An analysis of the association among
carbon dioxide emissions, energy
consumption and economic performance:
an econometric model
Khurram Ejaz Chandia ,Ifra Gul ,Saira Aziz ,Binesh
Sarwar ,Salman Zulfiqar
Published online: 03 May 2018
ABSTRACT: This work is intended to present an investigation
into the macroeconomic reasons for carbon dioxide emissions in
Pakistan for the period 1971–2016. This study adds to the current
empirical literature on the association among carbon dioxide emissions,
energy consumption, economic growth and the level of investment in
Pakistan. The data has been checked for its stationary by applying the
augmented Dickey-Fuller (ADF) unit root test, and then a cointegration test helped in the estimation of long-run equilibrium
association between environmental variables. The outcomes of the cointegration test suggest the presence of a long-run equilibrium
connection between series. The results of Vector Error Correction
Model (VECM) demonstrate that all variables transform to sustain
long-run equilibrium. The outcomes of a Granger causality test showed
that causality runs from economic performance and population toward
CO2 emissions, while bi-directional causality is found between carbon
dioxide emissions and energy usage, and carbon dioxide emissions and
trade openness. The results of cumulative sum of recursive residuals
(CUSUM) and cumulative sum of recursive residuals squares
(CUSUMSQ) show the stability of the functional relationship
established in the present work, which implies that this model is an
adequate and useful tool for strategy formulation. So, an upsurge or a
decrease in macroeconomic series will cause and has caused variations
in carbon dioxide emissions in the long run in Pakistan.
KEYWORDS:


Carbon dioxide emission
energy consumption

economic performance
Introduction : The emission of carbon dioxide from the burning
of fossil fuels has increased by 3 times since 1960. The world faces
greater concerns as carbon dioxide emission has played a significant
role in global warming. Carbon dioxide emissions – economic
performance models could play a vital role in explaining part of
population growth patterns and economic performance patterns. The
fast growth of the economy and high increase of population increase
the degradation of the environment.
The empirical testing of environmental and macroeconomic variables is
necessary; because every economy heavily depends on energy
consumption, the environmental policies of energy conservation have
the potential to influence economic performance. Knowledge of
directional causality among macroeconomic variables and
environmental variables can elucidate policy implications and
recommendations. The most critical problems faced by the world
economy are the formulation and implementation of restrictions on
emissions of carbon dioxide for the welfare of society and the
betterment of the environment.
Various governments of the world signed the Kyoto Protocol in 1997,
which was related to the emission of environmental pollutants and
gases. The protocol emphasizes that industrial countries of the world
must reduce their emissions to 5% less than the emission level in 1990,
during the period from 2008 to 2012. Trade can intensify pollution and
environmental degradation in developing economies due to the
production of emission-intensive products. The climate dynamics of
Asian countries are very delicate due to their weather conditions and
deployment of heavy industry.
Developing countries, such as Pakistan, do not have access to
infrastructure facilities and financial resources to adapt to climatic
variations. Pakistan fulfills more than 50% of its energy needs using oil
and gas, which contribute toward the emission of carbon dioxide in the
environment. Environmental problems become more sensitive in the
case of Pakistan, which is in the phase of development and is using a
considerable amount of energy to run the cycle of its economy. The
government of Pakistan cannot afford to think about controlling
climatic variations at the cost of its economic performance. This study
will analyze this sensitive and critical problem of Pakistan.
The Pakistani economy has observed remarkable as well as depressing
growth rates, and a high as well as a low level of economic
development between 1971 and 2016. So, this kind of study is of great
significance for Pakistan to plan the path toward a decrease in
CO2 emissions intensity. This work attempts to observe the
contributory associations among carbon emissions, economic
performance, and energy usage in Pakistan, employing annual time
series data, and including trade openness, level of investment and
population as possible determinants of energy emissions.
The contribution of the current work is that it considers several
potential advantages as compared to the earlier literature. The
research contributes to the body of empirical literature by attempting
to observe the contributory connection among economic performance,
energy usage and carbon dioxide emissions in Pakistan with yearly
time series data, and including the level of investment, trade openness
and population growth as possible determinants of carbon dioxide
emissions. To best of the authors’ knowledge, this is the first study to
incorporate the level of investment, trade openness and population
growth as possible determinants of carbon dioxide emissions in
general, and particularly for the case of Pakistan. Moreover, another
contribution is that this study employed the most recent available data
set (i.e. from 1971 to 2016). To best of the authors’ knowledge, this is
the first work to use this latest available data set. Another input of this
work is toward strategy making. The outcomes of this research work
could support legislators in developing a complete energy policy on the
degradation of the environment in Pakistan.
This research is also an effort to fulfill the gap identified in [Citation1
That study indicated in its conclusion that other researchers should use
their methodology and results to obtain better insight into the
environment and finance-economic connection in other developing
countries. As Pakistan is the developing country of the world, so this
study fulfills the gap indicated in [Citation1]. To make this work more
valuable, the following variables were added to the study: level of
investment, trade openness and population, with an opinion on
classifying the explanatory variable(s) accountable for the Granger
positioning of CO2 emission and energy usage, thus revealing the cause
of Pakistan's environmental degradation. The fundamental intention is
to utilize the results of this study as strategic guidance for other South
Asian countries, who are engaged in their own struggle to meet their
economic growth prospects and minimize environmental degradation.
The remainder of the current article is as follows. The next section
presents a survey of previous work on the topic under review. The
third section elaborates the methodology employed in this paper. The
results are analyzed and debated in the fourth section. The fifth
section concludes the study.
Literature review
Many studies have been carried out to determine the relationship
between the carbon emission, energy consumption and economic
performance of a country. Researchers [Citation2–5,Citation101] have
debated whether economic performance and energy consumption lead
to higher carbon emission, which further leads to environmental
pollution. They have suggested that environmental pollution and
economic performance both increase with increased usage of energy.
There is a dynamic relationship among economic growth, energy
consumption and carbon emission in the USA [Citation6]. Increase in
economic growth leads to an increase in energy consumption, which
increases CO2 emission. The work of [Citation7,Citation8] studied
France and Malaysia's economic performance and concluded that in
these countries, economic growth leads to energy consumption and
CO2 emission. The study of [Citation9] found that there is a direct
relationship between Chinese economic performance and energy
consumption and CO2 emission. The paper [Citation10] investigated 11
countries to find their relationship among economic performance,
energy consumption, and carbon emission.
In the context of the Turkish economy, the impact of financial
development, energy usage, trade openness and economic growth on
carbon dioxide emissions has been assessed in [Citation1]. The study
employed data from 1960 to 2007 for its analysis. The findings of the
study revealed the verification of the environmental Kuznets curve
(EKC) hypothesis in Turkey. The results of the bound F-test approach
for co integration gave proof of a long-run association among the
variables employed in the study. The study concluded that any
variations in the long-run equilibrium among variables are adjusted for
each interval to reach the level of the long-run equilibrium.
Countries like Bangladesh and Indonesia have other sources of
CO2 emissions besides economic activities, whereas other countries
are facing CO2 emissions due to economic performance and increase in
energy consumption. Examining per-capita carbon emission, electricity
consumption per capita, Gross Domestic Product (GDP) per capita and
fiscal expansion through in-depth research, a study [Citation11]
concluded that electricity consumption could increase carbon emission
which would in turn increase GDP per capita and economic
development. According to [Citation12], Saudi Arabia tried to reduce
its CO2 emission, but this had adverse effects on the economic growth
of the country. Energy consumption, energy price and carbon emission
are the driving forces of economic growth. Industrial civilization is
using fossil energy for economic activity. Greenhouse gas emissions are
increasing day by day, which shows a constant increase in human
activities. Coal consumption for energy is around 69.7% of the total
energy. The paper [Citation13] concluded that CO2 emission has a
positive relationship with economic growth and increase in GDP in the
case of China.
Another study [Citation14] looked into the association among
CO2 emissions, economic growth and use of energy over the period
from 1971 to 2007 for six Association of South-East Asian Nations
(ASEAN) (Indonesia, Malaysia, Philipines, Singapore, Thailand and
Brunei Darussalam). That study employed panel vector autoregression
(VAR) methodology to discover the connection between series. The
outcomes of research stated that energy usage has a statistically
meaningful role in the GDP growth equation. This means that intensive
energy usage leads to better economic activity. The results of the VAR
suggested that the growth hypothesis holds for ASEAN states. The
research paper concluded that energy plays a limiting role toward
economic growth and energy supply shocks could negatively affect
economic growth.
A study [Citation15] observed the connection between economic
performance, CO2 emissions, and energy usage in Italy. The study
employed different time series econometric methods such as unit root
tests for stationarity, cointegration and VAR for the long-run
association, and a Toda–Yamamoto (TY) causality test for analysis of
causality. The results indicated that CO2 emissions and real GDP play a
role in driving energy consumption. The research showed bi-directional
causation among CO2 emissions and economic performance. The study
concluded that Italy has to reduce CO2 emissions via reducing energy
use.
The causal association among real GDP, financial development,
urbanization, CO2 emissions, trade openness and energy usage is the
focus of a study in the context of Tunisia [Citation16]. The study covers
the period from 1971 to 2012, and used the Auto-Regressive
Distributive Lag (ARDL) test to estimate long-run association and an
error correction method for analyzing short-run dynamics of the
relationship among variables. This research showed that Tunisia's
financial progress is taking place at the cost of environmental pollution.
The study could not prove the validity of the EKC hypothesis in the case
of Tunisia.
Another paper [Citation17] analyzed the connections among the
growth of the economy, emissions of CO2 and energy usage from 1971
to 2006 in case of Israel. The study employed time series econometric
techniques for analysis of data. Various tests of a unit root were
applied to check stationarity of data. The study used Johansen and
Juselius cointegration and the TY Granger non-causality test for
underlying association among variables. The results indicated the
existence of long-run association among series. The outcomes of the
causality test showed that GDP initiated energy usage and
CO2 emissions. The study concluded that waste and energy usage
would have little or no influence on the performance of the economy.
The influences of trade, financial development and growth of the
economy on electric power consumption were observed in [Citation18]
for Japan. That paper employed the Cobb-Douglas production function
for time series data from 1970 to 2012. The findings revealed the
presence of a long-run relationship among the series. The research
concluded that capital lowers energy usage in Japan, in both the short
run and the long run.
The work of [Citation48] tried to investigate the effects of Foreign
Direct Investment (FDI), economic growth, and energy consumption on
carbon dioxide emissions. In this regard, five economically developed
ASEAN countries, namely Indonesia, Malaysia, the Philippines,
Singapore and Thailand, were selected for the period from 1981 to
2011. The primary reason for choosing these ASEAN countries was the
observation of high energy demand in the last few years in these
countries. The study employed a panel data framework to find out the
determinants of carbon dioxide emission. Further, quantile regression
techniques were used to investigate the factors throughout the
conditional distribution. The results showed that the impacts of
different factors on CO2 emissions are divergent. The effect of FDI on
CO2 emission is positive, but this is negative in the low- or middleemission countries. Energy consumption also has a significant positive
effect with high quantiles, while economic growth was found to have
an adverse effect in the uppermost quantile.
The study [Citation49] has examined the EKC hypothesis in Brazil, India,
China and Indonesia. This bell-shaped EKC curve explains that
economic growth brings prosperity to the environment in the long run.
It states that environmental well-being rises first with high economic
growth and then falls at the last stage of a given income level.
Population growth was taken as one of the significant factors in the
model as it is considered to increase greenhouse gas emissions and
urbanization. The results showed that the relationship between
economic growth and the environment is vital in all of the selected
countries, and energy consumption and population growth act as a
mediating variable and play an important role in carbon dioxide
emissions. In China, GDP per capita and energy consumption per capita
were found to have a significant positive relationship with
CO2 emissions in both the long and short run. For Brazil, a significant
negative relationship was found of population growth with
CO2 emissions, while GDP per capita and energy consumption were
found to have a significant positive relationship in the long run. Overall,
the study supports the fact that energy consumption and economic
growth have a negative effect the environment. Therefore, these
countries, as emerging economies, should focus efforts on energy
savings and should fulfill energy demands through non-fossil fuels.
An investigation of the association among energy consumption,
economic growth and carbon dioxide emissions for the years 1992 to
2013 in South Caucasus and Turkey was carried out in [Citation19]. The
study employed three variables (i.e. GDP per capita, CO2 emissions per
capita and energy usage). The principal purpose of this work was to
make a comparison of aggregate income to energy factors in countries
at different levels of socio-economic integration. The study employed
panel-data VAR methodology for analysis. The VAR estimates show
that real GDP has been influenced by its previous trends as well as by
CO2 with a negative sign, indicating that emissions have a determining
effect on the economic activity of the region.
Another study, conducted by [Citation50]), investigated the effects of
carbon dioxide emissions in 12 sub-Sahara African countries from 1971
to 2010. This study used the bounds test to check the cointegration as
well as causality among variables as developed by previous researchers.
It was found that in the long run, there is a direct relationship of
energy consumption and economic growth with CO2 emissions. The bidirectional causal relationship is found between economic growth and
carbon dioxide emissions for the Democratic Republic of Cango, Gabon
and Nigeria. This result supports the literature and the hypothesis that
the levels of economic activity and CO2 emissions affect each other.
An analysis of economic performance, CO2 emissions and energy
consumption in Middle East countries was carried out in [Citation20].
The study authors chose 10 different Middle East countries over the
period 1971 to 2010 for analysis. The study employed panel-data VAR
methodology. This technique incorporates traditional VAR
methodology. It takes all variables in the system as endogenous, with a
panel-data approach that allows for individual unobserved
heterogeneity. In the VAR estimations of Gulf Cooperation Council
(GCC) countries, the response of economic growth is negative toward
CO2 emissions. Energy usage drives CO2 emissions along with lagged
CO2 emissions. The study notes that energy usage and CO2 emissions
have no impact on the economic growth of non-GCC nations.
The study [Citation51] analyzed the spatiotemporal and econometric
characteristics of CO2 emissions in China for the period 1995–2012. As
China is now becoming the world's largest developing country, the
results of this study indicate that its per capita and total carbon dioxide
emissions have notably increased in the past few years. In this regard,
panel data was employed to measure the causal relationship among
economic growth, energy consumption, and CO2 emissions. The overall
results show a positive cointegration among all variables in the long
run. Due to the rapid economic growth of China, it depends on carbon
energy consumption, which is the primary cause of increasing carbon
dioxide emissions in China. So, the findings indicate an urgent need to
implement long-term energy and economic development policies in
China to address the issues caused by the emission of CO2 and other
greenhouse gases.
The study [Citation21] assessed the association among energy use,
GDP and CO2 emissions for six GCC states from 1960 to 2013. Several
econometric time series tools were used for analysis. The paper
revealed that the ‘energy-led growth hypothesis’ holds for Kuwait,
Oman and Qatar. The ‘conservation hypothesis’ holds for Bahrain,
‘feedback mechanism’ for Saudi Arabia and ‘neutrality hypothesis’ for
the United Arab Emirates. According to [Citation22], the absence of
causality among GDP and energy usage is referred to as the ‘neutrality
hypothesis’. Uni-directional causality running from GDP toward energy
is called the ‘conservation hypothesis’. Uni-directional causality
running from energy to economic performance is called the ‘growth
hypothesis’. The bi-directional causality between economic
performance and energy usage is known as a ‘feedback mechanism’.
The influence of trade openness, economic performance and financial
progress on energy was addressed for the South African economy
[Citation23]. The study covers the period from 1970 to 2011. The study
employed the Ng-Perron unit root test and the Zivot-Andrew unit root
test for a structural break. The research examined the long-run
relationship through the ARDL bound test approach to cointegration,.
and causality was investigated through vector error correction model
(VECM) Granger causality analysis. The study concluded that financial
development plays a stimulating role in energy demands of South
Africa. It is argued in the study that energy should be acknowledged as
a partially responsible element for achieving strategic economic
development and sustainable growth of the economy.
Literature focusing on Pakistan
Several studies including empirical research have focused on the
economy to study the environmental degradation and economic
performance of Pakistan. This segment of the paper points out the
gaps in these studies and outlines the contribution of the current study
toward those issues on which previous studies were silent. A detailed
analysis is given below.
The existing literature outlines three different approaches to studying
the association between macroeconomic variables and environmental
variables of the country. The first approach emphasizes environmental
degradation variables and economic performance linkage, which is
commonly acknowledged as examining the soundness of the EKC
hypothesis. The second approach concentrates on the association
between economic performance and usage of energy in the country.
The third approach is a combination of the above two methods, which
observes the connection of economic performance, environmental
variables, and energy usage together.
The current study employs this third approach, examining economic
variables and environmental variables along with energy consumption
simultaneously. Most of the previous reviews on Pakistan have used
tri-variate models, focusing on quadratic relationships, that yield
inconsistent findings. As indicated in Table 1, the EKC hypothesis is
rejected or partially verified in all cases, so now it has become
necessary to study a multivariate model by including the level of
investment, trade openness and population growth simultaneously in a
linear relationship. None of the previous studies included the level of
investment in their model. So, the current research focuses on
including the level of investment as a potential determinant of
emissions, along with incorporating trade openness and population
growth in the model.
Methodology
In contrast with a couple of past studies, the present study looked at a
multivariate system that is used to identify the actual and real
relationship among six variables, instead of analyzing bi-variate or trivariate association. The present paper uses environmental and
macroeconomic variables in the settings of Pakistan's economy. The
study examines the connections among environmental and
macroeconomic variables using various econometric time series tools.
This study postulates a linear relationship between different variables.
The basic structure of the model is an extended version of another
study [Citation29] that has been adapted for the case of Pakistan. The
model has the following formula:


The variable for economic performance is the GDP of Pakistan,
measured per capita. The variable investment is measured as the ratio
of the level of investment to GDP of Pakistan. The variable energy
consumption is measured as energy use in kg of oil equivalent per
capita in the form of a natural log. The variable trade openness is
measured as the ratio of the sum of exports and imports as a
percentage of GDP. The variable CO2 emission is measured as total
greenhouse gas emissions (kt of CO2 equivalent) per capita. The
variable of the population is measured as the growth rate of the
population in Pakistan. The following paragraphs elaborate the reasons
for inclusion of the variables mentioned above in the empirical model.
Energy consumption and economic performance are widely considered
primary sources of national carbon dioxide emissions, greenhouse
gases and ultimate degradation of the environment. Government
institutions and other bodies across the globe pay attention to such
concerns when analyzing the dimensions of climatic variations, and
other ecological disruptions. According to a report of the Royal Society
[Citation52], increasing patterns of energy consumption are exhausting
the resources of the planet and hence producing various
environmental issues including carbon dioxide emissions to the
atmosphere. So, this is the original notion of including the variables (i.e.
carbon dioxide emissions, energy consumption and economic
performance) in an empirical model of the current study [Citat
The belief exists in different policy circles that the level of investment
(public as well as private) can increase the level of productivity in a
country,which can in turn lead to better economic performance. Public
and private investment not only provides capital financing for the
economy but also develops positive externalities through technology
adoption. This proposition generates a debate about its costs and
benefits. Capital inflows undoubtedly boost economic activity and
productivity gains. Private-sector investment is encouraged because of
its prominent role in job creation and economic growth. However,
these concerns could undercut environmental regulations to attract
investment in general, and mainly private investment. The bottom line
is that a country becomes ‘pollution heaven’ as the private sector tries
its best to cut environmental costs. Even the government/public sector
is least interested in environmental concerns in countries like Pakistan,
where the government itself initiated the project of coal power plants
for electricity generation, which will undoubtedly lead to
environmental degradation of the associated area in future. This
notion leads to the inclusion of the variable ‘level of investment’ in the
current study [
In recent times, various international bodies, especially the World
Trade Organization (WTO), International Center for Trade and
Development International Trade Center, have emphasized a strong
association between trade and environment. They aim to propose a
program on natural resources, trade and environment as they are
focusing on the need for an adequate trade plan that could lead
toward sustainable development and an improved lifestyle in different
nations using natural resources. Pakistan became a member of the
WTO on January 1, 1995, but Pakistani economists and policymakers
have not developed a framework for examining the effects of trade
opening on pollution and environment and vice versa. So, this study
has included the variable of trade openness in its empirical model to
capture the effect mentioned above. The fundamental proposition is
that carbon dioxide emissions could be affected due to an increase in
economic activity as a result of trade. The boost in trade will generate
economic activities as well as energy usage. Assuming other things
remain constant, economic activities and energy usage could lead to
higher carbon dioxide emissions
The variable of population is also part of the empirical model of the
current study. Everyone has energy demands for shelter, water, food
and so forth. Assuming all else is equal, if the population increases,
energy demands also increase. The literature suggests two phenomena:
first, increasing population in any country could generate a demand for
energy for different purposes such as power, transportation and
industry, thus increasing carbon dioxide emissions. The second
phenomenon elaborates that population growth could lead to
deforestation that could contribute to carbon dioxide emissions [
This study used annual data from 1971 to 2016. The data for these
variables were collected from World Development Indicators (2016)
[53] and Handbook of Statistics on the Economy of Pakistan
[54], which is published by the State Bank of Pakistan. A graphical
representation of variables included in the model can be found
in Appendix I. It is worth mentioning here that energy consumption
patterns faced some decline in the near past. The energy mix of
Pakistan is comprised of commercial fuels as well as traditional fuels.
According to the figures for 2012, conventional fuel had a share of
34.9%, natural gas 31.7%, and oil 24.8%. The rest of the fuel was
provided by coal, hydropower and nuclear power. Electricity
generation from oil has been increasing since 1996; hydro sources
remained the same. After 2002, the economy began growing as percapita income increased by 8.9% between 2004 and 2008. Growth in
energy usage was significant in the commercial sector (11.3%) and the
industrial sector (12.5%). After 2008, energy usage/consumption was
unable to expand due to constraints. Demand for energy expanded but
government as well as the private sector failed to address and
accommodate the increasing demand. The gap between energy supply
and demand increased from 3000 MW to 7000 MW in 2011. Supply
was constant and demand continued to grow. Due to these facts,
energy consumption failed to follow its increasing patterns of the past.
This was the result of the prevailing energy crisis that reached its peak
in 2014 [
The first step in time-series analysis is to test for the stationarity of
variables. So, the initial phase is to verify the presence of unit root in
data series, as time-series variables are often non-stationary. The
augmented Dickey-Fuller (ADF), Dickey-Fuller Generalized Least
Squares (DF-GLS), Phillip and Parron (PP) and Kwiatkowski, Phillips,
Schmidt, and Shin (KPSS) tests were employed to deduce nonstationarity of macroeconomic series. Consider the autoregressive AR
(1) model:is a white noise process. If the series is taken as stationary,
and if , the series is known to have a unit root and is non-stationary.
The null hypothesis (Ho) and the alternative hypothesis (HA) for the
presence of unit root in environmental and macroeconomic series are
Ho; µ = 1 and HA; µ < 1, respectively.
If macroeconomic variables are found to be integrated at the same
order, then variables could be considered cointegrated. The
cointegration method is employed for estimation of long-run
equilibrium association among series. To observe the long-run
connection between environmental and macroeconomic variables (i.e.
GDP, trade openness, population growth, carbon dioxide emissions,
and energy consumption), Johansen cointegration is estimated
for EquationEquation (1). However, the present study followed the
method elaborated in [Citation34], to correct for small-sample bias and
allow more appropriate statistical interferences to be made with small
samples. However, due to the small size of the sample considered in
the current paper, it is likely that the Johansen test statistics might be
prejudiced [Citation35]. Consequently, the study followed the method
of Reinsel and Ahn [Citation34], who recommended multiplying the
Johansen trace statistics with the scale factor N/(N–pk). N is the
number of observations, k is the number of variables and p is the lag
parameter in the estimated VAR system. Such a practice corrects for
small-sample prejudice and permits more proper statistical intrusions
to be made with fewer samples.
Autocorrelation is most likely to occur in a time-series framework. The
equation states that error terms and should be independently
distributed. For checking autocorrelation, the ordinary least squares
(OLS) regression equation is initially estimated, and then BreuschGodfrey Serial Correlation (LM) test is applied to check for an
occurrence of serial correlation.
It is important to note that the cointegration technique can only
explain the long-term association among the series included in the
empirical model, thus overlooking the short-term features of the
changing aspects contained in the data. EquationEquation (1) is
additionally assessed to test short-run changing issues of
environmental and macroeconomic series. The study designs a VECM
for the series. The VECMs include lagged values of fluctuations in all
series in EquationEquation (1) in addition to lagged values of estimated
residuals. Afterward, pairwise Granger causality test is employed to
observe the direction of causality between series. The technique used
to examine the association is the Granger causality test suggested in
[Citation36], which considers the cause and effect association between
two or more variables. Cumulative sum of recursive residuals (CUSUM)
test is also applied to observe the stability of coefficients.
Analysis and discussion of results
Before moving to the first step of testing for unit root test, it is
necessary to have a brief look at descriptive statistics. As a preliminary
analysis, Table 2 summarizes descriptive statistics. The mean value of
all variables is positive. All the variables except trade openness have a
negative value of skewness, indicating that the distribution is skewed
to the left, with more observations on the right. Tables 2–8 provides
the summary statistics for the sample. The mean value of all variables
is positive. It is specified that for every variable, the standard deviation
value is near to the pseudo standard deviation; the inter-quartile range
(IQR) shows the absence of outliers in the observed sample.
The first step in time-series analysis is to test for the stationarity of
variables. So, the initial phase is to verify the presence of unit root in
data series, as time-series variables are often non-stationary. The
augmented Dickey-Fuller (ADF), Dickey-Fuller Generalized Least
Squares (DF-GLS), Phillip and Parron (PP) and Kwiatkowski, Phillips,
Schmidt, and Shin (KPSS) tests were employed to deduce nonstationarity of macroeconomic series. Consider the autoregressive AR
modelis a white noise process. If the series is taken as stationary, and
if , the series is known to have a unit root and is non-stationary. The
null hypothesis (Ho) and the alternative hypothesis (HA) for the
presence of unit root in environmental and macroeconomic series are
Ho; µ = 1 and HA; µ < 1, respectively.
If macroeconomic variables are found to be integrated at the same
order, then variables could be considered cointegrated. The
cointegration method is employed for estimation of long-run
equilibrium association among series. To observe the long-run
connection between environmental and macroeconomic variables (i.e.
GDP, trade openness, population growth, carbon dioxide emissions,
and energy consumption), Johansen cointegration is estimated
for EquationEquation (1). However, the present study followed the
method elaborated in [Citation34], to correct for small-sample bias and
allow more appropriate statistical interferences to be made with small
samples. However, due to the small size of the sample considered in
the current paper, it is likely that the Johansen test statistics might be
prejudiced [Citation35]. Consequently, the study followed the method
of Reinsel and Ahn [Citation34], who recommended multiplying the
Johansen trace statistics with the scale factor N/(N–pk). N is the
number of observations, k is the number of variables and p is the lag
parameter in the estimated VAR system. Such a practice corrects for
small-sample prejudice and permits more proper statistical intrusions
to be made with fewer samples.
Autocorrelation is most likely to occur in a time-series framework. It is
defined in the form of an equation as:The equation states that error
terms and should be independently distributed. For checking
autocorrelation, the ordinary least squares (OLS) regression equation is
initially estimated, and then Breusch-Godfrey Serial Correlation (LM)
test is applied to check for an occurrence of serial correlation.
It is important to note that the co integration technique can only
explain the long-term association among the series included in the
empirical model, thus overlooking the short-term features of the
changing aspects contained in the data. EquationEquation (1) is
additionally assessed to test short-run changing issues of
environmental and macroeconomic series. The study designs a VECM
for the series. The VECMs include lagged values of fluctuations in all
series in EquationEquation (1) in addition to lagged values of estimated
residuals. Afterward, pairwise Granger causality test is employed to
observe the direction of causality between series. The technique used
to examine the association is the Granger causality test suggested in
[Citation36], which considers the cause and effect association between
two or more variables. Cumulative sum of recursive residuals (CUSUM)
test is also applied to observe the stability of coefficients.