An Historical Review of International Trade and CO2 Emissions in Pakistan: A Quantitative

Abstract

Environmental protection has become an international issue, reflecting widespread concern about environmental degradation. Researchers have emphasized the importance of the environment in trade policy debates. The focus of this study is to explore the relationship that is present between trade openness and the quality of environment in Pakistan. This research investigates the famous nexus between trade and the environment. The study uses data from 1990-2021 for analysis. The emissions of CO2 (Metric tons per capita) serves as the proxy of Environmental quality as the dependent variable. Empirical analysis is done by applying Autoregressive Distributed Lag (ARDL) time series econometric technique. Trade openness is the main independent variable, whereas population, foreign direct investment, and non-renewable energy are used as control factors. The results obtained from co-integration test prove a long term relationship between CO2 emissions and trade openness. A study indicated that increased commercial openness leads to an increase in CO2 emissions, which promotes environmental degradation in Pakistan. It is proposed that trade policies take environmental protection into account.

Full text

Perennial Journal of History (PJH)
Vol. V No. I (January-June 2024) PP 79-104
ISSN: 2707-6709 (print) ISSN: 2788-693X (online)
https://doi.org/10.52700/pjh.v5i1.183
© 2024 The Authors, Published by The Women University Multan. This is an
Open Access Article under the Creative Common Attribution Non-Commercial
4.0
Date of Acceptance: 23 June 2024
Available Online: 27 June 2024
An Historical Review of International Trade and
CO2 Emissions in Pakistan: A Quantitative
Analysis
Sajida Timsal
Mphil Scholar
The Women University Multan
Sajidatimsal@gmail.com
Ayesha Ashraf
Assistant Professor
Department of Economics
TheWomen University Multan
ayeshaashrafa@gmail.com
Khazeeb Zahra
BS Scholor
Department of Economics
The Women University Multan
Khazeebzahra78@gmail.com
Salma Mouneer
Lecturer
Department of Economics
The Women University Multan
salmamouneer@wum.edu.pk (Corresponding Author)
Abstract
Environmental protection has become an international issue,
reflecting widespread concern about environmental degradation.
Researchers have emphasized the importance of the environment in
trade policy debates. The focus of this study is to explore the
relationship that is present between trade openness and the quality
of environment in Pakistan. This research investigates the famous
nexus between trade and the environment. The study uses data from
1990-2021 for analysis. The emissions of CO2 (Metric tons per
capita) serves as the proxy of Environmental quality as the
dependent variable. Empirical analysis is done by applying
Autoregressive Distributed Lag (ARDL) time series econometric
technique. Trade openness is the main independent variable,

Perennial Journal of History, Vol V. No.1, 2024
80
whereas population, foreign direct investment, and non-renewable
energy are used as control factors. The results obtained from co-
integration test prove a long term relationship between CO2
emissions and trade openness. A study indicated that increased
commercial openness leads to an increase in CO2 emissions, which
promotes environmental degradation in Pakistan. It is proposed that
trade policies take environmental protection into account.
Keywords: Trade Openness, Foreign Direct Investment,
Degradation, Non-Renewable Energy
Introduction:
In recent years, environmental deprivation has rapidly appeared as
a major distress for the whole world this is reflected by the fact the
protection of the environment has become a priority worldwide in
recent times. When we look at the problem of environmental
damage in terms of trade policy we can’t help but question how the
environment is affected by trade openness. Trade does provide a lot
of benefits to countries because as a result of trade countries
experience increases in investment, job opportunities, and income
(Zahonogo 2017). However, not all countries only enjoy the benefits
of trade openness, especially the developing countries. Developing
countries have weak infrastructure, no strict regulatory framework,
lack of awareness, etc. All these things harm the environment in
these countries to a huge extent thus leading to several
environmental problems (Malefane and Odhiambo 2018). That is
why in recent years many empirical studies, Shabaz et al., 2013;
Antweiler et al., 2001; Cole and Elliot, 2003; Frankel and Romer,
1999; and Boulatoff and Jenkins, 2010 have inquired about the
direction and nature of the relation present between the quality of
environment and trade openness as both these things are very critical
to every nation these days. The interesting part is, that the results
and evidence gained from these studies show mixed results,
according to some studies trade openness leaves good impact on the
environment while according to other studies trade openness causes
harm to the environmental quality. Several studies show conflicting
results confirming that trade openness acts like a two-edged source
that can either reduce pollution or increase pollution. The difference
is caused and determined by the efficient and right use of resources

Sajida Timsal, Ayesha Ashraf, Khazeeb Zahra & Salma Mouneer
81
resulting from free or open trade (Inglesi-Lotz 2018; Copeland and
Taylor 1995; and Mapapu and Phiri 2018). It is evident from theory
and empirical studies too that trade openness leads to more growth
in a country. But that increase in growth can lead to environmental
degradation. The countries that experience this issue must impose
stringent environmental protection policies to combat the adverse
impacts of growth on the environment. The environmental worth
can also be significantly improved by opting for environment-
friendly production processes.
Thus, we can discuss the problem statement of the current study
following this discussion. The question at the moment is what kind
of effect trade openness creates for the environment. Is it harmful to
the environmental quality or beneficial? We can be more specific
and state the problem this way: do the emissions of air pollutants
increase with the rise in trade openness or fall with a rise in trade
openness? This is the problem that the current study strives to solve
and answer with the help of empirical analysis. We are using the
CO2 emissions because in this study we use a proxy for
environmental quality or environmental degradation. By using the
level of pollutant emissions of CO2 in the environment we examine
the quality of the environment. Thus if the emissions are high the
quality of the environment deteriorates and if there are fewer
emissions, the excellence of the environment is improving. We find
the answer to this problem statement in the scenario of Pakistan. The
study utilizes time series data for Pakistan to find empirical evidence
regarding this problem statement. The study's new findings will
contribute to the ongoing discussion and understanding of the
environmental impact of trade liberalisation.
Pakistan has been making significant efforts in improving,
increasing, and promoting international trade. Looking at the trade
openness data of Pakistan for past few years, it is clear that the
percentage of trade openness, though experiencing some natural ups
and downs, is still increasing, slowly but surely. The trade openness
percentage in 2017 was 25.47, in 2019 it was 28.91, and in 2022 it
was recorded at 33.05 percent according to the data from WDI. Even
though Pakistan is increasing its trade it is still not as open in terms
of trade as compared to other economies. Pakistan’s biggest trading

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partners include China, UAE, Afghanistan, US, and Saudi Arabia.
In general the things that Pakistan exports the most are cotton yarn,
rice, surgical instruments, textiles, leather goods, fruits, sports items,
garments, and sugar. And the things that Pakistan overall imports
generally are machinery, carpets, electric equipment, petroleum,
chemicals, gold, medical instruments, plastic products, steel and
iron.
Just like Pakistan is moving in the direction of increased trade, the
CO2 emissions in the country are also increasing at the same time.
In total greenhouse gass emissions, CO2 has the biggest share and it
harms the environment intensly as well as posing several health
complications in humans. So CO2 emissions are dangerous for not
only a person’s health but the health of the environment too. If we
look at the data of CO2 emissions in KT in Pakistan, it was pretty
low in 2013, it was 146K KT. But after that, this number kept on
increasing till 2017 where it reached a huge value of 210K KT
CO2emissions. Then in next three years this value was brought down
to some extent ranging from 198K KT, 201K KT, and 202K KT in
the years 2018, 2019, and 2020, respectively. After which this value
increased to a great extent in 2021, recording highest ever CO2
emissions in Pakistan with 220K KT in 2021, according to the
Global Atmospheric Research’s database for emissions.
So is this increase in CO2 emissions while the trade openness is also
increasing in Pakistan, a mere coincidence or the increasing trade
openness affects CO2 emissions? That’s what this study is going to
answer.
The importance and significance of this study lies in the outcomes
of this study that can deliver esteemed understandings for
policymakers and stakeholders in developing effective strategies
and policies to balance economic growth through trade openness
while mitigating the negative environmental impacts. It can
contribute in devising the sustainable development strategies that
prioritize environmental conservation alongside trade liberalization.
This study is in alliance with the very critical United Nations'
Sustainable Development Goals, particularly Goal 13: Climate
Action and Goal 15: Life on Land. Understanding the link between
environmental quality and trade openness contributes to achieving

Sajida Timsal, Ayesha Ashraf, Khazeeb Zahra & Salma Mouneer
83
these global goals by informing targeted interventions and policy
measures.
The study aims to fulfill the following objectives:
 This study investigates whether trade openness does
improve the Environmental quality of Pakistan or not.
 How does Non-Renewable Energy affect the Environment?
 How does FDI affect the Environment of Pakistan?
 Does a rise in Population cause Environmental Degradation?
What is the relationship of FDI, Population, Trade openness, and
Non-Renewable Energy with Pakistan’s Environment?
What policy suggestions can we provide to help sustain
environmental quality in Pakistan?
The research gap of this study is built on the statement that this study
directly addresses whether the Porter Hypothesis, which suggests
that trade openness creates a beneficial effect on the environment, is
valid, or if trade openness harms the environment, in case of
Pakistan which has not been done frequently and recently. The data
used is up to date and the relevant methodology is applied. Much
research on the topic regarding the effect of trade openness on the
environment has been studied until approximately 2017, but since
then, in recent years, Pakistan has seen many events that have added
to the environmental deterioration in Pakistan. The country has been
grappling with the devastating effects of floods and has also
experienced significant climate change, political instability, and
environmental changes. Furthermore, COVID-19 had a significant
impact on Pakistan. In this study, we observed the recent effects of
these factors in recent years that were not considered in other studies
done on this topic before.
This study significantly adds to the literature by providing valuable
insights regarding this pressing issue of environmental stability and
trade openness. This study examines and investigates the effect

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84
caused by trade openness on environmental quality, in this way, it
greatly contributes to a meaningful and detailed comprehension of
the association among economic activities and their impact on the
environment. This knowledge can support efforts to develop
environmentally friendly practices and policies to mitigate negative
consequences such as resource depletion, ecosystem degradation,
and pollution. The study explores the intricate connection between
trade and the environment, shedding light on the complex
interactions and trade-offs between economic development and
environmental sustainability. It helps to assess the trade-offs and
identify potential synergies between trade openness and
environmental conservation, thereby guiding decision-making
processes. The literature up to this point regarding this study has a
gap that the current study fills with substantial empirical proof and
analysis of the specific relationship between trade openness and
environmental quality. It contributes to the body of knowledge in
the field of environmental economics, trade studies, and sustainable
development, fostering further research and academic discussion.
Understanding the impact of trade openness on the environment is
relevant not only at the country level but also from a global
perspective.
The study's findings can inform international trade policies,
negotiations, and agreements to ensure that trade liberalization
promotes sustainable development and environmental stewardship
on a global scale.
Overall, the study on the effect of trade openness on environmental
quality, incorporating population, FDI, and non-renewable energy
as additional independent variables, carries significance for policy
formulation, sustainable development, environmental conservation,
academic research, and global trade governance.
The study applied several econometric techniques including bounds
test, unit root tests, diagnostic tests as well as ARDL. The main
findings reveal that first and foremost the link between environment
and trade openness in Pakistan indeed exists in the long run.
Secondly, the estimates reveal that increased openness to trade is
causing environmental degradation in Pakistan in the long run. For

Sajida Timsal, Ayesha Ashraf, Khazeeb Zahra & Salma Mouneer
85
Pakistan as a developing country, the environmental effect of trade
openness is found to be significantly negative.
Literature Review:
Huge empirical literature has found to explore the causal link
between trade openness and envoirnmental protection. This section
provides deep insight into empirical literature to understand the
theoratical underpinnings of this linkage.
Lokina et al. (2022) examined the effects on the environment’s
quality caused by international trade. The study analyzed the impact
of trade openness by using the variables inflow of FDI and trade
openness. Variable of ecological footprint was used as a proxy for
environmental quality. The study gathered data from 23 Sub-
Saharan African countries. Panel data was collected from 1990 to
2015. The study employed the Feasible Generalized Least Square
methodology. According to the results, trade openness reduced the
ecological footprint while FDI inflows increased it. Another notable
result of the study was that it revealed the link present between
ecological footprint and GDP was of inverted U shape. The findings
of this study can be used greatly in policies regarding sustainable
development and socioeconomic planning.
Nadeem Shah et al. (2022) analyzed how energy use and trade
openness affect the environment. The researchers collected data
from Pakistan. The researhers gathered time series data from 1976
to 2019 for this study. The dependent variable of the study was
carbon dioxide emissions, while the independent variables were
GDP, trade openness, and energy use. Variables’ stationarity was
figured with PP and ADF test. The study then applied the ARDL
technique to obtain long-run estimates. The estimates showed that
CO2 emissions in Pakistan are increasing due to an increase in trade
openness and energy use.
Suhrab et al. (2022) observed the top influential variables affecting
the environment in the case of Pakistan. Data from 1985-2018 was
collected. Important independent variables in the study were
urbanization, renewable energy, financial development, trade
openness, GDP.The dependent variable of the research was CO2
emissions. The empirical analysis involved the Granger causality

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test and cointegration technique. The results depicted that CO2
emissions increase with the boost in trade openness, urbanization,
and financial development. The results also confirmed that using
more renewable energy lessened the CO2 emissions in Pakistan. All
independent variables showcased a long-run relation with the
dependent variable. Unidirectional causality going from GDP to
CO2 emissions and from renewable energy to CO2 emission was also
confirmed by Granger causality results.
Gupta et al. (2022) analyzed how the ecological footprint is affected
by trade openness and physical infrastructure. The said asymmetric
impact was measured in terms of Pakistan. The researchers used
data from 1970-2019. The study applied a non-linear ARDL model
to find results. The findings showed that when the physical
infrastructure faces positive shocks and negative shocks the
ecological footprint increases and decreases symmetrically in the
long run but asymmetrically in the short run in the case of Pakistan.
However in case of short term as well as long run the ecological
footprint increase and decrease asymmetrically because of trade
openness experiencing positive and negative shocks. Urbanization
was found to positively and significantly increase the ecological
footprint of Pakistan in both the short term and long run.
Mishra et al. (2021) examined the factors affecting the environment
in India. The study utilized dependent variable of CO2 emissions.
The study aimed to find out the effect of trade openness,
manufacturing, and economic growth on CO2 emissions. The
autoregressive distributive lag (ARDL) model was employed in this
research. According to the results there existed a long-run
relationship between independent variables and CO2 emissions. The
research focused on data from 1971-2016. The results showed that
in India the increase in trade openness significantly reduced CO2
emissions. The rise in GDP and manufacturing increased CO2
emissions; both these variables had a positive significant link with
the CO2 emissions.
Memon et al. (2021) analyzed the nexus between trade openness,
technological innovation, economic growth, and ecological
footprint. The study gathered time series data from Pakistan for the
years of 1992 to 2018. The researchers wanted to analyze whether

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87
technological innovations, trade openness, and economic growth are
contributing to a sustainable environment in Pakistan or not. To
determine the link between variables being used the ARDL
methodology was used. The results revealed that presence of long
run link in the variables. According to the results, there is a positive
and significant link present in Pakistan between the ecological
footprint and the energy related public-private partnership
investment in both the short run and the long run. The estimates
revealed that increased investment in energy negatively affected
environmental sustainability in Pakistan.
Sethi et al. (2018) highlighted the dynamic nexus that exists between
Carbon Dioxide (CO2) emissions and Free Trade Agreements
(FTAs) in Pakistan. Data from the years 1980 to 2014 was used. The
key variables used in the study included CO2 emissions, energy use,
GDP, and trade openness. The study used unit root tests,
cointegration tests, and OLS for econometric analysis. The
empirical estimates showed that CO2 emissions increased with
increases in GDP because GDP had a significant as well as positive
relation with CO2 emissions. The results indicated there is present a
long-run, positive relation between the use of energy and CO2
emissions. The results also declared the existence of a negative link
present between CO2 emissions with FTAs and trade openness.
Shahzad et al. (2016) observed the nature and direction of relation
between environment and trade openness, energy use, and financial
development. The researchers focused on Pakistan and collected
yearly time series data for the years 1971 to 2011. The study used
CO2 emissions as a proxy for the dependent variable i.e.,
environment. A Bound test for cointegration and ARDL technique
was employed. The results of ARDL indicated that when trade
openness and financial development rose by a percent, it increased
carbon emissions by 0.165% and 0.247% in the long term as a result
of one percent rise in financial development and trade openness,
respectively. In the short run, these values were 0.122% and 0.087%
for trade openness and financial development, respectively.
Zaman et al. (2015) investigated the causal nexus between air
quality and trade liberalization in the case of Pakistan. For empirical
analysis, time series data was collected from 1980 to 2010. The

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Granger causality test allowed analysts to reach empirical
conclusions. The results exposed causality running from FDI to CO2
emissions. This is because FDI is spent on dirty technology which
increases pollution along with an increase in production processes.
With increased trade and FDI, production increases which also
increases the concentration of CO2 pollutants in the air. Thus as a
result the air quality worsens and the environment faces negative
effects. According to results there was found bidirectional causality
in CO2 and trade openness, as well as in FDI and technology.
Serhat (2014) studied the effect of GDP, energy use, trade openness
on CO2. The study analyzed panel data collected from 85 countries
from 1990 to 2011. Independent variables of this study were trade
openness, energy consumption, and production. The dependent
variable used was CO2 emissions. The results showed that CO2
emissions were increasing as a result of increases in energy use and
production in the selected countries. Furthermore, the results
showed trade openness also increased CO2 emissions in the short
run but after reaching a threshold level it reduced CO2 emissions.
By reviewing relevant literature we can conclude that the literature
tells us we can expect different results. Some studies show that trade
openness does not leave negative consequences for the environment
while other studies prove that trade openness leades to increase in
harmful emissions, thus negatively affecting the environment. So
the studies depict mixed results.
Sources
It is of great importance to collect data from a verified and authentic
source. This study has gathered secondary data from the period 1990
to 2021. The data is annual. In this research, the variable used as
dependent is the environment, and to show the effects or changes
caused by independent variables on dependent varable the current
research uses CO2 emissions as a proxy of environmental
degradation. The data on CO2 emissions (metric tons per capita) is

Sajida Timsal, Ayesha Ashraf, Khazeeb Zahra & Salma Mouneer
89
collected from World Development Indicators (WDI). The core
independent variable is Trade Openness in this research, which takes
the ratio of the sum of imports and exports to GDP, and the data of
Trade Openness is calculated by the author by using the data of
exports, imports, and GDP from WDI. The data of FDI (net inflows,
BOP current US$) and Population (total) is also collected from
WDI. The fourth independent variable in this study is non-
renewable energy consumption. The data on coal consumption is
used here to show non-renewable energy’s usage. The data on Coal
(quad Btu) is gathered from the Energy Information Administration
(EIA). Before moving on to the technical analysis, we have taken
the log of all the variables, dependent and independent.
Model Specification
To estimate the changes and effects caused by Trade Openness
(TOP), Nonrenewable Energy (NONRNW), Population (POP), and
Foreign Direct Investment (FDI) on the environment, we can use an
econometric model such as a multiple regression equation. The
equation could be formulated as follows:
lnCO2 = β0 + β1lnTOP + β2lnNONRNW + β3lnPOP + β4lnFDI + ε
In this equation:
- CO2 represents the environmental outcome or indicator that we are
interested in measuring.
- TOP refers to the level of Trade Openness; which depicts the level
of a country’s engagement in the international trade. It can be
measured using indicators such as the ratio of exports plus imports
to GDP.
- NONRNW represents the usage of Non-Renewable Energy
sources, like natural gas, fossil fuels, oil, and coal. It can be
measured by energy consumption data for these sources.
- POP denotes the Population size of the country or region under
consideration.

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- FDI represents the inflow of Foreign Direct Investment, which
shows the investment made by foreign entities in the host or
receiving country's businesses and industries.
- Ln represents that we have taken a log of the variables.
- β0 is intercept, which captures the baseline level of the
environmental outcome when all the independent variables are zero.
- β1, β2, β3, and β4 are the coefficients that represent the estimated
effect or impact of each independent variable (TOP, NONRNW,
POP, and FDI) on the environment. These coefficients reveal what
is the direction and extent of the relationships.
- ε is the error term, the unobserved factors and random variations
are shown by this that affect the environmental outcome but are not
accounted for in the model.
By estimating the values of the coefficients (β1, β2, β3, and β4)
through regression analysis, we can assess the statistical
significance and magnitude of the effects of Trade Openness, Non-
Renewable Energy, Population, and Foreign Direct Investment on
the Environment. The sign and magnitude of the coefficients reveal
the level of strength and the direction or the nature of the
relationships between these variables and the environment.
Results and Discussion:
Unit Root Test
The first step of the time series empirical analysis is to find out the
order of integration of the variables. This means first of all we have
to find out the stationarity status of all the variables in the study. To
check the stationarity status the Augmented Dickey-Fuller (ADF)
unit root test is applied. After finding the stationarity results, ARDL
is applied.
Table 1: Results of ADF Unit Root Test
Variables
Level
1st Difference

Sajida Timsal, Ayesha Ashraf, Khazeeb Zahra & Salma Mouneer
91
Intercept
Trend &
Intercept
Intercept
Trend &
Intercep
t
Resul
t
Log- CO2
-
1.83368
3
-
2.11207
9
-
4.71262
0
-
I(1)
0.3577
0.5186
0.0007
-
Log-TOP
-
3.30275
4
-
-
-
I(0)
0.0241
-
-
-
Log-FDI
-
1.72438
8
-
1.85039
7
-
4.46157
0
-
I(1)
0.4092
0.6547
0.0014
-
Log-POP
-
2.87014
9
-
-
-
I(0)
0.0627
-
-
-
Log-
NONRN
W
1.19518
4
-
1.91879
9
-
5.68929
7
-
I(1)
0.9971
0.6137
0.0001
-
Source: this is made by the researcher by using Eviews 9
The table depicts that POP and TOP (the independent variables) are
integrated at level, while NONRWN and FDI (independent
variables) CO2 emissions (dependent variable) are stationed at first
difference. Thus with the mixture of integration order of I(0) and
I(1), it is clear that the ARDL technique will be applied in this
research.

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ARDL Estimates of the Model:
To find the impact of TOP (trade openness) on CO2 (environment),
ARDL is applied. But the initial step here is to confirm whether there
is a long-run relation present among all the variables. This is found
by reaching the bound test results.
Table 2: Results for Bound Test
Statistics
Co-efficient
K
F-Stat
6.034
4
Critical Value for Bounds
significance
I0 Bound
I1 Bound
10%
2.451
3.522
5%
2.861
4.011
2.5%
3.246
4.492
1%
3.738
5.059
Source: This is made by the researcher by using Eviews 9
The above table depicts that indeed a long-term relation is present
in independent variables of current study and dependent variable
because the F statistic is greater than the upper and lower bound.
Thus, H0 is rejected and H1 is accepted. Now as a long-run
relationship is being confirmed then it is necessary to get the long-
run and short-run estimates of the data.
Long-Run Estimates
ARDL technique is applied to reach long-run estimates. The
following table shows the long-run estimates: -

Sajida Timsal, Ayesha Ashraf, Khazeeb Zahra & Salma Mouneer
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Table 3: Long-Run Estimates
Long Run Coefficients
Variable
Co-
efficient
Std.
Error
t-Stat
P-
value
LogTOP
0.04761
1
0.02329
4
2.04387
5
0.054
4
LogFDI
0.06983
4
0.01931
9
3.61474
5
0.001
7
LogPOP
0.11081
9
0.09995
3
1.10870
8
0.280
7
LogNONRN
W
0.08570
4
0.03151
3
2.71967
6
0.013
2
C
-
3.77105
1
1.77046
4
-
2.12997
9
0.045
8
Source: this is made by the researcher by using Eviews 9
The study used a model in which CO2 as a proxy of Environment
was used as the dependent variable and TOP, FDI, POP, and
NONRWN were used as independent variables. Then this model
was estimated with the help of the ARDL technique.
The table shows that the regression coefficient of TOP is 0.047611
and it has a positive sign; this means TOP has a positive relationship
with CO2 Emissions and with a one percent increase in TOP, CO2
increases by 0.047611%. In the analysis of studies, articles, journals,
etc., we also come to the same finding i.e. when there is increasing
Trade Openness in developing countries it harms the country's
environment. Transporting the commodities to long distnaces is a
most common result of trade openness, resulting in increased
emissions from trucks, ships, and airplanes. These modes of
transportation contribute to air pollution through the release of
nitrogen oxides carbon dioxide pollutants etc., and particulate
matter. Developing countries with inadequate transportation

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94
infrastructure and outdated vehicles may experience even higher
levels of emissions. There are many other ways in which
environmental degradation is occurring due to Trade Openness for
example Trade Openness often encourages industrialization and
increased production in developing countries. Industries such as
manufacturing, mining, and energy production can contribute
significantly to air pollution through emissions of pollutants like
CO2 Emissions. These same results are supported by other
studies, such as [Hussain et al (2022), Mahmood et al (2020),
Shahzad et al (2016)]. According to these results, we can conclude
that we accept the alternative hypothesis of this study, i.e., the TOP
has an impact on the CO2 Emissions in Pakistan. According to our
study, there is a positive link between TOP and CO2 Emissions.
The regression coefficient of FDI is 0.069834 and it has a positive
sign; this means FDI has a positive relationship with CO2 and hurts
Environmental quality. One percent increase in FDI, CO2 increases
by 0.069834 %. In the analysis of studies, articles, journals, etc., we
also come to the same finding i.e. Foreign Direct Investment (FDI)
frequently targets industries reliant on natural resources, such as
mining and extraction. Unfortunately, these industries often
prioritize short-term profits without considering environmental
sustainability, leading to detrimental resource extraction practices.
The consequences include deforestation, which destroys vital
habitats and disrupts ecosystems, as well as soil erosion which
degrades agricultural productivity. Additionally, mining activities
often generate water pollution, contaminating rivers and streams
with toxic substances. These unsustainable practices contribute to
environmental degradation. The pollution in the environment that is
a result of the boost in the inflow of FDI is called the “pollution
haven” phenomenon. Other studies have also found similar results,
corroborating the findings such as [Wang et al (.2020), Huang et al
(2021)].
POP has a regression coefficient of 0.110819 and with a positive
sign, it is clear that the Population harms environmental quality.
With a one percent increase in POP, the CO2 increases by
0.110819%. Several studies have proven that a growing population
exerts pressure on infrastructure and public services, necessitating

Sajida Timsal, Ayesha Ashraf, Khazeeb Zahra & Salma Mouneer
95
the construction of new housing, transportation networks, energy
facilities, and water supply systems. Unfortunately, this
development often involves the conversion of natural areas into
urban or industrial zones. As cities expand, green spaces diminish,
leading to the loss of valuable ecosystems and biodiversity.
Additionally, urbanization leads to increased energy consumption
and emissions, contributing to climate change. The rising demand
for water resources strains already limited supplies, further
exacerbating environmental challenges. Human behavior plays a
significant role in environmental degradation as well. Unsustainable
consumption patterns, overexploitation of resources, pollution from
industrial and agricultural activities, and inadequate waste
management practices all contribute to environmental harm.
Addressing these issues requires adopting sustainable practices,
promoting conservation, and raising awareness about the
importance of responsible behaviors to preserve the environment for
future generations [A. Nagdeve (2007), Weber and Scuibba (2019),
Mittal (2013)].
The regression coefficient of NONRWN is 0.085704 and it has a
positive sign which depicts that NONRWN and CO2 are directly
related. With a one percent increase in NONRWN, CO2 increases
by 0.085704%. In Pakistan, the country heavily relies on non-
renewable energy sources, particularly coal and furnace oil, for
power generation. These energy sources are relatively cheaper and
more easily available compared to renewable energy alternatives.
As a result, Pakistan faces significant challenges in transitioning to
renewable energy due to the higher cost associated with renewable
technologies. The use of non-renewable energy sources in Pakistan
has led to increased air pollution and degradation of air quality. The
burning of coal and fossil fuels releases pollutants like particulate
matter (PM), nitrogen oxides (NOx), and sulfur dioxide (SO2)
into the air [Bello Ajide and Ibrahim (2021), Khan et al (2021),
Ansari (2017)].
Short-Run Estimates
The benefit of the ARDL technique is that it gives us both long and
short-run estimates. So, the short-run estimates are also found by
ARDL. Short-run estimates are as follows:

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96
Table 4: Short-Run Estimates
Co-integrating Form
Variable
Co-
efficient
Std.
Error
t-Stat
P-
value
D(Log_TOP)
0.006014
0.008258
0.728185
0.4749
D (LogTOP (-1))
-
0.019546
0.008937
-
2.187115
0.0408
D(LOGFDI)
0.042254
0.011131
3.795970
0.0011
D(LogPOP)
0.067053
0.067260
0.996927
0.3307
D(LogNONRNW)
0.188691
0.038598
4.888658
0.0001
CointEq (-1)
-
0.605071
0.121925
-
4.962643
0.0001
Cointeq = LogCO2(0.0476*Log_TOP_2+0.0698
*LogFDI_NT_INFLW +
0.1108*LogPOP_TOTL +0.0857*LogNONRNW -3.7711)
Source: this is made by the researcher by using Eviews
The table showing the estimates of the short run here depicts that
FDI and NONRWN have a significant impact but TOP and POP
have an insignificant impact on the Environment. The software has
automatically assumed a lag of TOP of one year, considering the
significant changes that have occurred and the system or software
has not taken any lag of any other variable.
The coefficient in the cointegration equation is significant as well as
negative. This implies that we are moving away from disequilibrium
by 60%. In other words, our model indicates a 60% adjustment
toward equilibrium.
The results in the short run differ from the long run because the short
run does not capture the fully matured effects faced by dependent

Sajida Timsal, Ayesha Ashraf, Khazeeb Zahra & Salma Mouneer
97
variable because of the independent variables. That is why we rely
on long-run results more.
Stability Test:
It is critical to ensure the stability of the model that is being
estimated. This goal can simply be achieved with the help of
CUSUM test and CUSUM Squares test results.
CUSUM Test
The following diagram shows the CUSUM test:-
-15
-10
-5
0
5
10
15
96 98 00 02 04 06 08 10 12 14 16 18 20
CUSUM 5% Significance
Source: this is made by the researcher by using Eviews 9
The blue line of CUSUM statistics in the above diagram lies
between the critical limits of red lines at 5% significance, so we can
conclude that our data and model are stable.

Perennial Journal of History, Vol V. No.1, 2024
98
CUSUM Squares Test
The following diagram shows the CUSUM Squares test: -
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
96 98 00 02 04 06 08 10 12 14 16 18 20
CUSUM of Squares 5% Signific ance
Source: this is made by the researcher by using Eviews 9
The critical red lines at 5% significance in the above diagram are
not getting touched and crossed by the blue line, confirming that the
model is stable.
Conclusion:
This study is carried out to find out how and to what extent trade
openness affects the environmental quality in Pakistan. To analyze
the impact of Trade Openness a model was created and then later on
estimated. CO2 Emissions as a proxy of the environment was the
dependent variable in this study while trade openness was the main
independent variable. Besides Population, Foreign Direct
Investment and Non-Renewable Energy were also taken as
independent variables.
ARDL methodology was employed in this work. The long-run and
short-run results are obtained and explained. The data of variables
ranged from 1990 to 2021, it was secondary data. In the short run,
all variables demonstrated a significant impact with log but
population had an insignificant impact with log. In the long run, all
variables showed a positive significant impact the results that were
obtained are completely backed up by theories and other works of

Sajida Timsal, Ayesha Ashraf, Khazeeb Zahra & Salma Mouneer
99
research as well. It has been proven that trade openness negatively
affects environmental quality.
Policy Suggestions:
Based on the estimations made in this research, the following
suggestions can be given regarding policy formulation: -
1. Explore the use of economic instruments such as
environmental taxes, carbon pricing, and tradable permits to
internalize the environmental costs of Trade activities. This
will allow the development of such economic incentives for
businesses that will help them lower their environmental
impact.
2. The government must thoroughly, strictly, and regularly
assess the big investments being made and projects being
carried that are related to trade. This is the only way to figure
out and point out any rising environmental concerns because
of such projects, and take timely action to combat the
negative effects to minimize them.
3. It should be made a compulsion for all FDI based projects to
undergo strict checking on the basis of Environmental
Impact Assessment before they even are granted approval.
Such an assessment will thoroughly check how these
projects are going to potentially affect the environment’s
quality and stability. If such projects even have little
potential for harming environment, effective mitigating
strategies must be put forward before the work actually
begins to control the negative results. Strict monitoring and
evaluation should follow the implementation of approved
projects.
4. Establish robust monitoring and evaluation mechanisms to
track the progress and effectiveness of environmental
policies and initiatives. Regularly assess the environmental
impact of Non-Renewable Energy sectors and the success of
renewable energy integration to ensure continuous
improvement and adaptation of policies.

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100
5. Implementing effective Population management strategies
can help address environmental challenges. This can include
promoting family planning services, education, and access
to contraceptives. Encouraging smaller family sizes can help
reduce the strain on resources and mitigate environmental
degradation.

Sajida Timsal, Ayesha Ashraf, Khazeeb Zahra & Salma Mouneer
101
References
1. Onwachukwu, C. I., Yan, K. M. I., & Tu, K. (2021). The
causal effect of trade liberalization on the
environment. Journal of Cleaner Production, 318, 128615.
2. Thuy, D. P. T., & Nguyen, H. T. (2022). Effects of trade
openness on environmental quality: Evidence from
developing countries.
3. Karedla, Y., Mishra, R., & Patel, N. (2021). The impact of
economic growth, trade openness and manufacturing on
CO2 emissions in India: an autoregressive distributive lag
(ARDL) bounds test approach. Journal of Economics,
Finance, and Administrative Science, 26(52), 376-389.
4. Khan, H., Weili, L., Khan, I., & Khamphengxay, S. (2021).
Renewable energy consumption, trade openness, and
environmental degradation: a panel data analysis of
developing and developed countries. Mathematical
Problems in Engineering, 2021, 1-13.
5. Chhabra, M., Giri, A. K., & Kumar, A. (2023). Do trade
openness and institutional quality contribute to carbon
emission reduction? Evidence from BRICS
countries. Environmental Science and Pollution
Research, 30(17), 50986-51002.
6. Dou, Y., Zhao, J., Malik, M. N., & Dong, K. (2021).
Assessing the impact of trade openness on CO2 emissions:
evidence from China-Japan-ROK FTA countries. Journal
of environmental management, 296, 113241.
7. Dauda, L., Long, X., Mensah, C. N., Salman, M., Boamah,
K. B., Ampon-Wireko, S., & Dogbe, C. S. K. (2021).
Innovation, trade openness, and CO2 emissions in selected
countries in Africa. Journal of Cleaner Production, 281,
125143.
8. Lv, Z., & Xu, T. (2019). Trade openness, urbanization, and
CO2 emissions: dynamic panel data analysis of middle-
income countries. The Journal of International Trade &
Economic Development, 28(3), 317-330.
9. Ansari, M. A., Haider, S., & Khan, N. A. (2020). Does trade
openness affects global carbon dioxide emissions: evidence

Perennial Journal of History, Vol V. No.1, 2024
102
from the top CO2 emitters. Management of Environmental
Quality: An International Journal, 31(1), 32-53.
10. Karlsson, J., & Paulsson, K. (2019). The effect of trade
openness on CO2 emissions.
11. Wang, Q., & Zhang, F. (2021). The effects of trade
openness on decoupling carbon emissions from economic
growth–evidence from 182 countries. Journal of cleaner
production, 279, 123838.
12. Khan, A., Safdar, S., & Nadeem, H. (2023). Decomposing
the effect of trade on the environment: a case study of
Pakistan. Environmental Science and Pollution
Research, 30(2), 3817-3834.
13. Ghouse, G., Hashmat, A., & Athar, A. (2021). Causal
Economic Interactions between CO2 Emissions and
Economic Growth. Pakistan Journal of Economic Studies
(PJES), 4(1), 45-61.
14. Awan, S. A., Meo, M. S., Ghimire, A., Wu, R. Y., &
Zhuang, P. F. (2018, May). Is trade openness good or bad
for the environment in Pakistan; An ARDL bounds testing
approach. In 4th Annual International Conference on
Management, Economics and Social Development
(ICMESD 2018) (pp. 822-827). Atlantis Press.
15. Shahbaz, M., Nasreen, S., Ahmed, K., & Hammoudeh, S.
(2017). Trade openness–carbon emissions nexus: the
importance of turning points of trade openness for country
panels. Energy Economics, 61, 221-232.
16. Ali, Z., Zaman, Z., & Ali, M. (2015). The effect of
international trade on carbon emissions: evidence from
Pakistan. Journal of Economics and Sustainable
Development, 6(9), 289-299.
17. Suhrab, M., Soomro, J. A., Ullah, S., & Chavara, J. (2023).
The effect of gross domestic product, urbanization, trade
openness, financial development, and renewable energy on
CO2 emission. Environmental Science and Pollution
Research, 30(9), 22985-22991.
18. Salman, A., Sethi, B., Aslam, F., & Kahloon, T. (2018).
Free trade agreements and environmental nexus in
Pakistan. Policy Perspectives, 15(3), 179-195.

Sajida Timsal, Ayesha Ashraf, Khazeeb Zahra & Salma Mouneer
103
19. Fatima, Z., Shah, F. N., Bashir, B., & Shazeb, M. (2022).
Impact of Energy Consumption and Trade on CO2
Emission in Pakistan. Journal of Economic Impact, 4(1),
99-105.
20. Akin, C. S. (2014). The impact of foreign trade, energy
consumption, and income on CO2 emissions. International
Journal of Energy Economics and Policy, 4(3), 465-475.
21. Antweiler, W., Copeland, B. R., & Taylor, M. S. (2001). Is
free trade good for the environment? American Economic
Review, 91(4), 877–908.
https://doi.org/10.1257/aer.91.4.877.
22. Khan, I., Han, L., Khan, H., & Kim Oanh, L. T. (2021).
Analyzing renewable and nonrenewable energy sources for
environmental quality: dynamic investigation in developing
countries. Mathematical Problems in Engineering, 2021, 1-
12.
23. Ibrahim, R. L., & Ajide, K. B. (2021). Disaggregated
environmental impacts of non-renewable energy and trade
openness in selected G-20 countries: the conditioning role
of technological innovation. Environmental Science and
Pollution Research, 28, 67496-67510.
24. Nagdeve, D. A. (2007). Population growth and
environmental degradation in India. International Institute
for Population Sciences. http://paa2007. Princeton.
edu/papers/7192. Department of fertility studies, Govandi
station road, Deonar, Mumbai, 400, 088.
25. Mittal, R., & Mittal, C. G. (2013). Impact of population
explosion on the environment. WeSchool “Knowledge
Builder”-The National Journal, 1(1).
26. Weber, H., & Sciubba, J. D. (2019). The effect of
population growth on the environment: evidence from
European regions. European Journal of Population, 35,
379-402.
27. Weber, H., & Sciubba, J. D. (2019). The effect of
population growth on the environment: evidence from
European regions. European Journal of Population, 35,
379-402.
28. Huang, Y., Chen, F., Wei, H., Xiang, J., Xu, Z., & Akram,
R. (2022). The impacts of FDI inflows on carbon emissions:

Perennial Journal of History, Vol V. No.1, 2024
104
Economic development and regulatory quality as
moderators. Frontiers in Energy Research, 9, 820596.
29. Huang, Y., Chen, F., Wei, H., Xiang, J., Xu, Z., & Akram,
R. (2021). The impacts of FDI inflows on carbon emissions:
Economic Development and regulatory quality as
moderators. Frontiers in Energy Research, 9.
30. Qamri, G. M., Sheng, B., Adeel-Farooq, R. M., & Alam, G. M.
(2022). The criticality of FDI in Environmental Degradation
through financial development and economic growth:
Implications for promoting the green sector. Resources
Policy, 78, 102765.
31. Demena, B. A., & Afesorgbor, S. K. (2020). The effect of
FDI on environmental emissions: Evidence from a meta-
analysis. Energy Policy, 138, 111192.
32. Demena, B., & Afesorgbor, S. K. (2019). The effect of FDI
on environmental emissions: Evidence from a meta-
analysis. ISS Working Paper Series/General
Series, 650(650), 1-41.
33. Jun, W., Mahmood, H., & Zakaria, M. (2020). Impact of
trade openness on the environment in China. Journal of
Business Economics and Management, 21(4), 1185-1202.
34. Wang, S., Wang, H., & Sun, Q. (2020). The impact of
foreign direct investment on environmental pollution in
China: Corruption matters. International journal of
environmental research and public health, 17(18), 6477.
35. Karedla, Y., Mishra, R., & Patel, N. (2021). The impact of
economic growth, trade openness and manufacturing on
CO2 emissions in India: an autoregressive distributive lag
(ARDL) bounds test approach. Journal of Economics,
Finance and Administrative Science, 26(52), 376-389.
36. Hussain, I., Wang, H., Safdar, M., Ho, Q. B., Wemegah, T.
D., & Noor, S. (2022). Estimation of Shipping Emissions in
Developing Country: A Case Study of Mohammad Bin
Qasim Port, Pakistan. International Journal of
Environmental Research and Public Health, 19(19), 11868.