted resources (Murshed, 2021). TIN may also reduce environmental pollution by increasing the level of energy efficiency (Anwar et al., 2021d). Environmental degradation can also be reduced through public awareness regarding sustainable development with the help of TIN (Lai et al., 2017). Moreover, Samargandi (2017) reported that CO₂E could be reduced through a major change in production activities with the help of TIN.

Empirical Evidence

Technological Innovations and Emission Nexus

The existing literature regarding TIN and CO₂E is an imperative area of study for attaining sustainable development. This literature can be divided into two major parts: first, the role of TIN in promoting green/clean energy development. Second, the role of TIN in reducing the CO₂E. TIN plays a key role in the development of green energy (Chen and Lei, 2018). By applying the autoregressive distributed lag (ARDL) technique, Sohag et al. (2015) inspected the influence of TIN, energy usage and CO₂E. They claimed that TIN raises energy efficiency, which impedes the use of fossil fuel-based energy and CO₂E. Similarly, Lantz and Feng (2006) scrutinized the role of TIN, population (POPU), and EGR on CO₂E during the period from 1970to 2000 for Canada. They discovered that technology development impedes CO₂E, while POPU and EGR increase CO₂E.

Furthermore, considering the data of China from 2000 to 2011, Lin and Wang (2015) reported that TIN and low-carbon investment perform a vital role in reducing CO₂E. Meliciani (2000) discovered that investment in TIN improved the environmental quality during the 1973–1993 period for 27 economies. By using ARDL for China from 1995 to 2012, Jin et al. (2017) discovered that technological improvement in the energy sector reduces CO₂E. They also suggested that the government should encourage TIN in the energy sector. Mensah et al. (2018) investigated the influence of TIN on CO₂E for 28 OECD nations. They discovered that investment in TIN mitigated pollution during the period from 1990 to 2014. Danish et al. (2019) suggested that EGR originates from industrialization, causing an increased use of fossil fuels (natural resources), which increases CO₂E. They emphasized that natural resources could be used in a better way with the support of TIN.

According to Cho and Sohn (2018), green TIN is a key source of mitigating the CO₂E for the UK, Italy, Germany and France. On the other hand, numerous studies have provided evidence that TIN has a direct inverse relationship with CO₂E. For instance, Balsalobre-Lorente et al. (2018) examined the relationship between TIN and CO₂E. The study findings supported the argument that TIN mitigates CO₂E for European Union (EU) countries during the period from 1985 to 2016.

Institutional Quality and Emission Nexus

There is a dearth of literature in finding the relationship between INQ and environmental deterioration. However, the empirical findings of these studies are inconclusive and have mixed results. Some studies provided a positive association, while some claimed a negative association between INQ and environment. For instance, Zakaria and Bibi (2019) examined the influence of EGR and INQ on CO₂E. They claimed that INQ mitigated CO₂E for the South Asian economies. The empirical findings of Zhang et al. (2016) found that INQ enhances environmental quality through direct and indirect ways in the case of Asia-Pacific Economic Cooperation (APEC) countries. Abid (2016) stated that democracy, government effectiveness, corruption, and social and political insecurity also impede CO₂E. Similarly, Ibrahim and Law (2016) claimed that INQ mitigates CO₂E. They even suggested that institutional reforms are very important to gain favourable outcomes regarding the environment. Sarwar and Alsaggaf (2021) scrutinized the influence of governance on CO₂E for Saudi Arabia during the period from 1970 to 2018. They found that better governance could impede CO₂E. Haldar and Sethi (2021) examined the influence of INQ on CO₂E by using the augmented mean group technique. They claimed that the INQ reduced CO₂E in 39 developing economies from 1995 to 2017.

On the other hand, Andersson (2018) examined the link between INQ and the environment. After empirical analysis, he found that governance and institutions are liable for environmental deterioration. By using the system generalized method of moments technique during the period from 2002 to 2015, Nguyen et al. (2018) explored the influence of INQ on CO₂E for 36 emerging nations. They found that INQ increases economic activities, causing an increase in the CO₂E. During the period from 1975 to 2007, Charfeddine and Mrabet (2017) investigated the impact of INQ on the environment for 15 Middle East and North African (MENA) countries. They claimed that INQ raises environmental deterioration. By using the data involving 66 developing countries for the period from 1991 to 2017, empirical results from a study carried out by Azam et al. (2021) found a positive relationship between INQ and CO₂E.

From the aforementioned discussion, we conclude that the relationship among technological innovation (TIN), INQ and CO₂E is inconclusive and has mixed results. Several studies found a positive link between TIN on CO₂E, while few studies claimed a negative association between TIN and CO₂E. Similarly, numerous studies provide positive evidence, whereas some studies claim a negative association between INQ and CO₂E. On the other hand, most of the aforementioned studies have used linear models, which may provide misleading results when data are not normally distributed. These misleading and inconclusive findings demand further investigation of the relationship between TIN, INQ and CO₂E. Therefore, in this study, we re-examine the influence of TIN, INQ, TO, EGR and POPU on CO₂E by using the QR, which provides the series of coefficients for a single variable.

Methodology

Data Source and Sample Period

This study used the yearly data from 1996 to 2018. The data for CO₂E, EGR, TIN, TO and POPU were collected from World Development Indicators (World Bank, 2021), while the data for INQ are collected from International Country Risk Guide (ICRG, 2021). The complete details regarding the sign, source and measurement scales of the variables are presented in Table 1.

Table 1.

Data Depiction.

Variables	Sign	Measurement Scale	Source
CO₂ emissions	CO₂E	Metric tons of CO₂ emissions	World Bank (2021)
Economic growth	EGR	GDP per capita (constant US$-2010)	World Bank (2021)
Institutional quality	INQ	Control on corruption index (range 0–6)	ICRG (2021)
Technological innovations	TIN	Total number of trademark applications	World Bank (2021)
Trade openness	TO	Exports and imports of goods and Services (% of GDP)	World Bank (2021)
Population growth	POPU	Total number of individuals	World Bank (2021)

Source: The authors.

Note: This study used all the variables after taking the logarithm.

Model Specification

Following Chekouri et al. (2020), this study used the log-linear STIRPAT model, which is a stochastic version of the IPAT model (Ozcan & Ulucak, 2021). In the IPAT model, ‘I’ represents the environmental impact, while ‘P’, ‘A’ and ‘T’ represent population, affluence and technology. The model of this study is as follows:

{CO}_{2} E_{i t} = β_{EGR} {EGR}_{i t} + β_{INO} {INQ}_{i t} + β_{IIN} {TIN}_{i t} + β_{TO} {TO}_{i t} + β_{POPuPOPU} U_{i t} + ε_{i t}

(1)

In the aforementioned equation (1) the CO₂E (CO₂ emissions) is the dependent variable, and the EGR, INQ, TIN, TO (trade openness) and POPU (population) are independent variables. The beta (β) of each variable is used to present the respective coefficient, and the sign of ε presents the error term. The values of i and t present the number of countries and time, respectively. In Equation (1), we are not including the energy consumption-related variable by following the studies of Itkonen (2012), and Jaforullah and King (2017), who suggested that the total effect of income on CO₂E will be underestimated if energy consumption is assumed to be a positive linear function of income.

Estimation Technique

Panel Quantile Regression

To identify the indicators of carbon emissions, most of the existing studies have used classical regression techniques like ordinary least square (OLS) (Anwar et al., 2021c), but the outcomes of such techniques are based on the mean value and fail to present the true picture of conditional distribution (Chien et al., 2021). Similarly, most of the time, the data are not normally distributed and contain outliers. In such a situation, those estimations, based on mean value, are unable to provide better outcomes. Therefore, in this study, we used panel QR. This technique provides a series of coefficients of a single indicator according to the nature of data, and it can overcome the issue of heterogeneity and outliers. QR has several advantages over the classical model. It provides a set of coefficients that detect the change in carbon emission due to the variation that occurred in the independent variables (Xu et al., 2017). Moreover, this technique can address those issues that affect the accuracy of the estimations such as unobserved heterogeneity, outliers and heteroscedasticity (Koenker & Hallock, 2001).

The following Equations (2) and (3) present the difference between the panel linear and panel QR models.

\begin{array}{l} z_{i t} = β_{0} + β_{2} x_{2, i t} + \dots + β_{n} x_{n, i t} + u_{i t} \\ i = 1_{2} \dots, p \end{array}

(2)

In the above-mentioned panel linear model, t and i depict the time and cross sections, respectively. The coefficient of the linear model is depicted as β, which could be changed in QR.

\begin{matrix} Q_{τ} (z_{i t}) = β_{0} (τ) + β_{2} (τ) x_{2, i t} + \dots + β_{n} (τ) x_{n, i t} + u (τ) i_{t} \\ i = 1, \dots, p \end{matrix}

(3)

In Equation (3), the coefficients are dependent on the nth quantile.

Cross-sectional Dependence Test

Table 2 presents the outcomes of the cross-sectional dependence (CSD) test, which reveals that all the variables (CO₂E, EGR, INQ, TIN and POPU), except TO, have the issue of CSD. In such a situation, the use of second-generation estimation techniques is likely to provide reliable results because second-generation techniques can control the issue of CSD.

Table 2.

Cross-Sectional Dependence Test.

Variables	CD Test	p-Value
CO₂E _it	15.54495	0.000
EGR _it	20.10346	0.000
INQ _it	2.071931	0.0383
TIN _it	19.56313	0.000
TO _it	0.998520	0.318
POPU _it	11.05465	0.000

Source: The authors.

Results and Discussion

Table 3 demonstrates the characteristics of all the indicators. The mean values of CO₂E, EGR, INQ, TIN, TO and POPU are 1.12, 8.54, 0.75, 11.49, 3.78 and 19.36, respectively. Similarly, the maximum values of CO₂E, EGR, INQ, TIN, TO and POPU are 2.45, 9.62, 1.38, 14.55, 4.56 and 21.05, respectively. Furthermore, the p-values of CO₂E, EGR, INQ, TIN, TO and POPU are <0.05, which confirms the absence of data normality.

Table 3.

Summary Statistics.

	CO₂E	EGR	INQ	TIN	TO	POPU
Mean	1.128287	8.546073	0.755195	11.49516	3.788053	19.36455
Minimum	−0.234883	6.567978	0	9.893084	2.74955	17.9002
Median	1.161494	8.987629	0.756122	11.32921	3.876759	19.06322
Maximum	2.454443	9.620394	1.386294	14.55955	4.566286	21.05453
Kurtosis	2.147782	2.296823	3.686251	4.537952	3.346957	1.829347
Skewness	0.223157	−0.788942	−0.890744	1.276398	−0.881282	0.568217
SD	0.732863	0.844969	0.301637	1.021855	0.342773	1.063958
Jarque–Bera	6.208375	20.01884	24.44944	59.58386	21.64788	17.857
Prob. value	0.044861	0.000045	0.000005	0	0.00002	0.000133
Observations	161	161	161	161	161	161

Source: The authors.

After checking the characteristics of the data, we applied the unit root test to check the stationarity of the variables. For this purpose, we applied the CADF and Im-Pesaran-Shin tests; the results of these tests are presented in Table 4, which confirm that all the indicators are stationary. These outcomes allow us to apply the cointegration and long-run estimations to our data.

Table 4.

Results of Stationary Analysis.

Variables	Cross-Sectionally Augmented Dickey–Fuller (CADF)				Order of Integration
	I(0)		I(1)
	t-Statistics	p-Value	t-Statistics	p-Value
CO₂E _it	−2.01**	0.02			I (0)
EGR _it	−1.37*	0.08			I (0)
INQ _it	−1.34*	0.08			I (0)
TIN _it	−0.99	0.16	−3.608***	0.000	I (1)
TO _it	−0.16	0.43	−5.323***	0.000	I (1)
POPU _it	−2.05**	0.02			I (0)
lm, Pesaran and Shin (2003)
CO₂E _it	1.70	0.95	−4.55***	0.00	I (1)
EGR _it	3.28	0.99	−9.58***	0.00	I (1)
INQ _it	−2.46***	0.00			I (1)
TIN _it	0.47	0.68	−5.82***	0.00	I (1)
TO _it	−0.17	0.42	−6.97***	0.00	I (1)
POPU _it	1.04	0.85	−2.82***	0.00	I (1)

Source: The authors.

Note: *, ** and *** Show significant levels at the 10%, 5% and 1%, respectively.

Tables 5 and 6 present the empirical results of three (Pedroni, Kao, and Johansen) different cointegration techniques. The results of these techniques confirm the presence of cointegration among the indicators. As 4 statistics out of 7 statistics of Pedroni verifies the presence of cointegration. Similarly, the p-values of Kao and Johansen are also <0.05, providing the same evidence of cointegration.

Table 5.

Panel Cointegration.

Estimates	Stats.	Prob.
CO₂E _it = f (EGR _it + INQ _it + TIN _it + TO _it + POPU _it )
Panel vs. statistics	−0.752387	0.7741
Panel rho statistics	2.682957	0.9964
Panel PP statistics	−1.639674	0.0505
Panel ADF statistics	−1.510710	0.0654
Alternative hypothesis: individual AR coefficient
Group rho statistic	3.493799	0.9998
Group PP statistic	−5.051682	0.0000
Group ADF statistic	−1.756627	0.0395

Source: The authors and Pedroni (2004).

Table 6.

Johansen Fisher Panel Cointegration Test.

Hypothesiz-ed No. of CE(s)	Fisher Stat.
Hypothesiz-ed No. of CE(s)	From Max-eigen test	p-Value	From Trace Test	p-Value
None	362.2	0.0000	178.7	0.0000
At most 1	325.1	0.0000	201.2	0.0000
At most 2	227.5	0.0000	120.8	0.0000
At most 3	133.1	0.0000	88.98	0.0000
At most 4	69.21	0.0000	49.98	0.0000
At most 5	48.42	0.0000	48.42	0.0000
Kao-cointegration test
ADF			t-Statistics	p-Value
ADF			−2.559062	0.0052

Source: The authors.

After confirming the presence of CSD issue, stationarity and cointegration among the variables, we move towards long-term elasticities. For this purpose, we applied the feasible generalized least square (FGLS) technique. This technique can control the issue of CSD. The results of FGLS are presented in Table 7, which shows that EGR increases environmental pollution. As 1% upsurge in EGR surges CO₂E by 1.650%. This outcome suggests that when economic activities increase, it leads to an increase in the energy demand, and most of the time, emerging economies meet their energy requirements from fossil fuels, which increase the CO₂E. The same outcomes were found in the study by Sun et al. (2022), Anwar et al. (2021b), The Phan et al. (2021) and Meo et al. (2020).

Table 7.

Feasible Generalized Least Square.

	Coefficient	SE	t-Statistics	p-Value
EGR _it	1.650	0.109	15.03	0.000
INQ _it	−0.258	0.095	−2.71	0.007
TIN _it	−0.608	0.758	−8.03	0.000
TO _it	1.015	0.102	9.90	0.000
POPU _it	1.242	0.115	10.74	0.000

Source: The authors.

INQ is also a major determinant for mitigating carbon emissions. In the present study, we used the control on the corruption index as a measure for INQ. The outcomes demonstrate that a 1% rise in INQ mitigates CO₂E by 0.258%. This result proposes that strengthening the institutions would discourage rent-seeking behaviour; in such a situation, environmental degradation would be reduced. This result is in line with Rizk and Slimane (2018), Tang et al. (2021), and Haldar and Sethi (2021). Similarly, TIN is also useful in reducing environmental pollution. The estimated outcomes verified that a 1% upsurge in TIN impedes CO₂E by 0.608%. Therefore, these results advocate in favour of inspiring the E7 countries to the adoption of green energy-based advanced technology, which is environmentally friendly as opposed to outdated fossil fuel-based technologies. This outcome is analogous to the discoveries of Chen and Lei (2018), Omri and Tarek (2020), and Saleem et al. (2020).

Moreover, the TO increases CO₂E. A 1% rise in TO increases the CO₂E by 1.015%. This result implies that trade liberalization has an adverse influence on the quality of the environment. This result is similar to the findings of Jalil and Feridun (2011), and Mahmood et al. (2019). Finally, population growth also increases environmental pollution in E7 countries. A 1% rise in POPU increases the CO₂E by 1.242%. This is understandable as when POPU increases, there is a demand for more infrastructure, leading to an increase in the demand for goods and services, which cause an increased use of fossil fuel-based energy. Thus, it leads to reduced environmental quality.

Figure 3.

Source: The authors.

Figure 3 indicates that the data of this study are not normally distributed, and contain outliers; therefore, in this situation, the application of linear regression models may provide misleading outcomes. Thus, to get a true picture of the relationship between the variables, we use QR, which provides the series of coefficient according to the nature of data.

In Table 3, the p-values of the JB test suggest that the data are not normal. Similarly, the high and low unbalanced values in Figure 3 also depict that the data are not normally distributed. Therefore, we use QR, which provides us a series of coefficients according to the nature of data. Table 8 presents the outcome of QR. EGR increases the CO₂E across all the quantiles (10th–90th). This outcome is parallel to Xu et al. (2021), Chien et al. (2021) and Suki et al. (2020). Environmental quality is also attached to the rules and regulations of society, which are enforced by the institutions. Therefore, INQ should be good enough to control environmental pollution. Our results display that INQ decreases the CO₂E from middle to high quantile (30th–90th quantile). This result verifies the effectiveness of institutions of E7 economies for better environmental quality. The value of coefficients is small, which suggests that these economies should further strengthen their institutions to discourage rent-seeking behaviour. This evidence is similar to Anwar et al. (2021d), Danish et al. (2019) and Bhattacharya et al. (2017). Similarly, TIN is also an effective determinant that increases environmental quality. The result shows that TIN is reducing CO₂E across all the quantiles. This result represents the efforts of E7 economies for the adoption of modern and green energy-based technologies. This outcome is analogous to the prior studies of Razzaq et al. (2021), Godil et al. (2021), and Destek and Manga (2021). TO also increases CO₂E across all the quantiles (10th–90th). This finding is similar to Mahmood et al. (2019), and Jalil and Feridun (2011).

Table 8.

Results of Panel Quantile Estimations.

Variables	Values	Grid of Quantiles
Variables	Values	0.10	0.20	0.30	0.40	0.50	0.60	0.70	0.80	0.90
EGR _it	Coeff.	1.048***	1.441***	1.805***	1.775***	1.732***	1.576***	1.531***	1.503***	1.383***
	SE	0.149	0.146	0.170	0.150	0.149	0.143	0.165	0.147	0.122
	t-Stat.	7.031	9.813	10.56	11.81	11.55	11.01	9.249	10.17	11.32
	Prob.	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
INQ _it	Coeff.	0.037	0.011	−0.256	−0.491***	−0.496***	−0.631***	−0.420***	−0.270***	−0.368***
	SE	0.136	0.134	0.156	0.138	0.137	0.131	0.152	0.135	0.112
	t-Stat.	0.275	0.087	−1.635	−3.558	−3.604	−4.805	−2.764	−1.997	−3.280
	Prob.	0.783	0.930	0.104	0.000	0.000	0.000	0.006	0.047	0.001
TIN _it	Coeff.	−0.283***	−0.450***	−0.676***	−0.621***	−0.602***	−0.501***	−0.586***	−0.574***	−0.524***
	SE	0.104	0.103	0.120	0.105	0.105	0.100	0.116	0.104	0.086
	t-Stat.	−2.703	−4.359	−5.622	−5.874	−5.704	−4.982	−5.030	−5.519	−6.096
	Prob.	0.007	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
TO _it	Coeff.	0.999***	1.267***	1.441***	1.144***	1.075***	0.995***	0.925***	0.979***	1.122***
	SE	0.128	0.126	0.147	0.129	0.129	0.123	0.143	0.127	0.105
	t-Stat.	7.760	9.994	9.766	8.814	8.301	8.054	6.474	7.674	10.63
	Prob.	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
POPU _it	Coeff.	0.640***	0.982***	1.348***	1.273***	1.231***	1.071***	1.146***	1.123***	1.041***
	SE	0.159	0.157	0.183	0.161	0.160	0.153	0.177	0.158	0.130
	t-Stat.	4.010	6.240	7.358	7.904	7.663	6.989	6.457	7.095	7.952
	Prob.	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

Source: The authors.

Note: ***, ** and * Represent significant levels at the 1%, 5% and 10%, respectively.

Moreover, POPU upsurges the COE across all quantiles (10th–90th). The demand for goods and services increases with the increase in population, creating pressure on economic activities, and then more energy is used that creates environmental pollution.

Figure 4 presents the graphical representation of the outcomes of FGLS and QR. In the case of FGLS, the coefficient of EGR is 1.650, while QR shows that the coefficients of EGR lie between 1.048 and 1.805. Similarly, FGLS shows that the coefficients of INQ, TIN, TO and POPU are −0.258, −0.608, 1.015 and 1.242, respectively. On the other hand, QR shows that the coefficients of INQ, TIN, TO and POPU lie between −0.631 and 0.037, −0.676 and −0.283, 0.925 and 1.441, and 0.640 and 1.328, respectively.

Table 9.

Variance Decomposition of CO₂E.

Period	SE	CO₂E _it	EGR _it	INQ _it	TIN _it	TO _it	POPU _it
1	0.043885	100.0000	0.000000	0.000000	0.000000	0.000000	0.000000
2	0.066891	98.49455	0.217081	6.48E-05	0.670192	0.021773	0.596336
3	0.088428	95.95977	1.141609	0.436950	0.711683	0.013165	1.736828
4	0.107309	93.48608	2.201416	0.592825	0.545373	0.046568	3.127739
5	0.124390	90.83818	3.401395	0.614068	0.411429	0.134705	4.600227
6	0.139897	88.23415	4.551078	0.564836	0.327154	0.242275	6.080508
7	0.154134	85.79588	5.540571	0.499153	0.270627	0.358461	7.535308
8	0.167328	83.58059	6.327920	0.439816	0.230021	0.483770	8.937879
9	0.179651	81.59906	6.939409	0.391310	0.199648	0.611345	10.25923
10	0.191230	79.84483	7.413977	0.352299	0.176242	0.733374	11.47928
Variance decomposition of EGR
Period	SE	CO₂E _it	EGR _it	INQ _it	TIN _it	TO _it	POPU _it
1	0.032079	34.32701	65.67299	0.000000	0.000000	0.000000	0.000000
2	0.054067	32.16595	67.32329	0.020772	0.008887	0.108656	0.372448
3	0.076538	30.68240	67.60400	0.212293	0.073379	0.365787	1.062147
4	0.097752	29.17048	67.72826	0.341948	0.182260	0.658149	1.918907
5	0.117771	27.63963	67.91814	0.388949	0.269682	0.913158	2.870434
6	0.136466	26.19425	68.02896	0.381398	0.336219	1.151467	3.907705
7	0.153949	24.86381	67.98256	0.353152	0.392390	1.390213	5.017870
8	0.170352	23.64605	67.78188	0.320645	0.439172	1.629115	6.183142
9	0.185819	22.53093	67.45449	0.289669	0.477396	1.863643	7.383875
10	0.200482	21.50810	67.02641	0.261891	0.509264	2.091848	8.602493
Variance decomposition of INQ
Period	SE	CO₂E _it	EGR _it	INQ _it	TIN _it	TO _it	POPU _it
1	0.171831	1.113700	0.234354	98.65195	0.000000	0.000000	0.000000
2	0.229064	4.284669	0.650938	91.78182	1.706601	0.696686	0.879287
3	0.257613	5.113171	1.703218	86.33496	2.956304	1.506138	2.386209
4	0.275273	5.590212	3.564505	80.55356	3.565424	2.564578	4.161722
5	0.287071	5.734506	5.173956	75.91250	3.838423	3.573633	5.766984
6	0.295897	5.755042	6.341222	72.42002	4.031917	4.414501	7.037299
7	0.302829	5.712043	7.113962	69.85437	4.192570	5.128157	7.998896
8	0.308580	5.636918	7.637678	67.90586	4.335279	5.767124	8.717140
9	0.313536	5.546093	8.009079	66.37108	4.474962	6.348285	9.250503
10	0.317929	5.449400	8.291146	65.12105	4.619755	6.875890	9.642758
Variance decomposition of TIN
Period	SE	CO₂E _it	EGR _it	INQ _it	TIN _it	TO _it	POPU _it
1	0.120719	8.581002	7.828498	0.564257	83.02624	0.000000	0.000000
2	0.183365	12.33442	6.504338	1.498671	78.92655	0.323074	0.412946
3	0.221753	14.91676	5.233531	1.313802	76.56739	0.235837	1.732683
4	0.253399	16.56965	4.706844	1.133730	73.93400	0.277590	3.378184
5	0.282948	16.80308	4.482620	1.106588	72.27572	0.294174	5.037827
6	0.310510	16.46985	4.390921	1.227080	70.93870	0.315758	6.657692
7	0.336031	15.94990	4.309164	1.409105	69.73521	0.369733	8.226884
8	0.360010	15.37099	4.222486	1.583910	68.69422	0.441024	9.687377
9	0.382765	14.78808	4.129511	1.734324	67.83667	0.511378	11.00004
10	0.404382	14.24325	4.033927	1.859745	67.12941	0.576522	12.15714
Variance decomposition of TO
Period	SE	CO₂E _it	EGR _it	INQ _it	TIN _it	TO _it	POPU _it
1	0.081263	7.993462	0.401187	8.811536	0.074917	82.71890	0.000000
2	0.116998	12.53917	0.198374	9.631934	0.621302	77.00916	5.69E-05
3	0.142123	11.68060	0.728316	12.99401	3.811238	70.78106	0.004771
4	0.165018	10.25597	1.624685	17.79132	5.432445	64.87224	0.023347
5	0.187570	8.628069	2.899911	22.09027	5.956699	60.36865	0.056399
6	0.209045	7.266505	4.114533	25.40514	6.324902	56.80685	0.082073
7	0.229334	6.187594	5.251968	27.93104	6.723822	53.81268	0.092901
8	0.248429	5.356205	6.315999	29.80130	7.054415	51.38137	0.090704
9	0.266340	4.716301	7.297382	31.15724	7.310425	49.43767	0.080981
10	0.283117	4.219500	8.175511	32.15552	7.530560	47.84660	0.072310
Variance decomposition of POPU
Period	SE	CO₂E _it	EGR _it	INQ _it	TIN _it	TO _it	POPU _it
1	0.000284	0.019210	0.007736	0.600559	1.297793	5.146087	92.92861
2	0.000859	0.048364	0.138682	0.625270	0.984862	7.025401	91.17742
3	0.001752	0.383984	0.144738	0.688755	0.820097	7.736771	90.22565
4	0.002963	0.922068	0.132385	0.751060	0.710271	8.063191	89.42102
5	0.004486	1.529862	0.122206	0.822044	0.645280	8.275937	88.60467
6	0.006303	2.136718	0.118546	0.886017	0.602985	8.439026	87.81671
7	0.008394	2.709181	0.120458	0.937350	0.570956	8.565746	87.09631
8	0.010734	3.234091	0.126689	0.975926	0.544850	8.664230	86.45421
9	0.013300	3.708074	0.136056	1.003481	0.523250	8.741893	85.88725
10	0.016070	4.132794	0.147525	1.022290	0.505251	8.804063	85.38808

Source: The authors.

Note: Cholesky ordering: CO₂E _it , EGR _it , INQ _it , TIN _it , TO _it and POPU _it .

Figure 4.

Source: The authors.

Subsequently confirming the long-term affiliation, we used generalized forms of forecast error variance decomposition method (FEVDM) and impulse response function (IRF). Table 9 presents the outcomes of variance decomposition analysis (VDA), as the change in endogenous variable, due to its own and other variables. CO₂E, VDA results show that 75.82% change in CO₂E is due to its own shocks, while 7.413%, 0.352%, 0.176%, 0.733% and 11.479% change in CO₂E is due to EGR, INQ, TIN, TO and POPU, respectively. Similarly, 67.129% change in TIN is due to its own shocks, while 14.243%, 84.033%, 1.859%, 0.576% and 12.157% are due to CO₂E, EGR, INQ, TO and POP, respectively. On the other hand, EGR, INQ, TO and POP are contributed by CO₂E as 21.508%, 5.449%, 4.219% and 4.132%, and their contribution is 67.026%, 65.121%, 47.846% and 85.388%, respectively.

Figure 5.

Source: The authors.

Furthermore, FEVDM results of CO₂E, EGR, INQ, TIN, TO and POPU are projected with the help of IRF and presented in Figure 5. The connection between the five primary indicators was binary and measured as an exogenous response among the series of indicators. All these graphs present the same result as we draw from the econometric analysis and show the robustness of our results for a suitable policy. All graphs have presented the response function of numerical FEVDM method findings graphically. The findings of VDA are similar by estimates of the long term, which confirm the robust econometric modelling and findings, which are suitable for policy purposes.

Conclusion and Policy Implications

The present study is a pioneering attempt to reveal the quantile behaviour of the relationship among TIN, INQ and CO₂E. In doing so, we used the panel QR on the data set of E7 countries during the period from 1996 to 2018. The findings of this study show that the association between the indicators is quantile-based, and the previously used OLS/ARDL and other linear techniques may provide misleading outcomes. Unlike traditional techniques, panel QR provides a series of coefficients, according to the shocks, which occur in the data. The results demonstrate that EGR, TO and POPU increase carbon emissions, whereas TIN and INQ reduce environmental pollution.

From empirical outcomes, we propose the following policy framework for achieving SDG 8 (EGR), SDG 9 (innovations), SDG 16 (government institutions) and SDG 13 (climate change) for E7 countries.

EGR increases the carbon emission in E7 countries; therefore, these economies must redesign their growth pattern and replace their outdated, polluted and fossil fuel-based technology with advanced and green energy-based technology.

TIN reduces carbon emission with more intensity in the low-emission countries and has a smaller effect in high-emission countries. Therefore, the governments of E7 countries should further promote the technological process in their countries by improving the energy efficiency, by carrying out more research and by developing green energy, building such instruments that control or capture the emissions of industries, upgrading production processes and manufacturing hybrid or electric vehicles.

The governments of E7 countries should further empower the institutions so that these institutions can implement environment-related policies. If institutions are independent, they can enforce rules and regulations more efficiently with no political pressure. The independent institutions can help to reduce carbon emissions in several ways, such as applying carbon tax on polluting industries, administering polluting industries to adopt advanced technology, granting subsidies on green energy and through efficient allocation of natural resources.

Footnotes

Acknowledgement

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of this 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 iDs

Ahsan Anwar

Summaira Malik

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Cogitating the role of Technological Innovation and Institutional Quality in Formulating the Sustainable Development Goal Policies for E7 Countries: Evidence from Quantile Regression

Abstract

Keywords

Graphical Abstract

Introduction

Literature Review

Theoretical Development

Empirical Evidence

Technological Innovations and Emission Nexus

Institutional Quality and Emission Nexus

Methodology

Data Source and Sample Period

Data Depiction.

Model Specification

Estimation Technique

Panel Quantile Regression

Cross-sectional Dependence Test

Cross-Sectional Dependence Test.

Results and Discussion

Summary Statistics.

Results of Stationary Analysis.

Panel Cointegration.

Johansen Fisher Panel Cointegration Test.

Feasible Generalized Least Square.

Results of Panel Quantile Estimations.

Variance Decomposition of CO2E.

Conclusion and Policy Implications

Footnotes

Acknowledgement

Declaration of Conflicting Interests

Funding

ORCID iDs

References

Variance Decomposition of CO₂E.