Role of institutions in correcting environmental pollution: An empirical investigation

Journal Pre-proof Role of institutions in correcting environmental pollution: An empirical investigation Syed Tauseef Hassan, Danish, Salah Ud-Din Khan, Enju Xia, Hani Fatima

PII:

S2210-6707(19)31274-0

DOI:

https://doi.org/10.1016/j.scs.2019.101901

Reference:

SCS 101901

To appear in:

Sustainable Cities and Society

Received Date:

6 May 2019

Revised Date:

30 September 2019

Accepted Date:

16 October 2019

Please cite this article as: Hassan ST, Danish, Khan SU-Din, Xia E, Fatima H, Role of institutions in correcting environmental pollution: An empirical investigation, Sustainable Cities and Society (2019), doi: https://doi.org/10.1016/j.scs.2019.101901

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Role of institutions in correcting environmental pollution: An empirical investigation

Syed Tauseef Hassan b, Danish a *, Salah Ud-Din Khan c, Enju Xia b, Hani Fatima d

a

School of Economics and Trade, Guangdong University of foreign studies, Guangzhou 510006, China b

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School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China c

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Sustainable Energy Technologies (SET) Center, College of Engineering, King Saud University, PO-Box 800, Riyadh 11421, Saudi Arabia d

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The Centre for Experimental Economics in Education, Shaanxi Normal University, Xi'an China

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* Corresponding Author:

Highlights

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Danish School of Economics and Trade, Guangdong University of foreign studies, Guangzhou 510006, China [email protected]

This study assessed the overarching nexus between environment and economic policy

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We employed Autoregressive-Distributed Lag model

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Evidence shows Institutions affects long term CO2 emissions.

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New framework of EKC suggest, economic growth reduce CO2 emission over time

Abstract

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A growing literature has highlighted that institutional quality is an effective tool for ensuring a country’s sustainability. Institutions play a significant role in the country’s development, and specifically in terms of air pollution. How institutional quality enhances or weakens air quality is not been extensively estimated in the literature. This study takes a step forward to investigate the role of institutional quality in CO2 emissions in Pakistan. An autoregressive distributed lag model (ARDL) is used for data spanning from 1984 to 2014 in the context of Pakistan. The

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result indicates cointegration among variable under consideration. Overall, empirical

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results infer that institutions result in increasing CO2 emissions in Pakistan.

Moreover, institutions quality and CO2 emissions granger cause each other. Further,

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finding shows that more income reduces CO2 emissions over time, which validates the EKC existence for CO2 emissions. Our findings suggest there is a need to

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strengthen institutions to mitigate the environmental effect.

Keywords: Institutional quality;

ARDL;

Pakistan

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1. Introduction

CO2 emission;

Institutional quality is an important determinant of economic development in

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both developed and developing countries. Strong institutional frameworks oppose corruption, help to establish the rule of law, lessen military intervention in politics, and improves management of public finances. On the contrary, poor institutional quality has a long-term effect on the country’s economic development (Sherani, 2017). Generally speaking, institutional quality is associated with strategies realized by the county’s institutions to set cultural and lawful structures that help socioeconomic and financial exercises take place and directly impact efforts to reduce

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environmental pollution (Salman, Long, Dauda, & Nyarko, 2019). Institutional quality has become the topic of hot debate among researchers, scientists, and economists in the context of environmental degradation, as institutional quality both directly and indirectly impacts environmental quality. The role of institutions in environmental quality is not only decisive and valuable, but supports the idea that good quality institutions lessen the costs of higher economic growth, and, consequently, countries can enjoy a higher income in future by improving

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environmental quality (Rizk, 2018). Solid guidelines from institutions and a strong

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rule of law can force companies to control their carbon dioxide (CO2) emissions. Good institutional quality is necessary to reduce environmental pollution and

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environmental sustainability (Asongu & Odhiambo, 2019). Efficient institutions reduce the cost of transactions and facilitating transactions improves financial

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performance. Institutional reforms are crucial for developing countries because of

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their limited fiscal space and dependence on investment inflows to growth. Therefore, institutional quality could be viewed as an input to support sound legislation applied to reduce environmental pollution (Lau, Choong, & Eng, 2014).

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Pakistan has the worst institutional quality among developing countries, which is affecting economic growth and environmental quality (Álvarez, Barbero, Rodríguez-

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Pose, & Zofío, 2018). Pakistan’s increasing environmental issues need correction (Danish, Hassan, Baloch, Mehmood, & Zhang, 2019; Danish, Zhang, Wang, & Wang, 2018; Hassan, Xia, Khan, Mohsin, & Shah, 2018), and improving institutional quality will be a positive contribution to controlling pollution (Khan, Khan, Ali, Ahmad, & Ahmad, 2016). We choose Pakistan as a context because the institutional quality in Pakistan is low. Pakistan is positioned 29th in governmental effectiveness, 27th in regulatory quality, and 20th in the rule of law among

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developing countries (Kugelman & Husain, 2018). The institutional failure in Pakistan may be one of the main reasons for its poor environmental policies which need investigation. Numerous studies have been conducted on environmental pollution and its causes. the existing literature has begun to explore several factors of increasing CO2 emissions, including tourism (Danish & Wang, 2019b), biomass energy consumption (Danish & Wang, 2019a), human capital (Bano, Zhao, Ahmad, Wang,

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& Liu, 2018), natural resources (Balsalobre-lorente, Shahbaz, Roubaud, & Farhani,

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2018), corruption (Sinha, Gupta, Shahbaz, & Sengupta, 2019), energy consumption (Sinha, Shahbaz, & Balsalobre, 2017), forestry and agriculture (Waheed, Chang,

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Sarwar, & Chen, 2018), population density (Rahman, 2017), renewable and nonrenewable energy (Danish, Zhang, Wang, & Wang, 2017a), trade openness (Al-

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Mulali, Ozturk, & Lean, 2015); electricity consumption (Shahbaz, Sbia, Hamdi, &

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Ozturk, 2014), financial development (Salahuddin, Alam, Ozturk, & Sohag, 2018); globalization (Shahbaz, Mallick, Mahalik, & Loganathan, 2015), imported technology (Danish, Wang, & Wang, 2018), urbanization (Shahbaz, Loganathan,

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Muzaffar, Ahmed, & Ali Jabran, 2016), and energy production (Danish, Zhang, Wang, & Wang, 2017b). But little is known about the institution's role in the

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environment.

Given the above discussion, a much better understanding of the nexus among

institutional quality, income, and CO2 emissions is needed that would be beneficial for policy analyst and government officials in envisioning long - and short-run policies for addressing climate change mitigation. On this note, this research seeks the role of institutional quality in mitigating CO2 emissions within the EKC

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framework for Pakistan. From the country-development perspective, institutional quality is important for both economic and environmental development. Our research makes several key contributions to the literature. First, to the best of author’s knowledge, it is the first empirical study of Pakistan that considered the role of institutional quality in environmental pollution reduction. Second, following the pathways of (Narayan & Narayan, 2010) method, the present study gives a new insight to test EKC hypothesis for carbon emissions in Pakistan; this could prevent

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the issue of either collinearity or multicollinearity, and also fill the academic gap in

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the already existing EKC literature in Pakistan. Finally, the study used more robust technique; ARDL bounds testing approach using the pathways of (Kripfganz &

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Schneider, 2018) for critical value and p-value approximation and that produce more robust estimates.

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The rest of the paper is arranged as follows: Section 2 provides a literature

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review and defines the gap in the literature that this research seeks to fill. Section 3 describes the data source, the model specification, and estimation techniques. Section 4 explains our empirical analysis and discussion, and Section 5 concludes

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the study with policy implications.

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2. Literature review

The theoretical foundation of the study is based on the environmental Kuznets

curve (EKC) hypothesis. Grossman et al. (1991) introduced the EKC hypothesis, proposing that economic growth and pollution have an inverted U-shaped relationship, so an increase in income per capita improves the environmental situation in the country to a point at which increasing income per capita begins to have a negative effect on the environment. Economic growth promotes replacing

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dirty technologies with new, advanced technologies, which is helpful for environmental sustainability (Dinda, 2004). Economic development influence environment in three ways; first scale effect, where income causes to increase pollution because during the scale effect economic development increases the natural resource extraction leading to an increase in waste generation (Sarkodie & Strezov, 2019). Second, the composition effect reduces the adverse effect of income through economy’s structural changes. Third, pollution reduces through

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environmental-friendly technologies and stringent environmental measures; and this

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is known as the technique effect (Destek & Sarkodie, 2019). When composition and technique effect become dominant over scale effect it forms an inverted U-shape

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relationship between income and pollution (see Fig.1). Also, further progress in the

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economy starts decline environmental degradation (Shahbaz & Sinha, 2018).

Figure 1: It shows the relationship between per capita income and pollution (the wellknown EKC curve) (Mahmood et al. 2019; Sarkodie and Strezov 2018)

Meanwhile, economic growth alone cannot minimize pollution but together with environmental regulation measures (Lorente & Álvarez-Herranz, 2016), and strong institutional framework is necessary for effective implementation of environmental 6

measures that reduce pollution. Several institutional indicators affect the environment, suggesting that better quality of institutions leads to better government. The influence of institutional quality on economic development and environmental policy cannot be ignored, as institutional quality, economic growth, CO2 emissions, and energy consumption should be linked. We divided the extant research into two sections: (i) that which emphasizes the nexus between institutional quality and CO2 emissions (ii) that which deals with the relationship of institutional

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quality with economic growth and CO2 emissions.

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As for the literature that addresses the relationship between institutional quality and CO2 emissions, scholars have reached conflicting conclusions. Some scholars

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have found that aspects of institutional quality like corruption and democracy can reduce CO2 emissions (Wang, Danish, Zhang, & Wang, 2018b), while others have

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found that such institutional aspects may cause to increase CO2 emissions (Hosseini

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& Kaneko, 2013; Yamineva & Liu, 2019). Also, Zakaria and Bibi (2019) examined the impact of economic development and institutional quality on CO2 emissions in South Asian countries and found that institutions were negatively related to CO2

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emissions. Abid (2016) pointed out that political and social insecurity, government effectiveness, corruption, and democracy are also negatively related to CO2

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emissions. Zhang, Jin, Chevallier, & Shen, (2016) found that institutional quality may not have only a direct negative influence on environment but indirect as well. Control of corruption in institutions improve quality of environment in APEC countries. According to Ibrahim and Law (2016), changes in institutional quality reduces environmental pollution, as institutional reform is a necessary condition for countries with low institutional quality to realize the favorable environmental impact of trade. On the other hand, Andersson (2018) discussed that weak institutional

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quality, trade openness, the exchange rate policy, and legitimate property rights influence CO2 emissions. Dasgupta and Cian (2016) used econometric approaches to determine the influence of institutional quality on the environment and found that institutions and governance are responsible for environmental degradation. The second strand of the research deals with the relationship between institutional quality and economic growth. Over the past few decades, the role of institutional quality on economic development has received significant attention, but

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the evidence regarding the impact of institutions on economic growth differs. Some

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studies have pointed out that institutionally related variables affect economic growth (Lee, Chang, Arouri, & Lee, 2016), but after reaching a certain institutional quality

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threshold, the significant negative impact becomes insignificant. The role of the

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system in a country’s economic growth cannot be overemphasized (Kenneth 2015; Ugbem 2017), and others have agreed that institutional quality affects economic

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growth. However, Egbetokun et al. (2018) posited that only efficient institutions contribute to economic growth and encourage individuals to participate in

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productive activities. Good institutions provide appropriate stable structures to human interactions, which reduces uncertainty. Akinyemi et al. (2017) argued that

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institutions play a key role for the environment, which has a direct impact on economic growth, income inequality (Perera & Lee, 2013), and bank regulation and supervision (Bermpei, Kalyvas, & Nguyen, 2018). Similarly, results have indicated that improvements in the area of accountability, bureaucratic quality, and corruption are responsible for worsening of income distribution (Alemu, 2015). The nexus between institution quality and CO2 emission is a hot issue of debate for research, and the mechanism for the institutional role in carbon emissions is complex whether or not institution reduce pollution. Few available studies on such 8

evidence need further investigation since results from already existing studies focusing on these inter-linkages is rather unclear and meager. Also, such evidence was ignored in the context of Pakistan. Due to inadequate investigation between underlying variables, inconclusive findings, drawbacks in methodologies and insufficient evidence in some group of countries motivate us to investigate the causal linkage between institutional quality, income and carbon emission

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considering energy consumption for Pakistan.

3.1.

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3. Material and Methods Model construction

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Before discussing the model specification, we elaborate on the study’s conceptual framework, which supports our thoughts on the model’s variables. Efficient

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institutions make a significant contribution to reducing CO2 emissions (Lau et al.,

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2014), as they reduce transaction costs, facilitating financial performance. Institutional quality is especially important for developing countries because of their limited fiscal space and dependence on investment inflows. However, the uneven

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implementation of these transformational reforms has led to an ineffective institutional environment. The economic growth rate decreases day by day in

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Pakistan because of the country’s terrible institutional quality, so it is necessary to make proper institutional reforms (Younis, 2015). Institutional quality also affects countries’ economies through their energy sectors. For example, energy has a direct relationship with technological advancement, education, and integrated communication technology (Sinha & Shahbaz, 2018; Wang, Danish, Zhang, & Wang, 2018a). Quality of institutions ensure sustainable use of natural resources and protect environment (Abdala, 2008). Institutions play a key role in climate change

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mitigation and influence CO2 emissions, directly and indirectly through economic growth (Abid, 2016; Danish, Wang, & Baloch, 2019). Poor institutional quality enhances corruption, provide relaxation in implementation of environmental measures and makes rent-seeking behavior (Wang et al., 2018b). The arguments mentioned above show that the role of institutional quality is vague, so the effect of institutional quality, economic growth, economic growth square, and energy

(1)

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ln(CO2 )t  0  1 ln(Yt )  2 ln( IQt )  3 ln( ECt )  t

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consumption on CO2 emissions is expressed as:

Where CO2 refers to carbon dioxide in metric tons per capita, Y is real income

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measure in per capita, IQ is institutional quality and EC is energy consumption. 0

3.2.

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shows error term  0 is the intercept term, and subscripts t refer to time.

Econometric strategy

The empirical investigation starts with checking level of stationarity of the

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study’s variables. The stationarity level of data series exhibit importance for the selection of further econometric tests used in the analysis such as cointegration tests

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and long-run test. The Zivot & Andrews, (1992) unit root test is used that capture structural break in the data series. Later, the bound testing procedure is used by Pesaran, (2007) through pathways of (Kripfganz & Schneider, 2018) to analyzed the cointegration levels among study’s variables. ARDL model was further employed for both the long and short-term coefficients. ARDL has several advantages; it is used for the estimation of short-run and long-run results because the variables are integrated with l(0) or I(1), and the variables of the series are restricted with l(2) in

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the ARDL method. In addition, when the ARDL technique is used, a suitable lag is selected to handle the endogeneity problem. For ARDL method it is not necessary that all variables be stationary in the same order. It is best for small data sets. It produces unbiased estimates of long-run models. ARDL is accurate and easily understandable and calculates long-run and short-run estimations, so the choice of ARDL is appropriate because our study considers long-run dynamics. To assess the relationship between the study’s variables in Eq. (1), the ARDL

p

q

r

i 1

i 1

i 1

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method estimates the unrestricted error correction (UREC) as follow:

i 1

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t i

 1 ln(CO2 )t 1  2 ln(Y)t 1  3 ln(IQ)t 1 

(2)

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s

  (ln(EC)

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(ln(CO2 )t )   0   1(ln(CO2 ) t i    2 (ln(Y) t i   3  (ln(IQ)t i 

4 ln(EC)t 1  t

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Where Δ is the first difference operators; β1, 2, 3, and 4 represent the short-run results and λ1, 2, 3, and 4 show coefficients of long-run; p, q, r, and s are the lag length;

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α0 is a constant value and μ means the error term

The procedure of ARDL lies within four steps; first selection of appropriate lag

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of the underlying model using the Akaike Information criteria (AIC). The next step is to calculate the cointegration with the help of the F value. The equation of the null

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hypothesis is expressed as H0: λ1 ≠ λ 2 ≠ λ 3 ≠ λ 4 ≠ 0, against the alternative hypothesis, H1: λ 1 = λ 2 = λ 3 = λ 4 = 0. Following the pathways of (Kripfganz & Schneider, 2018) for the lower and upper limits such that, if the F-state exceeds the upper limit, the null hypothesis of no cointegration is rejected, which confirm the cointegration of the investigated variables. In the third step, the ARDL has pursued both long and short-run dynamics. The ARDL long-run dynamics the equation can be written as:

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p

q

r

i 1

i 1

i 1

ln(CO2 )t  0   1i (ln(CO2 )t i    2i (ln(Yt i )   3i (ln(IQt i )  (3)

s

 i 1

4i

(ln(ECt i )  t

Next, for estimation of the short-run coefficient of the study’s variables, the following error correction models are estimated: p

q

r

i 1

i 1

i 1

ln(CO2 )t   0   1i (ln(CO2 )t i    2i (ln(Yt i )   3i (ln(IQt i )  s

 4i (ln(ECt i )   ECTt 1  t

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i 1

(4)

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Where ECTt-1 indicates error correction term, which shows the adjustment speed of long-run equilibrium after a shock in the short-run. The ECTt-1 value must be

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negative and significant to meet the estimation criteria of the long-run equilibrium. In the fourth step, after estimating the long-run and short-run dynamics, we

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detect the causality directions among the selected variables. Exploring these

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relationships could be helpful in developing specific policies that enhance economic development and reduce air pollution through institutional quality. We employ the

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causality technique proposed by Engle and Granger (1987). This technique is used for simple regression analysis after generating the series known as Error Correction

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Term (ECT) and to contribute to the policy implications for the countries. After creating the series, we run ECT into the model. The negative and significant values define the long-run equilibrium, and the Wald statistic defines the short-run analysis between the variables. The equations of the causality model can be written as below: ln(CO2 )t  1   11,1 12,1 13,1 14,1  ln(CO2 )t 1   n1   1t  ln(Y)          ln(Y)      t t 1     2    21,1 22,1 23,1 24,1       n2   ( ECT )   2t  t 1 ln( IQ)t   3   31,1 32,1 33,1 34,1  ln( IQ)t 1   n3    3t              ln( EC )t   4    41,1  42,1 43,1 44,1  ln( EC )t 1   n4   4t 

Where ECTt-1 indicates error correction term in t time. 12

3.3.

Data

The time series annual data is used in this study for the years 1984-2014 in Pakistan. The data of CO2 emissions is collected in metric tons and then divide it by total population to get the final data in per capita metric tons (Mt), which retrieved from British Petroleum (PB) statistical review database (BP, 2018). The income is measure in per capita GDP constant 2010 US dollars, while energy consumption is measured in kg of oil equivalents per capita. The data for CO2 emissions, GDP, and

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energy consumption are from the World Development Indicator (WDI) databank.

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The data for institutional quality is sampled from the International Country Risk Guide (ICRG), which is a combination of twelve sub-indices following (Asif &

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Majid, 2018). To ensure reliable and consistent estimate the data series was

transformed into natural logarithm (Shahbaz, Hoang, Mahalik, & Roubaud, 2017;

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Shahbaz, Lahiani, Abosedra, & Hammoudeh, 2018)

4. Results and Discussion

Our study starts with a series of unit root analyses to check the stationarity of

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data series. Unit root analysis is performed to provide information about nonstationary data and stationary data, which is helpful in selecting the appropriate

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cointegration test for the empirical model to obtain reliable empirical results. The unit root tests used in earlier studies are the ADF unit root test (Dickey, Fuller, Dickey, & Fuller, 1979), the P-Perron unit root test (Phillips and Perron 1988), KPSS (Kwiatkowski, Phillips, Schmidt, & Shin, 1992) and N-Perron (Ng & Perron, 2001). These unit root tests are not capable of calculating structural break in series and without the structural break unit root tests give suspicious results. On this note, present study uses Zivot Andrews (ZA) unit root test which has the ability to give

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structural break in the data series. Results of ZA unit root test are reported in Table 1 which shows that the investigated variables are non-stationary at the _5__% level. The results suggest that the null hypothesis of the unit root analysis is not rejected. After taking the first difference, we reject the null hypothesis, and study’s variables are found to be stationary at the first difference. Here it should be mentioned that the structural breaks in the data series are mainly located in between 1997 and 2006, during the period Pakistan’s environmental policy act was introduced in 1997 which

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is later modified in 2003. Table 1: ZA unit root test result

First difference

T-statistic

Prob.

Break year

Ln CO2

-4.307

0.664

2011

Ln Y

-2.891

0.205

2002

Ln IQ

-4.242

0.199

Ln EC

-3.863

0.167

T-statistic

Prob.

Break year

-6.544**

0.0116

2006

-5.641**

0.0103

1997

1992

-5.105**

0.023

1997

2010

5.811**

0.030

2010

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Regressor

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Level

Note: * shows significance level at 5%

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The ARDL bound test results are presented in Table 2 which reveal that the Fstatistics of the study’s model exceeds the critical values for the upper bound, I(1)

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and validated by (Kripfganz & Schneider, 2018), so it suggests to reject the null hypothesis of no cointegration. In other words, variables under consideration are cointegrated and the ARDL model can be pursued.

Table 2: ARDL bounds test.

10%

5%

1%

Model

I(0)

I(1)

I(1)

CO2 = f(Y, IQ, EC)

3.393

4.410

4.183 5.333

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I(0)

I(1)

F-statistic I(0)

6.140 7.607

7.01**

Note: ** shows significance level at 5%

After fulfilling the assumptions of the ARDL model, now we discuss the longrun and short-run dynamics. Here it is important to discuss that EKC is investigated using (Narayan & Narayan, 2010) method, this could minimize the risk of either collinearity or multicollinearity (Dong, Sun, & Dong, 2018). Table 3 shows the ARDL long-run and short-run results. The econometric results reveal that the

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coefficient of income (LnY) is found positive and statistically significant both in the

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long and short-run, signifying that CO2 emissions increases with income. The

carbon emission elasticity to income decreases over time, more precisely, over time,

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magnitude of income decreases from 0.62 to 0.17. This result support the argument that benefit of income for CO2 emissions reduction will be accomplished eventually,

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and that validates the EKC hypothesis between pollution and income in case of

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Pakistan. The evidence suggests that previous policy implications have relied on economic development to reduce CO2 emissions, and the environmental

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consequences can be controlled by increasing the country’s economic growth. The results reveal that energy consumption makes a positive contribution to CO2 emissions in Pakistan. Because Pakistan’s growing industrial sectors increase energy

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demand and country still relies on conventional energy sources like coal, oil, and gas. Dependence on fossil fuel consumption should be reduced while shifting to alternate clean energy sources such as renewable energy. The results for both EKC and energy consumption correspond to (Danish et al., 2017a; Shahbaz, Lean, Shabbir, Hooi, & Shahbaz, 2012) for Pakistan. But our study deviates in a sense, the EKC is validated in the presence of institutional quality.

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The institutional quality results are reported in Table 3, showing that the coefficient of institutional quality is positive and statistically significant, implying that the institution has negative impact on CO2 emissions. This could be corroborated with the poor institution’s role in Pakistan, because of the low level of institutional quality in terms of corruption, political instability, the voice of accountability, religious groups, terrorism, and law and order, the judiciary, and law and order directly affect the institutions and policy implementation. For instance,

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high institutional quality implies access to information and political liberty support

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for a high-quality environment. High corruption level in the country restrict the

government officials from designing and implementation of efficient environmental

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policies. More precisely, poor quality of institution in Pakistan offers investment for both domestic and multinational companies in dirty export-oriented goods and

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provide opportunities for transfer of outdated technology which is not eco-friendly

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and contribute to pollution. More broadly, public policies are not always those that can accomplish well-intended goals, so an integrated approach to reduce pollution and a clean environment is adopted to achieve joint benefits and cost savings and to

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avoid aggressive policy objectives. Poor institutional quality halts an immature economic system and disturbing to the country's total factor productivity (Wang et

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al., 2018b). Institution enhances the effective implementation of environmental policy measures, perhaps due to awareness among citizens and organizations who are worried about ecological issues. Institutional quality sustains natural resource consumption which is good for the environment (Danish, Awais, & Wang, 2019). Findings are contradicting with (Salman et al., 2019) and (Lægreid et al., 2018), all who found institutional quality reduce CO2 emissions. Even though (Asumadu,

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Adams, Sarkodie, & Adams, 2018) found insignificant relationships between institutions and environmental pollution. In order to capture the effect of structural break identifies by ZA unit root test in the data, the effect of the break year 2002 as dummy was inserted in the model. The impact of structural break is significantly negative which can be associated with the event of Asian financial crisis which took place in the late 1990s (Solarin & Shahbaz, 2015). The event of Asian financial crisis formed an economic shock and

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declined the energy demand in Asia, including Pakistan, which reduces CO2

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emissions. Table 3: ARDL long and short-run results Long-run Coefficient

Prob.

Constant

0.6204 *

0.009

Ln GDP

0.1763 **

Ln IQ

0.5861 *

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F-statistic

0.8336 ***

0.095

0.018

0.2319 **

0.003

0.008

0.0209

0.282

1.2313 *

0.000

0.8874 *

0.000

-0.1066 *

0.000

-0.0353 *

0.004

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dummy2002

R2

Prob.

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Ln EC

ECTt-1

Coefficient

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Regressor

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Short-run

D.W

-0.3724 *

0.016

0.99

1516.5 *

0.99 0.000

1.93

1366.6 * 2.2

Diagnostic tests

F-statistic

Prob

χ2- (serial correlation)

2.2922

0.326

χ2- (heteroscedasticity)

1.7646

0.195

χ2- (RESET REMSEY) 0.0212

0.888

Note: * , ** & *** denote significance level at 1%,5% and 10%

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0.000

To ensure the model’s stability several diagnostic tests are applied and outcomes are reported in table 4. The result recommends the absence of multicollinearity, autocorrelation, and heteroscedasticity. In other words, the model is stable and results produce from ARDL method are reliable. We also employ sensitivity analyses like CUMSUM and CUMSUMsq1 which also confirm the model’s stability. After the long- and short-run parameters have been estimated, the next process is

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to find the causal relationships among study’s variables which are shown in Table 4.

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Bidirectional causality exists between institutional quality and energy consumption, between institutional quality and CO2 emissions and between energy consumption

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and CO2 emissions in a long-run path. Our findings are in line with Salman (2019). Moving on to the short-term causality analysis, the results are reported in Table

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4, where economic growth is found to have unidirectional causality with CO2

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emissions. According to the VECM, the causality approach detects a Uni-directional causality from GDP towards CO2 emissions, suggesting that CO2 emissions reduction is not limited to economic growth only. Bidirectional causality is found

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between energy consumption and CO2 emissions, inferring that for Pakistan it hard to decouple CO2 emissions due to higher demand of various energy fuels (Dong et

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al., 2018). Unidirectional causality is running from institutional quality toward CO2 emissions is found, suggesting institutional quality can be helpful in CO2 emissions reduction.

1

CUMSUM and CUMSUMsq are available upon request

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Table 4 Granger Causality Analysis Long-run causality Wald-statistic (t-statistic) ∆Log CO2

∆Log GDP

∆Log IQ

∆Log EC

ecmt-1

Log CO2

-

5.6740* [0.0057]

8.0915* [0.0000]

-2.9402* [0.0066]

-0.4173*[0.0066]

Log GDP

1.0830 [0.2884]

-

0.8776 [0.3879]

-1.2994 [ 0.2048]

-0.0958 [0.2048]

Log IQ

1.3760 [0.1801]

-0.7534 [0.4577]

-

-1.7800*** [0.0863]

Log EC

7.3522* [0.0000]

2.2452** [0.0331]

-1.4615 [0.1554]

-

-0.5053* [0.0042]

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5. Conclusions and policy implications

-0.2087*** [0.0863]

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Note: *,&*** shows the decision criteria at 1% & 10% level of significance

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Variables

This study explores the structural changes that link economic growth and

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institutional quality as they relate to CO2 emission and energy consumption. The study applies ARDL modeling and Granger causality for long and short-run and

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causality analysis respectively. The result summarizes that institutional quality has a positive and significant impact on CO2 emissions. Also, finding shows economic

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growth reduce CO2 emissions over time, which validated the EKC existence for CO2 emissions. The study offers new evidence for Pakistan through the institution’s role in the environment. Institutional quality helps to establish the inverted U-shaped relationship between income and pollution in Pakistan. Additionally, energy consumption contributes to CO2 emissions as we were accepted. Unidirectional causality is running from institutional quality toward CO2 emissions in short run, but in the long run institutional quality and CO2 emissions granger cause each other.

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The results have several implications for decision-makers. The magnitude of income is reducing overtime suggesting the positive role of income in pollution reduction. We urge that the government should continue current growth policies to enjoy the fruit of sustainable development. In term of institutional quality policy analyst should take serious steps to bring about substantial institutional reforms that might help the country’s environmental quality. Most important, government should increase public awareness, people will demand more for clean environment that

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might increase institutional performance. Strengthening the policy, legal, and

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institutional framework can mitigate climate change by reducing CO2 emissions.

Institutional performance could be improved by minimizing corruption, strengthen

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rule of law, and increase government efficiency. High institutional quality

implements environmental regulations more effectively. Better institutions provide

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easy access to information and political freedom, and that develop desire among

environmental legislation.

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people toward clean environment and boosts public awareness and support for

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Acknowledgment The authors would like to sincerely appreciate the Researchers Supporting Project number(RSP-2019/58), King Saud University, Riyadh, Saudi Arabia.

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