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American Journal of  Environmental
Economics (AJEE) 

Foreign Direct Investment, Trade Openness and Environmental Degradation in SSA 
Countries. A Quadratic Modeling and Turning Point Approach

Enongene Betrand Ewane1*, Etah Ivo Ewane2

Volume 2 Issue 1, Year 2023
ISSN: 2833-7905 (Online)

DOI: https://doi.org/10.54536/ajee.v2i1.1414
https://journals.e-palli.com/home/index.php/ajee

Article Information ABSTRACT

Received: March 21, 2023

Accepted: April 29, 2023

Published: May 09, 2023

The consequences of  carbon dioxide (CO2) emissions in Sub Saharan Africa (SSA) countries 
cannot be ignore given it adverse effect on human health and global warming. With rising 
CO2 emissions and fallen volume of  trade openness and FDI inflows in recent time, we 
seek to examine the effect of  trade openness and foreign direct investment (FDI) on 
environmental degradation using time series data from 1975 to 2020 in SSA. Using the 
environmental Kuznets curve (EKC) framework, the study employs a quadratic modeling 
and turning point approach to realize the study objectives. The findings reveals that (1) the 
trade openness-EKC and FDI-EKC does not hold given the presence of  decreasing effects 
in the short run and increasing effects in the long run; (2) it confirms that a U-shaped trade 
openness-emissions and FDI-emission nexus holds given the decrease in trade openness 
and FDI in the short run and an increase in trade openness and FDI in the longrun; (3) The 
analysis supports the halo effect hypothesis before the turning point but the pollution haven 
hypothesis sets in after the turning point; (4) it shows evidence that trade openness and FDI 
contributes to reduce CO2 emissions in the short but increase it in the long run. The study 
recommends that SSA countries should adopt stringent environmental policies to attain 
sustainable economic growth without associative harm to the environment. 

Keywords

CO2 Emission, Foreign Direct 
Investment, Trade Openness, 
Environmental Degradation, 
Quadratic Modelling

1 Department of  Economics, University of  Buea, Cameroon
2 Higher technical teachers training college, University of  Buea, Cameroon
* Corresponding author’s e-mail: betrandenongene@yahoo.com

INTRODUCTION
Attaining environmental sustainability is indispensable 
to any nation irrespective of  any economic activity. 
Hence, understanding and reducing carbon dioxide(CO2) 
emission and other forms of  emissions on the 
environment is paramount to every nation to attain the 
United Nations Sustainable Development Goal by 2030 
which aims to address climate change (Adeleye et al., 2022). 
Hossain (2012) reveal that the surge in CO2 emission 
is a main threat to global warming and climate change 
for both developing and developed countries. Recent 
environmental statistics further reveal that CO2 emission 
has grown drastically in the last three decades because 
of  human activities(Duodu et al., 2021).  As a result, 
environmental concern has attracted a lot of  attention 
especially with the launch of  the Paris Agreement and the 
Kyoto Protocol to combat climate change and mitigate 
the effects of  CO2 emissions on the environment (Duodu 
et al., 2021).
The situation is precarious in SSA countries with less 
stringent environmental policies and high poverty rate. 
The World Bank report of  2020 indicates that Sub-
Sahara Africa has witnessed an increase in carbon dioxide 
emissions (in Kilotons) from 708066.43 in 2010 to 
823424.72 in 2018 (see figure 1). The trend further shows 
a continuous but fluctuating rise in CO2 from 1990 to 
2020. In 2014, it witness a double rise in CO2 by 7.52% 
compared to 1.88% in 2013. However, in 2015 the carbon 
emissions reduces by 4.11% but a 2.6% increase was 
again witnessed in 2016 and after 2016, there has been 
a continuous increased (see figure 1). With this increase 
in CO2 emissions, environmental quality is adversely 
affected especially with the inflow of  FDI and openness 

to trade. 
It is argued that FDI inflows lead to more CO2 emissions 
especially as investors explore cheaper means of  production 
which is hazardous to the environment(Nyeadi, 2022). 
This is particularly true for developing countries like SSA 
with relaxed environmental regulations thereby attracting 
firms with high pollution levels from developed countries- 
a situation known as pollution haven hypothesis (PHH)
(Yakubu & Musah, 2022). At the same time, it is claimed 
that FDI contributes positively to the economy of  the 
host country in the domain of  knowledge diffusion, 
technological transfer, and access to markets(Wei & 
Zhou, 2022). However, due to the consequences of  FDI 
on the environment, most countries in SSA have started 
putting in strict environmental policies for investors to 
achieve sustainable development reason why FDI has 
dropped in recent times. The World Bank report indicates 
that FDI as a percentage of  GDP increased from 2.3% in 
2010 to 2.6% in 2015 but it has witnessed a drop of  1.9% 
in 2016 to 1.8% in 2020. 
Trade openness is also a key factor that worsens 
environmental degradation as it increases the size of  the 
economy leading to an increase in pollution (Nguyen, 
2022). Pollution can be exported through the importation 
of  goods and services when free trade increases. This 
put more pressure on the environment as inputs such as 
energy and timber increase the volume of  water and air 
pollution emissions. Many empirical studies in SSA have 
validated the PHH on trade-emissions nexus by revealing 
that trade in SSA increases environmental pollution 
(Duodu et al., 2021). However, the World Bank reports 
indicates that Trade openness as a percentage of  GDP in 
SSA has drop from 22.4% in 2017 to 20% in 2020.

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Figure 1: Trend of  CO2 (kilo tons) from 1975 to 2020
Source: Author’s computation

There is still an ongoing debate regarding the relationship 
between FDI, trade openness, and environmental 
degradation. While some previous studies find FDI and 
Trade openness to positively affect the environment, 
others believe they are environmentally friendly especially 
with ecologically friendly technologies. This research 
contributes to the ongoing debate in the context of  SSA 
countries in the following ways; (1) by testing the validity 
of  the PHH and polo hypothesis. (2) To establish the 
exact turning point of  trade openness and FDI, (3) To 
assess if  the trade openness- Kuznets curve hypothesis 
holds (4) To test if  the FDI-Kuznets  hypothesis holds 
(5) To find out if  trade openness and FDI  exhibit a 
monotonic (increasing or decreasing) relationship with 
environmental degradation in SSA.

LITERATURE REVIEW 
The relationship between trade openness, FDI, and 
carbon dioxide emission is diverse with a heterogeneous 
conclusion. The difference in conclusion is based on 
variables used, method of  data analysis, and scope 
of  study. Different factors influence environmental 
degradation which will be examined in the course of  the 
extant literature. However, the literature will be examined 
under separate headings.

Trade Openness and Carbon Dioxide Emissions
There is a lack of  consensus concerning trade openness 
and CO2 emission (Karedla et al., 2021). While some 
studies support the hypothesis that trade openness 
intensifies environmental degradation(Al-Mulali et al., 
2016; Jun et al., 2020; Shahzad et al., 2017; Tariq et al., 
2018; Wen & Dai, 2020), others are in support that 
environmental degradation reduces when trade openness  
increases (Cui et al., 2015; Ghazouani et al., 2020; Shahbaz 
& Sinha, 2017).
Sun et al (2020) reveals that trade will decrease 
environmental pollution in SSA countries in the longrun. 
The square term for trade openness further indicates 

the presents of  environmental Kuznets curve (EKC) 
hypothesis. Similarly, Simplice & Nicholas (2021) using  
49 SSA countries as a case study from 2000–2018 found 
that increasing trade openness affects  CO2 emissions 
while increased in FDI negatively affects CO2 emissions. 
They also found a Kuznets shape between trade and 
CO2 emissions while a U-shape relationship was found 
between CO2 emissions and FDI inflows. Contrary, 
Asongu & Odhiambo (2020) using 49 SSA countries from 
2000-2018 reveals that trade has a negative and significant 
effect on carbon dioxide emissions. The study further 
indicates that the minimum trade threshold for SSA 
countries to experience green economy as a percentage 
of  GDP is 100.
Yue et al (2021) Conclude that trade openness affects 
carbon emission positively in China, Japan, and ROK 
FTA countries from 1970 to 2019. Similarly, Duan et al 
(2022) indicates that financial development and trade 
openness boost CO2 emissions in China from 1997 
to 2020 using a  PVAR model. Also, Alfred & Haug 
(2019) using a linear and nonlinear ARDL found that an 
increase in imports positively affect CO2 emissions in the 
long run while an increase in export does not increase 
CO2 emissions in Turkey. Similarly, Muratn (2019) using 
Developing Countries found that in the long run, trade 
openness and FDI are the main determinants of  CO2 
emissions. Likewise, Chen et al (2021) in a panel data of  
64 countries along the Belt and Road for 18 years indicate 
that trade openness improvement has a significantly 
positive effect on CO2 emissions. In a related study, 
Hdom & Fuinhas (2020) indicates that the more the 
Brazilian economy is open to trade, the more polluted it 
becomes. Rauf  et al (2018) indicates that in industrialized 
economies, manufacturing contributes positively toward 
pollution levels. Equally, Chhabra et al (2023) reveals that 
trade openness is the cause of  environmental degradation 
in the BRICS nations from 1991 to 2019. Sajeev & Kaur 
(2020) indicates that developing countries that have fewer 
environmental regulations and are open to trade and FDI, 

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witness an increase in CO2 emissions. Similarly, Yu et al. 
(2019) indicated that trade openness has a double-edged 
effect when decrease indirectly in CIS countries. They 
found trade openness to increase CO2. Likewise, Ertugrul 
et al (2016) found that in the top ten developing countries, 
trade openness and energy consumption are key factors 
of  CO2 emissions which results in high pollution.
Contrary, Karedla et al (2021) using ARDL for a period 
of  45 years reveals that in the longrun, trade openness 
significantly decreases CO2 emissions in India.  Zhang 
et al (2017) found that trade openness negatively affects 
carbon emissions in ten countries. Similarly, (Appiah et 
al., 2022; Dauda et al., 2021; Khan et al., 2022) found a 
significant negative effect of  trade on environmental 
sustainability. However, (Keun-yeob & Bhuyan, 2018) 
using Bangladesh as a case study for 38 years reveals that 
in both the short and longrun, a negative and insignificant 
relationship exist between trade liberalization and 
environmental degradation. 
Other studies found contradictory results. Keho (2016) 
using ECOWAS as a case study obtain mixed results 
across countries. He reveals that in some countries 
trade worsens degradation via air quality while it is 
environmentally friendly in other countries. Wang & 
Zhang (2021) observed that in low-income countries 
there is a positive relationship between pollution and 
trade openness while in middles income countries the 
nexus is negative. Similarly, Jayanthakumaran et al (2012) 
also observed the relationship is negative in China but 
positive in India. Le et al (2016) stressed that the quality 
of  the environment may be worsen through trade but 
the effect differs across regions. Sun et al (2019) using 
SAARC countries found both positive and negative 
impacts of  trade on emissions. They found that in 
SAARC countries, trade and FDI have a long run positive 
effect on environmental degradation while in the short 
run, a negative relationship was witnessed between FDI 
and trade inflows with CO2 emissions.

FDI and Environmental Degradation
The “pollution heaven” hypothesis indicates that in the 
host country, FDI affects the natural environment while 
the “pollution halo” hypothesis points out that FDI 
improves the natural environment of  the host country 
(Wei & Zhou, 2022).
Starting with regional specific studies, Duodu et al (2021) 
employs the GMM across 23 SSA countries and conclude 
that in the short run when FDI is merged with policies 
and institutions for environmental sustainability, it 
deteriorates environmental quality but in the long run, the 
quality of  the environment is improved. Equally, Kivyiro 
& Arminen (2014) using six SSA countries from 1971-
2009 in an ARDL framework concludes that  FDI has 
a positive and significant effect on carbon emissions in 
Zimbabwe but a negative and significant impact in South 
Africa, Kenya, and the Democratic Republic of  Congo.  
Ojewumi & Akinlo (2017) also used Panel Vector Error 
Correction to reveal that FDI positively and significantly 

affects CO2 emissions in 33 SSA countries from 1980 to 
2013.  Similarly, Ssali et al (2019) examine the relationship 
between CO2 emission, economic growth, and FDI in 
6 selected SSA countries from 1980-2014. Using the 
ARDL and PMG Estimator, the results reveal that FDI 
does not have any significant impact on CO2 emission 
while GDP and energy use reduce environmental quality. 
In addition, Asongu & Odhiambo (2020) using 49 SSA 
countries from 2000-2018 reveals that FDI has a positive 
and significant effect on carbon dioxide emissions. The 
study further indicates that the minimum FDI threshold 
for SSA countries to experience green economy as 
a percentage of  GDP is 200. Contrary, Adams et al 
(2020) also found that FDI and regulatory quality has a 
significant negative effect on environmental quality across 
19 SSA countries for 31 years using DFE model and 
GMM. Ganda (2020) Using 44 SSA countries for 14 years 
showed that renewable energy consumption and financial 
development negatively affect environmental quality 
while economic growth positively affects environmental 
quality. 
In other economies, Bongsuk et al (2018) found that 
FDI decreases CO2 emission levels in the Chinese 
manufacturing sector from 2002–2015. Similarly, 
Abdulkadir et al (2018) using PMG estimators found that 
FDI inflows negatively affect environment degradation in 
GCC for 24 years. Also, Binyam & Sylvanus (2020) using 
a meta-analysis of  65 primary studies indicates that FDI 
can significantly reduce CO2 only after accounting for 
heterogeneity.  To et al (2019) with a focus on emerging 
markets in Asia from 1980–2016 found that FDI has a 
strong impact on the environment. Likewise, Huang et al 
(2022) using G20 economies from 1996 to 2018 reveals 
that the nexus between FDI inflows and CO2 emissions 
is positive. However, in countries with advanced 
development and regulatory quality, carbon emissions can 
be mitigated. Similarly, Nyeadi (2022) found a positive 
and significant relationship between FDI and carbon 
dioxide in low-income countries. 
Based on the pollution heaven hypothesis and fully 
modified least squares (FMOLS) technique Yakubu & 
Musah (2022) concludes that FDI inflows positively and 
significantly drive environmental pollution in Ghana 
from 2000Q1-2017Q4. Bunyaminu & Yakubu (2022) 
also found that FDI inflows decreases renewable energy 
demand but increases carbon dioxide emissions in Africa, 
hence supporting the PHH. Similarly, Khalid & Zaitouni 
(2021) support the PHH in Philippines only while in 
Malaysia and Singapore they support the pollution 
halo hypothesis. Also, some empirical studies rebut the 
PHH by indicating that FDI reduces environmental 
pollution(Al-Nimer et al., 2022; Kathuria, 2016; Nathaniel 
et al., 2020; Pradhan et al., 2022; Shahbaz et al., 2019; C. 
Zhang & Zhou, 2016). However, some empirical studies 
found contradictory results by accepting or rejecting the 
PHH base on the nexus between FDI and environmental 
pollution(Huynh & Hoang, 2019; Kisswani & Zaitouni, 
2021; Yilanci et al., 2020). 

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Some empirical studies did not find any significant 
relationship between FDI and CO2 emissions. Ponce et al 
(2022) used 100 countries for 39years to conclude that the 
role of  foreign direct investment on the environment is 
not stable over time. Similarly, Jugurnath & Emrith (2018) 
found that FDI does not significantly increase the levels 
of  CO2 emissions in SIDS countries. In addition, Alfred 
& Haug (2019) found that increases in FDI have no long-
run significant effects on CO2 emissions per capita. 

METHODOLOGY
Data
The study makes use of  time series data in SSA from 
world development indicators from 1975 to 2020. Carbon 
dioxide emission measured in kilo tons per capita is the 
dependent variable while trade openness (% of  GDP) and 
FDI inflows (% of  GDP) are the independent variables. 
Population growth and economic growth in dollars are 
control variables. Past studies have made use of  these 
control variables. For economic growth see (Adedoyin 
et al., 2021; Akam et al., 2021; Chontanawat, 2020), for  
population growth see (Adedoyin & Bekun, 2020; Bhat, 
2018; Yasin et al., 2020) 

Estimation Technique
We initiate our empirical analysis by first conducting the 
unit root test to ascertain the order integration. The risk 
of  non-stationary series cannot be over emphasized as 
it results to spurious regression(Gujarati, 2004). Hence, 
the study make use of  ADF and Zivot Andrew unit root 
test, which shows evidence of  I(1) series (see appendix 
3 and 4). We also conducted the summary statistics to 
display the averages, deviations, minimum, and maximum 
values (see appendix  1) and the correlation test to make 
sure no multicollinearity exist in the series (see appendix 
2). The pairwise correlation table in appendix 2 reveals 
that all variables are positively link to carbon missions.  In 
addition, there is no evidence of  exact linear dependence 
among the regressors as all the correlation statistics are 
below 0.70(Ewane & Abonongi, 2022)
Since our interest is to find out if  the relationship between 
trade openness, FDI, and CO2 emission is non linear 
or monotonic, we employ a quadratic function which 
allows the effect of  the independent variable (X) on the 
dependent variable to change. That is as the value of  X 
increases, the impact of  the dependent variable increases 
or decreases. The technique is use to find the equation of  
the parabola that best fits a set of  data- a second order 
polynomial model with the presence of  a square term. As 
a result, we get an equation of  the form: y=aX2+bX+c 
where a≠0.  

Empirical Specification
Adopting the study of   Adeleye et al (2022) and Adeleye 
(2023), the model with carbon dioxide emission express 
as a linear function of  control variables is specified as 

follows;
logC02t = β0 + β1logTOt + β2logPOPt + β3logGDPt +μ..1
logC02t = β0 + β1FDIt + β2logPOPt +β3logGDPt +μ……2
To find evidence of  the trade openness-EKC and FDI-
EKC, both the level and square term of  trade openness 
and FDI are included in equations 1 and 2 as shown 
below.
logC02t = β0 + β1logTOt + β2(logTOt )

2+ β3 logPOPt +β4 
logGDPt +μ……3
logC02t = β0 + β1FDIt + β2(FDIt )2+ β3 logPOPt +β4 
logGDPt +μ……4
Where CO2 is the carbon dioxide emission per capita; TO 
is trade openness; FDI is foreign direct investment; POP 
is population; GDP is growth domestic product. β1, β2, 
β3, β4 are parameters to be estimated while μ is the error 
term.
From equation 3 and 4, the various forms of  the 
relationship between trade openness, FDI, and carbon 
dioxide emission can be tested as follows;
β1< 0, β2> 0, the relationship is a U-shaped 
β1> 0, β2< 0, the relationship is inverse U-shaped
β1> 0, β2> 0, the relationship is monotonically increasing 
linear
β1< 0, β2< 0 the relationship is monotonically decreasing 
linear
β1= 0, β2= 0, the relationship is level.
However, deriving the turning point works with the 
outcome of   β1< 0, β2> 0, 
β1> 0, β2< 0, and β1 and  β2 must be significant(Adeleye 
et al., 2022)
The turning points from equation 3 and 4 can be obtained 
by taking the first derivatives and setting the equation to 
zero as follows;
(∂logC02/∂logTO) =β1 +(β2*2)logTO=0
β1= - (β2*2)logTO → logTO*=-o.5 (β1/β2)
(∂logC02/∂logFDI) =β1 +(β2*2)logFDI=0
β1= - (β2*2)logFDI →  logFDI*=-o.5 (β1/β2)
Where logTO* and logFDI* represents the threshold of  
trade openness and FDI outside which it decreases or 
increases carbon emissions based on the signs of  β1 and  
β2  
Such that: β1<0, β2> 0 for U-shape relationship or β1> 0, 
β2< 0 for inverse U-shape relationship. The trade or FDI 
turning point of  this curve is computed as follows; 
τ=0.5 (β1⁄β2 )………5
However, equation 5 has to be converted into it exponential 
form to obtain the real values. The exponential form of  
the equation become thus; 
τ=exp0.5 (β1⁄β2 )
To make sure the turning point stays within the limit 
values of  trade openness and FDI, some variables are 
estimated at levels while others are in their log forms. The 
a priori expectation is such that β1, β2, β3, β4>0. 

RESULTS AND DISCUSSION
The linear results in Table 1 reveals that population 

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and economic growth positively affect environmental 
degradation. Hence, a 1% increase in each of  them will 
increase cardo dioxide emission by 29.9% and 22.7% 
respectively and both are significant at 1% level. The 
results is consistent with the nonlinear results. Thus, 
rising population and advanced economic growth in SSA 
countries deteriorate environmental degradation. 
The linear result of  trade openness is negatively related 
to environmental degradation. A 1% increase in trade 
openness reduces CO2 emissions by 16.8% at a 1% 
significant level. Furthermore, the nonlinear square 
term of  trade openness is positive and significant 
violating the inverted U-shaped EKC theory (see figure 
2) but validates a U-shape relationship. This implies that 
countries in SSA countries reach a threshold limits after 
which the deteriorating effect of  trade openness on the 
environment starts to increase. With a turning point 
of  3.54 (see figure 2), trade openness initially declines 
environmental degradation but as trade advances outside 
the optimal point of  34.5 (% of  GDP), the level of  
emissions rises causing environmental degradation
The second estimate is that where FDI is the main 
explanatory variable. The linear results indicate that 
population negatively affects environmental degradation 
while economic growth has a positive impact at a 1% 
significant level. The findings is also true with that of  the 
nonlinear results. Also, the linear results reveal that FDI 
has a negative impact on environmental degradation. That 

is a percentage increase in FDI reduces carbon dioxide 
emission by 10.5% at a 1% significant level. The linear 
results of  equation 1 and 2 indicates that liberalizing trade 
and encouraging FDI decreases the adverse effect of  CO2 
emissions in SSA, which improve environmental quality 
(Acheampong et al., 2019; Alvarado et al., 2022) 
However, the non linear square term of  FDI is positively 
related to environmental degradation, which contradicts 
the EKC theory (see figure 2). This indicates that in the 
long run, FDI cause harm to the environment. This 
conclusion is vital because SSA countries attract FDI 
by adopting less stringent environmental laws. The 
turning point occurs at 2.19(see figure 2) indicating 
that at 8.9% (% of  GDP) turnaround point, carbon 
emission starts to increase. This implies that SSA witness 
a fall in environmental sustainability at the early stage of  
development through FDI but once a threshold point is 
attain, the effect on the environment deteriorates. Hence, 
the early development stage supports the halo effect 
hypothesis while the pollution haven hypothesis sets in 
after the turning point. The rationale for this positive rise 
in environmental degradation is due to reallocation of  
firms with high polluting from developed countries with 
high-income who have strict rules on regulating quality 
to developing countries with low-income who have 
less strict environmental regulations (Chang et al., 2019; 
Doytch & Uctum, 2016). 

Table 1: linear and nonlinear regression
VARIABLES Equation (1) 

estimates
VARIABLES Equation (2) 

estimates
linear Non-linear linear Non-linear

LogPOP 0.299*** 0.337*** logPOP -0.585*** -0.500***
-0.0738 -0.0902 -0.0701 -0.0993

logGDP 0.227*** 0.209*** logGDP 0.193*** 0.178***
-0.0336 -0.039 -0.028 -0.03

logTO -0.168*** -1.783** FDI -0.0163 -0.105***
-0.0531 -2.082 -0.0157 -0.0628

LogTOsquare 0.251*** FDIsquare 0.0228*
-0.324 -0.013

Turning point 3.5393973 Turning point 2.1865719
constant 1.536* 3.832 Constant 7.505*** 6.255***

-0.764 -2.931 -0.788 -1.235
Observations 46 46 Observations 46 46
R-squared 0.945 0.945 R-squared 0.651 0.673
F-Statistic 699.19 715.86 F-Statistic 49.4 44.79
Prob > F 0 0 Prob > F 0 0
Robust standard errors in parentheses  *** p<0.01, ** p<0.05, * p<0.1
Source: Author’s computation

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CONCLUSION 
The link between FDI, trade openness, and carbon 
dioxide emissions is an ongoing debate. We contributes 
to the recent debate by means of  a time series data in 
SSA countries from 1975 to 2020. Employing a quadratic 
modeling and turning point technique, we found that;

 (1) the trade openness and FDI does follow the EKC 
hypothesis due to the presence of  decreasing effects in 
the short run and increasing effect in the long run; 

(2) The U-shaped trade openness and FDI-emission 
nexus holds given the decreasing effect of  trade openness 
and FDI in the short run and an increasing effect of  trade 
openness and FDI in the long run; 

(3) The analysis supports the halo effect hypothesis 
before the turning point but the pollution haven 
hypothesis sets in after the turning point; (4) it shows 
evidence that trade openness and FDI negatively and 
significantly effect environmental degradation in the 
short run but positively and significantly contributes to 
environmental degradation in the long run.
The negative effect of  trade openness and FDI 
on environmental degradation can be mitigated 
through stringent environmental policies to safeguard 
environmental sustainability. This is plausible through the 
adoption of  green technologies that are environmentally 
friendly in producing goods and services. Also, the 
transport sector which is recognized as the sector with 
high CO2 emissions should be modernized to avoid 
CO2 emissions to the earth’s surface which causes health 
problems. In addition, to avoid pollution, the government 
of  each SSA country should initiate a CO2 emission 
management schemed to reduce the negative effects of  

CO2 emissions. Trade openness should be encouraged 
among member countries in SSA countries as they share 
a similar idea of  CO2 emission reduction strategy. In 
this case, they can easily adopt a suitable technology like 
green technology that is CO2 emission friendly in their 
manufacturing sector.

REFERENCES
Abdulkadir, A. ., Ibrahim, M. M., & Rayyanu, A. K. (2018). 

The effects of  FDI and energy consumption on 
environmental pollution in predominantly resource-
based economies of  the GCC. Sustainable Energy 
Technologies and Assessments, 25, 126–137. https://doi.
org/https://doi.org/10.1016/j.seta.2017.12.008

Acheampong, A., Adams, S., & Boateng, E. (2019). Do 
globalization and renewable energy contribute to 
carbon emissions mitigation in Sub-Saharan Africa? 
Sci Total Environ, 677, 436–446. https://doi.org/
https://doi. org/10.1016/j.scitotenv.2019.04.353

Adams, S., Boateng. E, & Acheampong, A. (2020). 
Transport energy consumption and environmental 
quality: Does urbanization matter?. Science of  The Total 
Environment, 744, 140617. https://doi.org/https://
doi.org/10.1016/j.scitotenv.2020.140617

Adedoyin, F., & Bekun, F. (2020). Modelling the 
interaction between tourism, energy consumption, 
pollutant emissions and urbanization: renewed 
evidence from panel VAR. Environ Sci Pollut Res, 
27(31), 38881–38900. https://doi.org/https://doi.
org/10.1007/ s11356-020-09869-9

Adedoyin, F., Nathaniel, S., & Adeleye, N. (2021). An 
investigation into the anthropogenic nexus among 

Figure 2: Turning points of  trade openness and FDI from QM technique.
Notes exp^3.54=34.5(% of  GDP), exp^2.19=8.9(% of  GDP)
Source: Author’s computations

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consumption of  energy, tourism, and economic 
growth: do economic policy uncertainties matter? 
Environ Sci Pollut Res, 28(3), 2835–2847. https://doi.
org/https://doi.org/ 10.1007/s11356-020-10638-x

Adeleye, B. N., Akam, D., Inuwa, N., James, H. T., & 
Basila, D. (2022). Does globalization and energy 
usage influence carbon emissions in South Asia? 
An empirical revisit of  the debate. Environmental 
Science and Pollution Research, 0123456789. https://doi.
org/10.1007/s11356-022-24457-9

Adeleye, N. (2023). Quadratic Modeling & Application : 
An Evidence Based Approach. February. https://doi.
org/10.13140/RG.2.2.22021.63208

Akam, D., Owolabi, O., & Nathaniel, S. (2021). Linking 
external debt and renewable energy to environmental 
sustainability in heavily indebted poor countries: new 
insights from advanced panel estimators. Environ Sci 
Pollut Res. https://doi.org/https://doi.org/10.1007/ 
s11356-021-15191-9

Al-Mulali, U., Solarin, S. A., & Ozturk, I. (2016). 
Investigating the presence of  the environmental 
Kuznets curve (EKC) hypothesis in Kenya: an 
autoregressive distributed lag (ARDL) approach. 
Natural Hazards, 80(3), 1729–1747.

Al-Nimer, M., Kayed, S., Ullah, R., Khan, N. U., & Khattak, 
M. S. (2022). Mapping the Research between Foreign 
Direct Investment and Environmental Concerns; Where 
Are We and Where to Go? Sustainability (Switzerland), 
14(24). https://doi.org/10.3390/su142416930

Alfred, A., & Haug, M. U. (2019). The role of  trade and FDI 
for CO2 emissions in Turkey: Nonlinear relationships. 
Energy Economics, 81, 297–307. https://doi.org/
https://doi.org/10.1016/j.eneco.2019.04.006.

Alvarado, R., Tillaguango, B., Murshed, M., Ochoa-
Moreno, S Rehman, A., Işık, C., & Alvarado-Espejo, 
J. (2022). Impact of  the informal economy on the 
ecological footprint: the role of  urban concentration 
and globalization. Economic Analysis and Policy, 75, 
750–767. https://doi.org/https://doi.org/10.1016/j.
eap.2022.07.001

Appiah, K., Worae, T., Yeboah, B., & Yeboah, M. 
(2022). The causal nexus between trade openness 
and environmental pollution in selected emerging 
economies. Ecol Indic, 138, 108872. https://doi.org/
https://doi.org/10. 1016/j.ecolind.2022.108872

Asongu, S., & Odhiambo, N. (2020). Trade and FDI 
Thresholds of  CO2 emissions for a Green Economy 
in SubSaharan Africa (No.20/072). African Governance 
and Development Institute. https://doi.org/10.1108/
IJESM-06-2020-0006.

Bhat, J. (2018). Renewable and non-renewable energy 
consumption— impact on economic growth and 
CO2 emissions in fve emerging market economies. 
Environ Sci Pollut Res, 25(35), 35515–35530.

Binyam, A., & Sylvanus, K. A. (2020). The effect of  FDI 
on environmental emissions: Evidence from a meta-
analysis. Energy Policy, 138, 111192. https://doi.org/
https://doi.org/10.1016/j.enpol.2019.111192.

Bongsuk, S., Woo-Yong, S., & Sang-Do, P. (2018). How 
foreign direct investment affects CO2 emission 
levels in the Chinese manufacturing industry: 
Evidence from panel data. Economic Systems, 42(2), 
320–331. https://doi.org/ttps://doi.org/10.1016/j.
ecosys.2017.06.002.

Bunyaminu, A., & Yakubu, I. N. (2022). Green Energy 
Demand and Financial Development: Evidence 
from Africa. Finance for Sustainability in a Turbulent 
Economy. IGI Global, 10, 978–1.

Chang, C., Dong, M., Sui, B., & Chu, Y. (2019). Driving 
forces of  global carbon emissions: from time-and 
spatial-dynamic perspectives. Econ Model. https://doi.
org/.https://doi.org/10.1016/j.econmod.2019.01.021

Chen, F., Jiang, G., & Kitila, G. M. (2021). Trade openness 
and CO2 emissions: The heterogeneous and mediating 
effects for the belt and road countries. Sustainability 
(Switzerland), 13(4), 1–16. https://doi.org/10.3390/
su13041958

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. https://doi.org/10.1007/s11356-
023-25789-w

Chontanawat, J. (2020). Relationship between energy 
consumption, CO2 emission and economic growth 
in ASEAN: cointegration and causality model. Energy 
Rep, 6, 660–665. https://doi.org/https://doi.org/10. 
1016/j.egyr.2019.09.046

Cui, J., Lapan, H., & Moschini, G. (2015). Productivity, 
export, and environmental performance: Air 
pollutants in the United States. J. Agr. Econ, 96(66).

Dauda, L., Long, X., & Mensah, C. et al. (2021). 
Innovation, trade openness and CO2 emissions 
in selected countries in Africa. J Clean Prod, 281, 
125143. https://doi.org/. https://doi.org/10.1016/j.
jclepro.2020.125143

Doytch, N., & Uctum, M. (2016). Globalisation and the 
environmental impact of  sectoral FDI. Econ Syst, 
404, 582–594. https://doi.org/https://doi.org/ 
10.1016/j.ecosys.2016.02.005

Duan, K., Cao, M., & Abdul Kader Malim, N. (2022). The 
Relationship between Trade Liberalization, Financial 
Development and Carbon Dioxide Emission—An 
Empirical Analysis. Sustainability (Switzerland), 14(16). 
https://doi.org/10.3390/su141610308

Duodu, E., Kwarteng, E., Fosu Oteng-Abayie, E., & 
Boakye Frimpong, P. (2021). Environmental Science 
and Pollution Research on July 30th, 2021. See the 
published version. In Environmental Science and 
Pollution.

Ertugrul, H., Cetin, M., Seker, F., & Dogan, E. (2016). The 
impact of  trade openness on global carbon dioxide 
emissions: evidence from the top ten emitters among 
developing countries. Ecol Indic, 67, 543– 555. https://
doi.org/https://doi.org/10.1016/j.ecolind.2016.03.027

Ewane, E. B., & Abonongi, A. S. (2022). Inflation 

https://journals.e-palli.com/home/index.php/ajee


Pa
ge

 
16

https://journals.e-palli.com/home/index.php/ajee

Am. J. Environ Econ. 2(1) 9-18, 2023

Rate Volatility and Household Final Consumption 
Expenditure: Evidence from Cameroon. Journal 
of  Economics, Management and Trade, 28(10), 89–99. 
https://doi.org/10.9734/jemt/2022/v28i1030451

Ganda, F. (2020). The influence of  growth determinants 
on environmental quality in Sub-Saharan Africa 
states. Environment, Development and Sustainability, 1–23. 
https://doi.org/10.1007/s10668-020-00907- 7

Ghazouani, T., Boukhatem, J., & Yan Sam, C. (2020).
Causal interactions between trade openness, 
renewable electricity consumption, and economic 
growth in Asia-Pacific countries: fresh evidence from 
a bootstrap ARDL approach. Renewable and Sustainable 
Energy Reviews, 133, 110094.

Gujarati, D. N. (2004). Basic Econometrics, 4th ed. In 
McGraw-Hill Companies, New York.

Hdom, H. A., & Fuinhas, J. A. (2020). Fuinhas, Energy 
production and trade openness: Assessing economic 
growth, CO2 emissions and the applicability of  the 
cointegration analysis. Energy Strategy Rev, 30, 100488.

Hossain, S. (2012). An Econometric Analysis for CO2 
Emissions, Energy Consumption, Economic Growth, 
Foreign Trade and Urbanization of  Japan. Low Carbon 
Economy, 3(3), 92–105.

Huang, Y., Chen, F., Wei, H., Xiang, J., Xu, Z., & 
Akram, R. (2022). The Impacts of  FDI Inflows on 
Carbon Emissions: Economic Development and 
Regulatory Quality as Moderators. Frontiers in Energy 
Research, 9(January), 1–11. https://doi.org/10.3389/
fenrg.2021.820596

Huynh, C. M., & Hoang, H. H. (2019). Foreign direct 
investment and air pollution in Asian countries: does 
institutional quality matter?. Applied Economics Letters, 
26(17), 1388-1392.

Jayanthakumaran, K., Verma, R., & Liu, Y. (2012). CO2 
emissions, energy consumption, trade and income: a 
comparative analysis of  China and India. Energy Policy, 
42, 450–460.

Jugurnath, B., & Emrith, A. (2018). Impact Of  Foreign 
Direct Investment On Environment Degradation: 
Evidence From SIDS Countries. The Journal 
of  Developing Areas, 55(2), 13–26. https://doi.
org/10.1353/jda.2018.0019

Jun, W., Mahmood, H., & Zakaria, M. (2020). Impact of  
trade openness on environment in China. Journal of  
Business Economics and Management, 21(4), 1185-1202.

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. https://doi.org/10.1108/
JEFAS-05-2021-0057

Kathuria, V. (2016). Does Environmental Governance 
matter for FDI?–Testing for Pollution Haven 
Hypothesis for Indian States Vinish Kathuria. 
Copenhagen Discussion Papers, 2016-54. 
Retrieved from https://www.econstor.eu/

handle/10419/208652.
Keho, Y. (2016). Trade openness and the environment : 

A time series study of  ECOWAS countries. Journal 
of  Economics, 4(4), 61–69. https://doi.org/10.15640/
jeds.v4n4a6

Keun-yeob, O., & Bhuyan, I. (2018). Trade Openness 
and CO 2 Emissions : Evidence of. Asian Journal of  
Atmospheric Environment, 12(1), 30–36. https://doi.
org/doi: https://doi.org/10.5572/ajae.2018.12.1.030

Khalid, M. K., & Zaitouni, M. (2021). Does FDI affect 
environmental degradation? Examining pollution 
haven and pollution halo hypotheses using ARDL 
modelling. Journal of  the Asia Pacific Economy. https://
doi.org/https://doi.org/10.1080/13547860.2021.19
49086

Khan, H., Weili, L., & Khan, I. (2022). Environmental 
innovation, trade openness and quality institutions: 
an integrated investigation about environmental 
sustainability. Environ Dev Sustain, 24, 3832–3862. 
https://doi.org/https://doi.org/10.1007/s10668-
021-01590-y

Kisswani, K. M., & Zaitouni, M. (2021). Does FDI affect 
environmental degradation? Examining pollution 
haven and pollution halo hypotheses using ARDL 
modelling. Journal of  the Asia Pacific Economy, 1–27.

Kivyiro, P., & Arminen, H. (2014). Carbon dioxide 
emissions, energy consumption, economic growth, 
and foreign direct investment: Causality analysis for 
Sub-Saharan Africa. Energy, 74, 595–606. https://doi.
org/6. https://dorg/10.1016/j.energy.2014.07.025

Le, T.-H., Chang, Y., & Park, D. (2016). Trade openness 
and environmental quality: international evidence. 
Energy Policy, 92, 45–55. https://doi.org/https:// doi.
org/10.1016/j.enpol.2016.01.030

Murat, H. (2019). The impact of  trade openness on 
global carbon dioxide emissions : Evidence from the 
top ten emitters among developing countries.MPRA 
Paper No. 97539 (Issue 97539).

Nathaniel, S., Aguegboh, E., Iheonu, C., Sharma, G., 
& Shah, M. (2020). Energy consumption, FDI, 
and urbanization linkage in coastal Mediterranean 
countries: re-assessing the pollution haven hypothesis. 
Environmental Science and Pollution Research, 27(28), 
35474–35487.

Nguyen, H. T. (2022). Effects of  trade openness on 
environmental quality : Evidence from developing 
countries. 1–27.

Nyeadi, J. D. (2022). The impact of  financial development 
and foreign direct investment on environmental 
sustainability in Sub-Saharan Africa: using PMG-
ARDL approach. Economic Research-Ekonomska 
Istrazivanja , 36(1), 2796–2818. https://doi.org/10.1
080/1331677X.2022.2106270

Ojewumi, S., & Akinlo, A. (2017). Foreign direct 
investment, economic growth and environmental 
quality in subSaharan Africa: a dynamic model analysis. 
African Journal of  Economic Review, 5(1), 48–68. https://
doi.org/Retrieved from file:///D:/ALL/149252-

https://journals.e-palli.com/home/index.php/ajee


Pa
ge

 
17

https://journals.e-palli.com/home/index.php/ajee

Am. J. Environ Econ. 2(1) 9-18, 2023

Article%20Text-392760-1-10-20161209.pdf
Ponce, P., Álvarez-García, J., Álvarez, V., & Irfan, M. 

(2022). Analysing the influence of  foreign direct 
investment and urbanization on the development of  
private financial system and its ecological footprint. 
Environmental Science and Pollution Research, 9624–9641. 
https://doi.org/10.1007/s11356-022-22772-9

Pradhan, A. K., Sachan, A., Sahu, U. K., & Mohindra, 
V. (2022). Do foreign direct investment inflows 
affect environmental degradation in BRICS nations?. 
Environmental Science and Pollution Research, 29(1), 690–
701.

Rauf, A., Zhang, J., Li, J., & Amin, W. (2018). Structural 
changes, energy consumption and carbon emissions 
in China: empirical evidence from ARDL bound 
testing model. Structural Change and Economic Dynamics, 
47, 194–206.

Sajeev, A., & Kaur, S. (2020). Environmental sustainability, 
trade and economic growth in India: implications 
for public policy,. International Trade, Politics and 
Development, 4(2), 141–160.

Shahbaz, M., Balsalobre-Lorente, D., & Sinha, A. (2019). 
Foreign Direct Investment–CO2 emissions nexus in 
Middle East and North African countries: Importance 
of  biomass energy consumption. Journal of  Cleaner 
Production, 217, 603–614.

Shahbaz, M., & Sinha, A. (2017). Trade openness-carbon 
emissions nexus: the importance of  turning points of  
trade openness for country panels. Energy Economics, 
61, 221-232.

Shahzad, S. J. H., Kumar, R. R., Zakaria, M., & Hurr, M. 
(2017). Carbon emission, energy consumption, trade 
openness and financial development in Pakistan: A 
revisit. Energy Rev, 70, 185–192.

Simplice, A., & Nicholas, M. O. (2021). Trade and FDI 
thresholds of  CO2 emissions for a Green economy in 
sub-Saharan Africa. International Journal of  Energy Sector 
Management, 15(1).

Ssali, M., Du, J., Mensah, I., & Hongo, D. (2019). 
Investigating the nexus among environmental 
pollution, economic growth, energy use, and foreign 
direct investment in 6 selected sub-Saharan African 
countries. Environmental Science and Pollution Research, 
26(11), 11245–11260. https://doi.org/https://doi.
org/10.1007/s11356-019-04455-0

Sun, H., Enna, L., Monney, A., Tran, D. K., Rasoulinezhad, 
E., & Taghizadeh-Hesary, F. (2020). The long-run 
effects of  trade openness on carbon emissions in 
sub-saharan african countries. Energies, 13(20), 1–18. 
https://doi.org/10.3390/en13205295

Sun, H., Tariq, G., Haris, M., & Mohsin, M. (2019). 
Evaluating the environmental efects of  economic 
openness: evidence from SAARC countries. Environ 
Sci Pollut Res, 26, 24542–24551. https://doi.org/. 
https://doi. org/10.1007/s11356-019-05750-6

Tariq, G., Sun, H., Haris, M., Kong, Y., & Nadeem, A. 
(2018). Trade liberalization, FDI inflows economic 
growth and environmental sustainability in Pakistan 
and India. J. Agric. Environ. Int. Dev, 112, 253–269.

To, A. H., Ha, D. T. T., Nguyen, H. M., & Vo, D. H. 
(2019). The impact of  foreign direct investment on 
environment degradation: Evidence from emerging 
markets in Asia. International Journal of  Environmental 
Research and Public Health, 16(9). https://doi.
org/10.3390/ijerph16091636

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, 2799, 123838.

Wei, H., & Zhou, Y. (2022). Study on the Impact of  
FDI on Environmental Pollution in Hebei Province. 
Frontiers in Business, Economics and Management, 6(3), 17–
21. https://doi.org/10.54097/fbem.v6i3.3111

Wen, H., & Dai, J. (2020). Trade openness, environmental 
regulation, and human capital in China: based on 
ARDL cointegration and Granger causality analysis. 
Environmental Science and Pollution Research, 27(2), 1789–
1799.

Yakubu, I. N., & Musah, A. (2022). FDI and Environmental 
Sustainability Nexus: Testing the Pollution Haven 
Hypothesis in the Presence of  Regulatory Quality.
MPRA Paper No. 115410. In International Journal of  
Advanced Research in Economics and Finance. https://doi.
org/10.55057/ijaref.2022.4.3.25

Yasin, I., Ahmad, N., & Chaudhary, M. (2020). 
Catechizing the environmental-impression of  
urbanization, fnancial development, and political 
institutions: a circumstance of  ecological footprints 
in 110 developed and less-developed countries. Soc 
Indic Res, 147, 621–649. https://doi.org/https://doi.
org/10.1007/s11205-019-02163-3

Yilanci, V., Bozoklu, S., & Gorus, M. S. (2020). Are 
BRICS countries pollution havens? Evidence from 
a bootstrap ARDL bounds testing approach with 
a Fourier function. Sustainable Cities and Society, 55, 
102035.

Yue, D., Jun, Z., & Muhammad, Nasir Malik Kangyin, 
D. (2021). Assessing the impact of  trade openness 
on CO2 emissions: Evidence from China-Japan-
ROK FTA countries. Journal of  Environmental 
Management, 296(113241). https://doi.org/https://
doi.org/10.1016/j.jenvman.2021.113241.

Zhang, C., & Zhou, X. (2016). Does foreign direct 
investment lead to lower CO2 emissions? Evidence 
from a regional analysis in China. Renewable and 
Sustainable Energy Reviews, 58, 943–951.

Zhang, Q., Jiang, X., & Ton, D. et al. (2017). Transboundary 
health impacts of  transported global air pollution and 
international trade. Nature, 543, 705–709. https://doi.
org/https://doi.org/10.1038/nature21712

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Table 1: Descriptive Statistics
Variable Obs Mean Std. Dev. Min Max
logco2 46 13.169 0.294 12.616 13.66
logpop 46 20.245 0.365 19.622 20.851
loggdp 46 26.999 0.816 25.617 28.246
logto 46 3.232 0.148 2.876 3.502
fdip 46 1.438 .987 .069 3.854

Source: Authors’ computations

Table 2: Pairwise correlations 
Variables LogCO2 logPOP logGDP logTO FDI
LogCO2 1.000
logPOP 0.949 1.000
logGDP 0.963 0.653 1.000
logTO 0.154 0.253 0.230 1.000
FDI 0.704 0.575 0.517 0.516 1.000

Source: Authors’ computations

Table 3: ADF unit root test
Variables Test statistics at levels P-Value Test statistics at first difference p-vales Decision
CO2 -0.783 0.8241 -4.353  0.0004 1(1)
logPOP -1.568  0.4998 -3.765 0.0033 1(1)
logGDP -1.093 0.7180 -3.661  0.0047 1(1)
logTO -2.511 0.1128 -6.114 0.0000 1(1)
FDI -1.161 0.6903 -5.432 0.0000 1(1)
logFDI -0.625 0.8653 -6.814 0.0000 1(1)

Source: Authors’ computations

APPENDIXES

Table 4: Zavot-Andrew unit root test
Variables Test statistics at levels Test statistics at first difference Break date Decision
CO2 -2.859 -6.459 1995 1(1)
logPOP -3.523 -2.603 1996 1(1)
logGDP -3.083 -4.578 1984 1(1)
logTO -3.897 -6.359 2001 1(1)
FDI -3.357 -6.374 2000 1(1) 
logFDI -4.252 -11.065 1998 1(1)

Note CV @ 5%=-4.42, Source: Authors’ computations

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