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American Journal of  Environmental
Economics (AJEE) 

Foreign Direct Investment, Institutional Quality, and Environmental Pollution in 
Selected African Countries

Kehinde John Akomolafe1*

Volume 3 Issue 1, Year 2024
ISSN: 2833-7905 (Online)

DOI: https://doi.org/10.54536/ajee.v3i1.3939
https://journals.e-palli.com/home/index.php/ajee

Article Information ABSTRACT

Received: October 20, 2024

Accepted: November 27, 2024

Published: December 07, 2024

African countries increasingly attract  foreign direct investment as they strive for economic 
growth and development. However, there have been concerns over the rising effects of  FDI 
on pollution. These concerns are rooted in the potential for industrial activities associated 
with FDI to have adverse environmental impacts. Hence, the needs to tackle the problem of  
pollution cannot be overemphasized. One way to do this is by strengthening the quality of  
African institutions. This study was conducted to examine the effects of  institutional quality 
and FDI on environmental pollution in selected African countries. Environmental pollution 
was measured using  CO2 emission.The result of  the PCSE estimator showed that institutional 
quality had negative effects on environmental pollution, but the effect of  FDI was positive 
and insignificant. Also, the effect of  moderating effect showed that institutional quality has the 
potential to reduce the positive effect of  FDI on environmental pollution. It was recommended 
that African governments should strengthen the quality of  their institutions by strengthening 
environmental governance frameworks. 

Keywords

Africa, Environment, FDI, 
PCSE, PHH, Pollution

1 Department of  Economics, Afe Babaola University, Ado Ekiti, Nigeria
* Corresponding author’s e-mail: akjohn@abuad.edu.ng

INTRODUCTION
Foreign Direct Investment (FDI) is vital to a country’s 
economic growth. This is especially true in less developed 
countries. It is an important tool for employment creation, 
poverty reduction and economic growth (Crescenzi et 
al., 2022). However, while FDI can provide significant 
benefits to an economy, it is not without possible 
drawbacks. One of  these is environmental pollution. FDI 
can sometimes result in environmental harm, especially 
if  the host country’s environmental rules are weak.This 
may allow multinational corporations (MNCs) to engage 
in environmentally detrimental practices than they might 
have done in countries with stronger environmental 
laws (Chirilus & Costea, 2023). This is referred to as the 
pollution haven hypothesis (Raihan, 2023).
It implies that multinational corporations, particularly 
those in pollution-intensive industries, may invest in 
or relocate production to nations with less stringent 
environmental regulations to avoid the higher costs of  
compliance in their home countries. This frequently 
results in developing countries with weak environmental 
enforcement attracting global corporations eager to 
cut regulatory costs, potentially leading to increasing 
pollution and environmental deterioration in such areas 
(Bashir, 2022; Raihan, 2023).
In recent times, Foreign direct investment is becoming 
more and more popular in African nations as they work 
to build their economies (Adegboye & Okorie, 2023). 
From $2,845 million in 1990, FDI inflows to Africa 
increased to $82,196 million in 2021 (United Nations 
Conference on Trade and Development (UNCTAD), 
2023). However, the growing impact of  foreign direct 
investment on pollution has raised worries. These worries 
stem from the possibility that industrial operations linked 
to foreign direct investment could negatively affect the 

environment. 
Many African countries have less stringent or poorly 
enforced environmental regulations (Baajike et al., 2022). 
This creates a situation where foreign investors are not  
held to high environmental standards, leading to pollution 
concerns. Also, there are concerns that FDI are attracted 
into resource extraction industries, such as mining and 
oil exploration, and these  pose environmental challenges 
(Kimiagari et al., 2023). According to Adegboye and 
Okorie (2023), the African region had the highest return 
on investment, at almost 11%, compared to Asia’s 9.1%, 
Latin America and the Caribbean’s 8.9%, and the global 
average of  7.1%. One of  the reasons for this is because 
most of  the FDI flows to Africa are attracted to the 
resource sector with the intention of  exploiting Africa’s 
resources to their advantage (Geda & Yimer, 2023). This 
is more so because of  the weak regulation and compliance 
in African countries.
Ironically, as FDI inflows in Africa continue to rise, CO2 
emissions are also on the increase. For instance, Carbon 
dioxide (CO2) emissions from industrial processes in Sub-
Saharan Africa increased from 23.6232 metric tons (MT) 
in 71.6857 metric tons (MT) in 2022. Similarly, Carbon 
dioxide (CO2) emissions from Industrial combustion 
in Sub-Saharan Africa increased from 58.9712 metric 
tons (MT) in 91.7708 metric tons (MT) in 2022 (World 
Bank, 2023). Air pollution is one of  today’s most serious 
environmental issues, posing a risk to human health. 
Every year, air pollution claims the lives of  approximately 
1.1 million people throughout Africa (Capitanio et al., 
2024). Similarly, many African countries have annual 
mean pollution concentrations exceeding the World 
Health Organization guideline (Fisher et al., 2021). 
Fisher et al. (2021) also argued that in 2019, air pollution-
related illness and mortality cost Ethiopia $3.02 billion, 



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On the other hands, there have been several studies 
that found a negative effect of  FDI on environmental 
pollution. Studies such as (Balsalobre-Lorente  et al., 2019; 
Demena  & Afesorgbor, 2020;  Hao et al., 2020; Jiang et al., 
2018; Kisswani & Zaitouni, 2023; Mert & Caglar, 2020; 
Nejati & Taleghani, 2022; Nguyen-Thanh et al., 2022; 
Tayyar, 2022) found evidence that FDI reduces pollution. 
The studies discovered that foreign direct investment can 
help to reduce pollution by encouraging the use of  cleaner 
technologies and sustainable practices. They argue that 
FDI encourages host countries to raise their standards 
in response to pressures from multinational corporations, 
which frequently bring advanced, environmentally 
friendly technologies. They conclude that FDI reduces 
pollution through technology diffusion,especially in high-
regulation economies where environmental norms are 
actively enforced.
Tayyar (2022), for instance, found that foreign direct 
investment (FDI) reduces pollution by promoting 
technology transfer and raising environmental awareness 
in host nations, and that this effect is stronger in countries 
with strict environmental rules. Nguyen-Thanh et al. 
(2022) concluded that FDI helped to reduce pollution 
by boosting industrial modernization and bringing better 
environmental standards and cleaner manufacturing 
processes. According to Nejati and Taleghani (2022), FDI 
is related with a decrease in air pollutants in developing 
nations due to the adoption of  cleaner technology and 
practices, which is especially beneficial in industries with 
historically high emissions.
While numerous studies imply that FDI alone leads 
to increased pollution, the interplay of  FDI and 
environmental control can mitigate this effect. For 
instance, Fahad et al. (2022) investigated the conditional 
impacts of  FDI on environmental quality and found 
that stricter environmental regulations in host nations 
increase FDI’s positive influence on pollution reduction. 
It suggested that when laws are strong, FDI firms are 
more inclined to use cleaner technology to meet local 
norms. According to Fu et al. (2024), the environmental 
impact of  FDI varies with regulatory strictness. FDI in 
countries with strong environmental policies leads to 
fewer emissions and improved sustainability practices. In 
contrast, in less-regulated countries, FDI may contribute 
to higher levels of  pollution, implying that regulatory 
quality is an important driver. Qian-qian et al. (2019) 
concluded  that FDI firms alter their practices in response 
to the host country’s regulatory framework. 
The study argued that in areas with strict environmental 
legislation, FDI promotes eco-friendly behaviors, 
whereas in less-regulated environments, investment can 
occasionally result in “pollution havens.” Also, Xie and 
Zhang (2024) revealed that the environmental impact of  
FDI depends on regulatory enforcement. In countries 
with strict environmental legislation, FDI brings in eco-
friendly technology and practices, but in countries with 
laxer restrictions, it may contribute to increased pollution 
due to inadequate control. According to Yang et al. (2021), 

Ghana $1.63 billion,and Rwanda $349 million. Hence, 
the needs to tackle the problem of  pollution cannot be 
overemphasized. One way to do this is by strengthening 
the quality of  African institutions. Stronger institutions 
may create, implement, and enforce more stringent 
environmental legislation to reduce emissions and 
pollutants. They can move the focus from pollution-
intensive sectors to those that support long-term 
growth by actively pushing FDI in renewable energy and 
environmentally friendly industries. This study therefore 
examines the moderating roles of  institutional quality 
in the relationship between FDI and environmental 
pollution in selected African countries.

LITERATURE REVIEW 
Pollution Haven Hypothesis and Pollution Halo 
Hypothesis
Pollution Haven Hypothesis and Pollution Halo 
Hypothesis are two important theories that have been 
used to explain FDI-Pollution relationships in the 
literature. The Pollution Haven Hypothesis argues that 
foreign direct investment may increase pollution in the 
host nations, particularly in countries with inadequate 
environmental standards (Akbulut & Yereli, 2023). This 
implies that multinational corporations from nations 
with stronger environmental rules transfer their polluting 
activities to countries with looser regulations, resulting in 
“pollution havens” (Uche et al., 2024). This move enables 
major companies to reduce their manufacturing costs 
while increasing pollution levels in the host countries.
The Pollution Halo Hypothesis, on the other hand, 
contends that FDI can improve environmental quality in 
host countries.According to Xu et al., (2021), multinational 
firms bring cleaner technology, sophisticated managerial 
methods, and higher environmental standards from 
their home countries to the host country. This transfer 
can have a “halo” effect, in which local businesses and 
industries adopt more sustainable practices, boosting 
overall environmental outcomes (Duan & Jiang, 2021; 
Padhan & Bhat, 2024).

Review of  Past Studies
Over the years, the relationship between FDI and 
pollution has been widely examined. For instance, studies 
such as (Abbas et al., 2023; Abdo et al., 2020; Adeel-
Farooq et al., 2021; Achuo & Ojong, 2024; Assamoi et al., 
2020; Boamah et al., 2023; Gharnit et al., 2019; Khan & 
Ozturk, 2020; Nadeem et al., 2020; Ren et al., 2014;  Shao 
et al., 2019; Quang, 2023; Wang et al., 2024; Zheng et al., 
2024 ) found evidence that FDI increases environmental 
pollution. They noted that greater FDI inflow is 
frequently associated with higher levels of  industrial 
emissions and air pollution. They ascribe this to weaker 
environmental rules in these countries, which make 
them appealing destinations for corporations looking 
for cheaper compliance costs. They confirmed that lax 
environmental regulations allow for polluting industry 
expansion via foreign investment. 



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the impact of  foreign direct investment on environmental 
results is mediated by local regulatory norms. The study 
highlighted that in countries with strict environmental 
regulations, FDI has a positive impact on environmental 
quality, whereas in less-regulated environment, FDI may 
exploit low standards, causing environmental harm. 
This study contributes to the debate by building on 
these findings, notably by investigating the impact of  
institutional quality in the FDI-pollution link. By adding 
institutional elements, the study gives a more in-depth 
knowledge of  how governance systems, regulatory 
efficacy, and institutional strength in host nations may 
influence FDI environmental consequences.

MATERIALS AND METHODS 
Two models were used for the analysis. The  first was used 
to examine  the effects of  FDI  and institutional quality 
on environmental pollution, while the second model was 
used to investigate how institutional quality moderate the 
effect of  FDI on environmental pollution in the selected 
African countries.

Model One
LOG_CO2it= δ0+δ1 LOGFDIit+ δ2 IQSTQUALit+ δ3 
LOG_CREDITit+ δ4LOG_CPIit+ δ_5 LOGEXCHAit+ 
ϵit                                                                                                              ………..(3.1)

Model  Two
LOG_CO2it= δ0 + δ1 LOGFDIit+ δ2 IQSTQUALit+ δ3 
INSTFDIit  + δ4LOG_CREDITit + δ5LOG_CPIit + δ6 
LOGEXCHA_it + ϵit                                                         ………..(3.2)
Where LOG_CO2 is the log of  CO2 emission, LOGFDI  
is the log of  FDI inflows, IQSTQUAL is institutional 
quality index, LOG_CREDIT is the log of  credit to the 
private sector which was used as the proxy for financial 
development, LOG_CPI is the log of  consumer price 
index, and LOGEXCHA is the log of  exchange rate, and 
INSTFDI is moderating variable. INSTFDI =  LOGFDI 
* IQSTQUAL
Institutional quality  was measured as the composite  
index from six governance indicators which are voice 

and accountability, political stability and absence of  
violence/terrorism, government effectiveness, regulatory 
quality, and rule of  law.  Environmental Pollution was 
measured as CO2 emissions (metric tons per capita), FDI 
Inflows:was measured using FDI inflows as percentage 
of  GDP, Domestic Credit  was measured as the domestic 
credit to the private sector, Consumer Price Index: was 
used to measure inflation, Exchange Rate was  measured 
as the Official exchange rate (LCU per US$, period 
average).
This study was based on panel data consisting of  time 
series data from  2002 and 2022. For the cross-sectional 
data, three countries were chosen from each region in 
Africa. Hence, the countries that were considered are 
Tunisia, Egypt,  and  Morocco,  from  North Africa, Benin, 
Nigeria, and Senegal from Western Africa, Cameroon, 
Chad, and Gabon, from Central Africa,  Kenya, Rwanda 
and Mauritania from  Eastern Africa, and  Botswana,  
Namibia,  Namibia, and South Africa.
Using the Variance Inflation Factor, the study 
begins by checking for multicollinearity. The test for 
heteroskdesticity was followed using modified wald test 
The  Pesaran Cross-Sectional Dependence test  was 
used to test for cross-sectional dependence.  The panel 
unit root tested using  Cross-Sectional Augmented ADF 
(CADF) test. The Panel co-integration test was done using  
the  Pedroni co-integration test,  while Panels Corrected 
Standard Errors was used as the primary estimator.

RESULTS AND DISCUSSION
Testing for the Multicollinearity
The variance inflation factor (VIF) and tolerance factor 
(TF) are measures used to assess multicollinearity among 
the predictor variables in  models. Table 1 shows that 
the variance inflation factor is low with the highest value 
of  2.21 while the tolerance factor has the lowest value 
of  0.45 in the first model. The second model has the 
highest value of  2.29 and lowest tolerance factor  value 
of  0.43. Given that the VIF values are below 5, there is 
no significant multicollinearity issue among the predictor 
variables in the two models (Shrestha, 2020).

Table 1: Results of  the VIF Test of  Multicollinearity
Model One Model Two

Variable VIF TF VIF TF 
LOG_CREDIT 2.21 0.451662 2.29 0.437020
LO_GEXCHA 1.83 0.545362 1.84 0.542995
IQSTQUAL 1.76 0.569608 2.56 0.391313
LOG_CPI 1.18 0.844904 1.18 0.844742
LOG_FDI 1.03 0.974278 1.12 0.890316
INSTFDI 2.16 0.462577
Source: Computed by the Author

Testing For Serial Correlation in the Models
Table 2 shows that the Woodrige Test of  Serial Correlation 
has probability values that is less than 5% in both models, 
implying that there is evidence of  serial correlation in the 

residuals. Because the probability value is less than 5%, 
the null hypothesis of  no serial correlation is rejected in 
the two models. 



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Table 2 : Woodrige Test of  Serial Correlation
Model One Model Two

F(,20) 346.156 355.181
Prob > F  0.0000  0.0000
Source: Computed by the Author

Table 3: Result of  the Modified Wald Test for 
Groupwise Heteroskedasticity

Model One Model Two
F(,20) 346.156 355.181
Prob > F  0.0000  0.0000
Source: Computed by the Author 

Table 4: Result of  the Pesaran and Yamagata.Test
Model One Model Two

       Delta                      13.983 16.934 12.376  15.514 
Prob Value 0.000  0.000 0.000  0.000
Source: Computed by the Author 

Table 5: Results of   the Cross-Sectional Dependence
Model One Model Two

Variable CD-test p-value CD-test p-value
LOG_CO2 + 47.372 0.000 47.372 0.000
IQSTQUAL + 3.096 0.002 3.096 0.002
LOGFDI + 4.014 0.000 4.014 0.000

Table 6: The Result of  the CADF Unit Root
Without
Difference 

With Difference 

Variable  t-bar P-value  t-bar  P-value
LOG_CO2 -2.595 0.080 -2.735 0.018
IQSTQUAL  -2.510 0.162 -3.422 0.000
LOGFDI  -2.334 0.453 -3.588 0.000
L O G _
CREDIT

-2.585 0.088  -3.780 0.000

LOG_CPI -2.070  0.067 -2.780 0.000
LOGEXCHA -2.489  0.189 -2.764 0.013
INSTFDI -2.618 0.064 -3.508 0.000
Source: Computed by the Author 

Table 7: The Result of  Pedroni Co-integration Test
Model One Model Two

Statistics Statistic P-value Statistic P-value
Modified
Phillips -Perr 
-on 

4.9890 0.0000 6.0836 0.0000

Phillips-
Perron

-3.0077 0.0013 -2.0945 0.0181

Augmented
Dickey-Fuller

-2.4268 0.0076 -1.2297 0.1094

Source: Computed by the Author 

Testing For Heteroskedasticity in the Models
Table 3 reveals that the Modified Wald test for groupwise 
heteroskedasticity for the two models yields probability 
value of  less than 1%. It provides significant evidence 
for rejecting the null hypothesis of  no groupwise 
heteroskedasticity. It implies that the error variances 
in regression models are not uniform across groups or 
clusters. This breach of  the homoscedasticity assumption 
may result in biased coefficient estimations and wasteful 
standard errors. As a result, our work solved this issue 
by determining the optimal estimator that is robust to 
heteroskedasticity.

Slope Homogeneity Test
Table 4 shows that the probability value for this test is  less 
than 1% for the two models. It suggests strong evidence 
against the null hypothesis of  slope homogeneity, 
indicating that the coefficients vary significantly across 
different units. 

Testing for Cross-Sectional Dependence
The Pesaran cross-sectional dependence test findings 
demonstrate that most of  the variables in the two models 
have probability values less than 1%, indicating strong 
evidence against the null hypothesis of  no cross-sectional 
dependence. Cross-sectional dependence can cause 
coefficient estimates to be biased and standard errors to 
be erroneous in panel data models. This study addressed 
this challenge by considering the  estimator that is robust 
to cross-sectional dependence.

INSTFDI -.175 0.861
L O G _
CREDIT

+ 35.397 0.000 35.397 0.000

LOG_CPI + 65.949 0.000 65.949 0.000
LOGEXCHA + 31.22 0.000 31.22 0.000
Source: Computed by the Author 

Panel Unit Root Test
The CADF test results provide insight into the stationarity 
of  the variables in the panel data model. The results 
demonstrate that the CADF probability values were 
greater than 5% when the variables were not diferrenced. 
When they were differenced ones, the probabilities 
were less than 5%. This means that when the variables 
were not diferrenced, they were non-stationary, i.e. they 
showed trends or had unit roots. After differencing, the 
variables became stationary, which means they no longer 
showed trends or unit roots. 

Testing for the Co-integration in the Models
Table 7 displays the Pedroni Co-integration test results 
for the models. The test provides three statistics: modified 
Phillips-Perron, Phillips-Perron, and augmented Dickey-
Fuller. All the three statistics have probability values that 
are less than 1%. This provides extremely strong evidence 
against the null hypothesis of  no co-integration in the 
two models. This implies a long-run relationship between 
the variables in the model.



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Results of  The Panels Corrected Standard Errors
Table 8 shows that in model one, there is a negative 
relationship between institutional quality and CO2 
emissions. This shows that higher institutional quality 
leads to lower CO2 emissions. This implies that improving 
institutional quality, such as upgrading governance 
structures, strengthening regulatory frameworks, and 
fostering openness and accountability, may contribute 
to minimizing environmental deterioration. Similarly, 
there was a positive relationship between FDI and CO2 
emissions, but it is not statistically significant at the 
5% level. This shows that an increase in foreign direct 
investment is connected with higher levels of  CO2 
emissions. The lack of  significance suggests that although 
FDI has the potential to increase pollution in Africa, the 
effect is relatively weak. This may imply that FDI has not 
grown enough to significantly contributes to pollution in 
Africa. 
In model two, there is also a negative relationship between 
institutional quality and CO2 emission.  This suggests 

Table 8: Results Of  The Panels Corrected Standard Errors
Coef. P>z. Coef. P>z.

IQSTQUAL -.0897178 0.000 -.1774559 0.000
LOGFDI .0024163 0.655 .0021982 0.816
INSTFDI -.0012278 0.755
LOG_CREDIT .181255 0.001 .2327117 0.004
LOG_CPI .3883593 0.000 .50382 0.000
LOGEXCHA -.4233512 0.000 -.5277795 0.000
_cons 8.53751 0.000 220.69 0.0000
Wald chi2(5) 128.64 220.69
Prob > chi2 0.0000 0.0000
Source: Computed by the Author

that higher institutional quality is associated with lower 
levels of  CO2 emissions. Similarly,  a positive relationship 
between Foreign Direct Investment (FDI) and CO2 
emissions was found, but the effect was not statistically 
significant at the 5% level. The moderating effect of  
institutional quality with FDI on CO2 is negative, but it 
is not statistically significant. The negative coefficient 
suggests that improved institutional quality may dampen 
the effect of  FDI on CO2 emissions.Stronger institutional 
quality, as evidenced by good governance and regulatory 
frameworks, may reduce the environmental effect of  
FDI by encouraging cleaner technologies, sustainable 
behaviours, and compliance with environmental 
standards. However, the lack of  significance in the 
moderating effect of  FDI on the relationship between 
FDI and CO2 emissions may indicate that FDI levels are 
too low to have a noticeable effect on CO2 emissions.It 
may also imply that institutional quality must improve to 
a certain level for it to be able to have a significant effect 
on the way FDI affects pollution in Africa. 

Summary and Conclusion
This study was conducted to examine the effects 
of   FDI  and institutional quality on environmental 
pollution in selected African countries. The findings 
showed that institutional quality had negative effects on 
environmental pollution, while the effect of  FDI was 
positive but insignificant. Also, the effect of  moderating 
effect showed that moderating FDI with institutional 
quality reduced pollution, but the effect was insignificant. 
The study concludes that FDI has the potential to 
increase pollution in Africa. Also, institutional quality 
has the potential to mitigate the potential effects of  FDI 
on environmental pollution in Africa. This implies that 
higher levels of  institutional quality may mitigate the CO2 
emissions associated with FDI activities through stricter 
environmental regulations. It is therefore recommended 
that African governments should strengthen institutional 
quality by strengthening environmental governance 
frameworks, regulatory institutions, and enforcement 
mechanisms.

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