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American Journal of  Environmental
Economics (AJEE) 

Impact of  Environmental Degradation on Economic Growth: Testing the Environmental 
Kuznets Curve in Nigeria

Aiyedogbon John O.1*, Ogwuche David Dauda1, Nanbal Joel Zuhumben1

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

DOI: https://doi.org/10.54536/ajee.v3i1.3243
https://journals.e-palli.com/home/index.php/ajee

Article Information ABSTRACT

Received: August 14, 2024

Accepted: September 15, 2024

Published: September 19, 2024

Looking at the contribution of  economic activity in the country, it is seen to add robustness 
to growth and development, but in contrast give birth to greenhouse gases (GHGs), such 
as CO2 CO2 which is also called carbon dioxid, CH4 which is also called methane, and N2O 
represent nitrous oxide. The factors influencing Greenhouse gas emissions include rising 
of  population, upward movement of  per capita output and consumption, infrastructural 
development made about infrastructures, human character, and innovation. However, if  
mitigation efforts are not enough, climate change would most likely lead to a slowdown 
the upward movement. Thus, the study examined the relationship between economic 
growth and environmental degradation using the Autoregressive Distributed Lag model and 
examined the impact of  environmental degradation on economic growth in Nigeria from 
1990 to 2022 using that lens. Based on the estimated ARDL regression result, environmental 
degradation as determined by carbon dioxide emissions (kt) and economic growth are 
negatively correlated. The negative indication indicated that economic growth will decline 
by roughly -0.455% and -0.893% over the medium and long terms, respectively, as carbon 
emissions rise. The findings indicated that there is validity to Nigeria’s Environmental 
Kuznets Curve, which demonstrates how environmental deterioration impedes economic 
progress. Similarly, a positive indication indicating a one-period lag in carbon emissions 
showed that, in the short term, economic growth increases by around 0.948% in tandem 
with rising carbon emissions. Thus, the paper illustrated an inverse U-shaped interaction 
between environmental degradation and economic growth, with healthy economy initially 
related with increased emissions. However, the majority of  the control variable estimates 
deviate from theoretical expectations. On the other hand, a positive and statistically 
significant one-period lagged coefficient of  trade openness suggests that trade openness 
looks to support and encourage economic growth in the short run. Similarly, during the 
period under consideration in Nigeria, the short-run economic growth is positively and 
strongly correlated with the one-period lagged coefficient of  gross fixed capital formation. 
The report suggested boosting energy efficiency in the world’s energy mix in order to lower 
greenhouse gas emissions, which are the main contributors to climate change. Therefore, 
in order to prevent the development of  closed-form relationships that could result in a 
slowdown in economic growth, Nigerian government agencies that are responsible for 
implementing national and international environmental rules should proceed with caution.

Keywords

ARDL, Carbon Emission, 
Climate Change, Economic 
Growth, Nigeria

1 Department of  Economics, Faculty of  Social Sciences, Bingham University, Karu, Nigeria
* Corresponding author’s e-mail: johnaiyedogbon@gmail.com

INTRODUCTION
The global economy has risen significantly over the 
past century due to a number of  factors, including 
globalization, industry booms, innovation, significant 
technology developments, unfettered international trade 
at all levels and in all sectors, and the overall state of  
the economy. But the growth in the economy has come 
at the expense of  the environment, causing pollution 
to increase and environmental deterioration to worsen 
(Abbasi et al., 2021). 
In contrast, the globe Economic Forum (WEF) claims 
that environmental hazards represent the five most likely 
long-term global risks and that they account for four 
of  the top five challenges facing the globe now (WEF, 
2021). It’s believed that environmental issues brought on 
by environmental deterioration pose the greatest danger 
to the SDGs (sustainable development objectives). This 
is accurate given that environmental risks affect all 
businesses, people, and society (SRI, 2021). There is no 

worldwide immunization against this risk, and no one 
can be immune to it (WEF, 2021). These risks have been 
mostly attributed to carbon dioxide emissions, the main 
cause of  climate change.
While looking at the activities in economy plays an 
important role development and progress, but also 
brought about a rise in the emissions of  greenhouse gases 
(GHGs), such as methane (CH4), carbon dioxide (CO2), 
and nitrous oxide (N2O). Mohammed et al. (2020) and 
Mohammed et al. (2021) claimed that these greenhouse 
gases have transformed the environment, exacerbated 
climate change, and reduced the health ecosystems. A 
few factors that influence greenhouse gas emissions 
are per capita consumption, population increase and 
output growth, infrastructure decisions, human behavior, 
innovation, and advanced technology (Bekun et al., 2021; 
Fatai et al., 2021 and Cai, et al., 2018). If  we don’t take 
strong enough actions to address climate change, our 
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development are likely to suffer. (Yusuf  et al., 2020).
The growing concerns about environmental degradation 
and climate change have received a lot of  attention in the 
literature and policy debates. The majority of  the focus 
in the economics literature has been on the relationship 
between environmental degradation and economic health 
within the procedure of  the Environmental Kuznets Curve 
idea. The EKC hypothesis has dominated the research 
regarding the relationship between economic growth 
and environmental degradation since the early 1990s. 
The relationship between environmental degradation 
and economic activity is an inverted U, according to the 
EKC theory. High levels of  pollution and environmental 
degradation are typical of  early economic growth; 
however, this pattern reverses once a certain per capita 
income is reached, and at high income levels, economic 
growth is linked to environmental improvements (Halkos 
& Managi, 2017; Halkos & Managi, 2016 and Grossman 
& Krueger, 1991).
Real evidence has contradicted the EKC’s assertion 
that there is an inverse U-shaped relationship between 
environmental degradation and economic activity. Both 
inside and outside of  Nigeria, numerous research has 
backed the EKC (Grossman & Krueger, 1995; Sisay & 
Balázs, 2019; Hammed et al. 2020; Maneejuk et al., 2020; 
Abdulkarim, 2023; Acheampong & Opoku, 2023 and 
Mohammed et al., 2024), but some have not (Aye & Edoja, 
2017; Adu & Denkyirah, 2018; and Raihan et al. (2022). 
While several research, including Yan et al. (2022), Cetin 
(2018), and Sica (2014), uncovered contradictory findings 
about the EKC hypothesis’s underlying assumptions for 
some nations. Thus, the study examines the relationship 
between environmental degradation and economic 
growth as well as how environmental deterioration propels 
economic growth in Nigeria. Furthermore, because there 
are so few empirical studies that particularly address 
the relationships between environmental sustainability 
and economic growth in Nigeria, a detailed research of  
whether the country falls into the Environmental Kuznets 
Curve is necessary.
There are several ways that environmental degradation 
can affect economic growth. Understanding these 
connections can help policymakers create comprehensive 
policies that balance environmental sustainability with 
economic development. Identifying how environmental 
degradation impacts economic health is essential. This 
includes recognizing that the effects can be both direct 
and indirect, allowing for more effective strategies to 
be developed. Trade openness, for instance, can have a 
significant impact on an economy since it can promote 
know-how and boost competitiveness in both domestic 
and international markets due to its productivity and 
competitive advantage (Chang et al., 2009). According 
to the majority of  research that have been published 
recently, trade openness has a significant role in explaining 
economic growth (Gershon et al., 2024; Acheampong & 
Opoku, 2023 and Mesagan, 2015). 
In a similar vein, foreign direct investment is thought 

to be an important channel via which environmental 
deterioration may influence economic expansion. 
According to the Pollution Haven Hypothesis (Eskeland 
& Harrison, 2003; Javorcik and Wei, 2003; Levinson, 
2020), developing nations are more likely to have 
polluted or degraded environments because of  their open 
economies. According to this theory, polluting companies 
in advanced nations with tight environmental rules will 
transfer to developing countries through foreign direct 
investment because of  the lax environmental restrictions 
in developing countries. Thus, developing nations turn 
into pollution havens. As a result, developing nations will 
import pollution from developed nations. Empirically, 
Opoku et al. (2022) demonstrated that environmental 
deterioration is a major factor in deciding foreign direct 
investment entry into emerging nations. Environmental 
impacts can also result from capital investment through 
foreign direct investment in nations with lax regulatory 
frameworks. Consequently, it is sometimes recommended 
that developing nations use gross fixed capital formation 
as a stand-in for capital investment in order to boost 
their long-term development rates. Accordingly, capital 
investment through inflows of  foreign direct investment 
is a major factor driving economic growth (Gershon et al., 
2024; Acheampong & Opoku, 2023 and Mesagan, 2015). 
According to the ways that environmental deterioration 
can impact economic growth, the following questions are 
attempted to be addressed in this paper:

Research Questions
The following research questions guided the paper:

i. How does carbon dioxide emissions impact on 
economic growth?

ii. What impact does trade openness have on economic 
growth in Nigeria?

iii. To what extent does foreign direct investment affect 
economic growth in Nigeria?

iv. What is the impact of  gross fixed capital formation 
on economic growth in Nigeria?
The rest of  this paper is divided into five sections. The 
next section provides a summary of  the literature, which 
is divided into theoretical interpretations, empirical 
reviews, and the concepts of  economic health or growth 
and carbon emissions. The economic framework and the 
empirical discoveries upon which data are presented in 
sections three and four while in addition to section five, 
conclusions and recommendations was concluded.

MATERIALS AND METHODS 
Conceptual Review
In the words of  Palmer (2012) who is a researcher 
expanded on the concept of  economic growth as the 
broadening of  a nation’s production capacity in all areas, 
which extend to economic production of  more goods 
and services for the nation and beyound. An increase in 
Real Gross Domestic Product (RGDP) signifies a rise in 
national production, income, and expenditure, indicating 
economic growth. This upward movement reflects an 



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expansion in the economy. The Gross Domestic Product 
(GDP) serves as a measure of  this economic expansion, 
while the GDP growth rate illustrates the pace at which 
the economy is developing. (Picardo, 2020).
Employment growth, lower taxes for public spending, 
debt reduction, longer life expectancies, higher 
investment, and advancements in R&D are all advantages 
of  economic growth. Slow economic growth, on the 
other hand, has negative effects on public services. 
These effects include inadequate health and educational 
facilities, low rates of  savings and investment, high rates 
of  poverty and unemployment, a rise in the national debt, 
and lower tax revenue than anticipated (Picardo, 2020).
Environmental degradation occurs when human 
activities result in harmful or unhealthy conditions for 
the environment. This can lead to the destruction of  
ecosystems, depletion of  natural resources, contamination 
of  the air and water, degradation of  soil, and the 
extinction of  plant and animal species. This process 
diminishes the quality and integrity of  essential resources 
like soil, water, and air, ultimately impacting the overall 
health of  biological ecosystems. Air, water, and soil are 
especially vulnerable to damage from excessive use and 
harmful human actions. In this context, carbon dioxide 
(CO2) emissions serve as a metric for environmental 
degradation, helping to assess whether economic 
growth can mitigate environmental decline under the 
Environmental Kuznets Curve (EKC) hypothesis in 
Nigeria. Greenhouse gas emissions, particularly CO2, are 
considered major contributors to global warming and 
environmental degradation, primarily resulting from the 
burning of  fossil fuels and cement production. Among 
them are carbon dioxide emissions from gas flaring and 
the consumption of  solid, liquid, and gas fuels (Climate 
Watch Historical GHG Emissions, 2023). 
Emissions of  carbon dioxide, which are mostly leftovers 
from the production and use of  energy, make up the 
majority of  greenhouse gases linked to global warming. 
Emissions of  anthropogenic carbon dioxide are mostly 
caused by burning fossil fuels and producing cement. 
When fossil fuels are burned, they produce varying 
amounts of  carbon dioxide at the same energy level. 
For example, coal releases almost twice as much carbon 
dioxide as natural gas, while oil releases roughly 50% 
more. For every metric ton of  cement produced, the 
cement manufacturing process emits almost half  a metric 
ton of  carbon dioxide. Emissions from land use, such 
as deforestation, are not included in the data on carbon 
dioxide emissions, which include gasses from the burning 
of  fossil fuels and the manufacturing of  cement. The kt 
(kiloton) unit of  measurement is used. Elemental carbon 
is a common unit of  measurement and reporting for 
carbon dioxide emissions. They were multiplied by 3.667, 
which is the ratio of  carbon mass to carbon dioxide mass, 
to get the actual mass of  carbon dioxide.

Empirical Review
Gershon et al. (2024) while studying the interactive effect 
of  energy consumption and economic growth on carbon 

emissions for seventeen selected African countries using 
static panel estimation techniques using annual data from 
2000 to 2017 found that while upward movement of  
energy consumption has an inverse or negative impact on 
carbon emissions, it has a beneficial impact on economic 
growth. In contrast energy used has a greater effect on 
economic broadening than it does on the environment. 
The report made the case for giving economic broadening 
and energy efficiency as the most important in order to 
significantly lessen the damaging impact of  energy use on 
the environment.
Using the environmental Kuznets curve (EKC), 
Mohammed et al. (2024) evaluated the effects of  policies, 
GDP, population structure, energy consumption, and 
carbon dioxide (CO2) emissions on the environment 
within the EU. The discovery demonstrated from 1990 
to 2019, the European Union-27’s energy consumption 
went up by þ1.18 million tonnes of  oil equivalent (Mtoe) 
annually (p < 0.05), but CO2 emissions decreased by 
twenty-four point two, five million tonnes (Mt) annually. 
Latvia had the lowest yearly CO2 emissions (0.087 
Mt CO2), whereas Germany had the biggest decrease 
(7.52 Mt CO2). The empirical environmental Kuznets 
curve study showed an inverted U-shaped relationship 
between gross domestic product and CO2 emissions in 
the European Union -27. Most importantly, a 1% rise in 
gross domestic product leads to a 0.705% rise in carbon 
emissions, but with time, a 1% rise in GDP2 results in a 
0.062% reduction in environmental pollution (p < 0.01). 
These findings suggested that economic development 
in the EU has progressed to the point where economic 
growth directly affects environmental benefits which 
further provided insight into how well environmental 
policies in the twenty-seven member states of  the EU 
prevent degradation and encourage green growth overall.
The question of  whether environmental degradation 
is linked to economic growth is investigated by 
Acheampong and Opoku (2023). In a global study 
involving 140 countries from 1980 to 2021, researchers 
used the two-step dynamic system generalized method 
of  moments to control for endogeneity. The results 
showed that environmental degradation negatively 
impacts economic growth. Additionally, the research 
uncovered an inverse U-shaped relationship between 
emissions and economic growth, indicating that initially, 
as the economy grows, emissions increase, but after 
a certain point, further growth leads to a decrease 
in emissions. Conversely, when looking at ecological 
footprint measures of  environmental deterioration, the 
relationship with economic growth followed a U-shaped 
pattern, suggesting that after reaching a certain level of  
economic growth, environmental impact increases again. 
The research recommended reducing greenhouse gas 
emissions by boosting energy efficiency in the world’s 
energy mix. 
Abubakar and Abdullahi (2022) explored the impact 
of  carbon dioxide emissions on economic growth 
and examined whether the relationship between 
CO2 emissions and economic health is influenced by 



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financial development in Nigeria, the most populous 
country in Africa, from 1980 to 2020. They employed 
the Autoregressive Distributed Lag (ARDL) estimation 
method to conduct their analysis. The results of  the 
cointegration boundary test revealed a long-term 
relationship among CO2 emissions, financial institutions, 
economic health, and energy consumption. Their 
empirical findings indicated that CO2 emissions do not 
have a direct long-term effect on economic growth. 
However, there was clear evidence that CO2 emissions 
and financial development together contribute to 
economic growth over the long term. This suggests that 
CO2 emissions impact economic health significantly 
when financial development is present. To moderate CO2 
emissions from productive activities while maintaining 
efficient productivity across major sectors, the study 
recommended that the government adopt clean modern 
technologies for production and phase out the use of  
fossil fuels such as oil and gas.
Using the Environmental Kuznets Curve (EKC) theory 
as a foundation, Maneejuk et al. (2020) investigated the 
connection between economic progress and environmental 
degradation. The Environmental Kuznets Curve (EKC) 
hypothesis utilizes CO2 emissions as a measure of  
environmental degradation to explore whether economic 
growth can potentially reduce environmental harm. 
Eight major global economic groups, comprising forty-
four countries globally, were studied. The relationship 
between environmental conditions and economic growth 
was analyzed using the kink regression model, which 
helps identify the turning point in this correlation. The 
findings validated the EKC hypothesis within three major 
international economic communities: The European 
Union (EU), the Organization for Economic Co-
operation and Development (OECD), and the Group 
of  Seven (G7). The study recommended that in addition 
to enhancing environmental quality, authorities should 
carefully design and implement specific measures to 
support economic growth in order to achieve sustainable 
development.
To evaluate the usability of  the environmental Kuznets 
curve (EKC) theory in Nigeria, Hammed et al. (2020) 
examined the relationship between environmental 
deterioration, energy use, and economic growth. The 
non-linear autoregressive distributed lag (ARDL) 
method is employed. The findings validated the EKC 
theory’s application in Nigeria, where GDP growth first 
deteriorates environmental quality before subsequently 
improving it. Moreover, it is found that energy usage 
deteriorates environmental quality since, in Nigeria, every 
1% increase in energy use results in a 0.002% rise in CO2. 
The report recommended using newer, lower-emission 
technologies since meeting the country’s energy demands 
is necessary for sustainable growth.
Using the Pooled Mean Group (PMG) method, Sisay 
and Balázs (2019) examined the EKC hypothesis 
for 12 East African nations from 1990 to 2013. The 
results showed that the relationship between per capita 

income and CO2 emissions, a proxy for environmental 
degradation, expands the basic inverted U-shaped curve 
association between economic activity and environmental 
degradation in a bell-shaped manner. As a result, one may 
argue that economic activity in East African countries 
does not produce greenhouse gas emissions. Thus, 
contemporary industrial methods, technical improvement, 
and environmental conservation legislation are required 
to make East African countries’ economic expansion 
efficient in minimizing CO2 emissions.
Cetin (2018) examined the effectiveness of  renewable 
energy sources in lowering CO2 emissions for both 
developed and developing nations between 1990 and 
2011 using a pooled mean group (PMG) estimator. 
The empirical findings of  the study show that while 
emerging markets do not support the EKC hypothesis, 
established markets do. The long-run elasticity results of  
the per capita statistics may also indicate that developed 
and emerging markets have different CO2 emissions. 
The study’s findings did, however, indicate that using 
renewable energy sources will eventually be essential to 
reducing CO2 emissions for both panel groups.
Mesagan (2015) focused on the interaction of  Nigeria’s 
economic health increase and carbon emissions from 
1970 to 2013. The study used an error correction model, 
and the discovering unambiguously illustrated that 
economic health increase has a progressive impact on 
carbon emissions during the initial period and an inverse 
impact during the lagged term. It also revealed that 
capital investment and trade openness have a progressive 
effect on Nigeria’s carbon emissions. The study further 
advised that while reducing gross domestic product (in 
an effort to reduce carbon emissions) may prevent the 
nation’s economic wellbeing, it is more workable to look 
for strategies to motivate green growth in the state. 

Theoretical Framework
This paper’s theoretical framework is based on EKC 
which means Environmental Kuznets Curve, which 
connect CO2 emissions to economic health. Simon 
Kuznets (1955) researched on the relationship between 
economic health and earnings inequality and first agreed 
on the EKC hypothesis. Kuznets (1955) came to the 
finalization that early economic health is connected to an 
increase in income disparity up to a certain point, above 
which inequality is reduced by ongoing economic health 
(Saba, 2023).
Growth in the early part is linked to increasing 
emissions. However, increased productivity becomes 
environmentally maintenance when economies of  scale, 
money, and innovation increase. As a result, an inverse 
or negative U-shaped relationship was seen between 
economic health and environmental deterioration. The 
environmental Kuznets curve (EKC) hypothesis has 
gained worldwide recognition based on these findings.
On a general note, there is a direct correlation between 
pollution and economic health. The connections between 
these 2 (two), however, can be reduced by a number 



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of  measures, such as switching to ecologically friendly 
technology and improving technological advancements 
that ensure overall gains in economic production and, 
more especially, in the reduction of  environmental hazard. 
Notwithstanding the fear surrounding the possibility 
of  limitless substitution or technological advancement, 
there might be hindrances on the degree to which these 
linkages can be further loosened in the future. 
As employed by David Stern in 2003, the functional 
relationship can be expressed as:
(E/P)t= f  (GDP/P)t                (1)
Equation (1) written as thus explicitly below:
                 (2)

Abubakar and Abdullahi (2022), who examined how 
carbon dioxide emissions affected economic growth and 
whether or not that relationship depended on Nigeria’s 
financial development between 1980 and 2020. Abubakar 
and Abdullahi (2022) model is of  the form:
GDP= f (EM, FD, EC + μ)               (5)
Where, GDP represents economic growth, EM is CO2 
emissions, FD represents financial development, EC 
represents energy consumption, and μ is the error term. 
For the purposes of  this work, equation 5 is expanded 
and adjusted to take into account three indices of  
economic growth: capital investment, which is measured 
by gross fixed capital formation, trade openness, and 
foreign direct investment. For the purposes of  this work, 
equation 5 is expanded and adjusted to take into account 
three indices of  economic growth: capital investment, 
which is measured by gross fixed capital formation, trade 
openness, and foreign direct investment.  The changes or 
modifications and extensions are written below:
GDPPCt= β0+β1CO2t+β2TOPt+β3FDI+β4GFCF+εt  (6)
We take the log of  the dependent and explanatory 
variables to guarantee consistency across all the variables.
lnGDPPCt= β0 + β1lnCO2t + β2lnTOPt + β3lnFDI + 
β4lnGFCF + εt                   (7)
Where, In: Natural logarithm, β0 is the intercept, β1 to β2 
are slope parameters. Economic growth is represented 
using GDP per capita (constant 2015 US$). Environmental 
degradation is measured with carbon dioxide emissions 
(kt), which serves as the main indicator of  environmental 
degradation in the literature (Opoku et al., 2022; Pal and 
Mitra, 2017; Sadorsky, 2009; Zheng et al., 2019). TOP 
represents trade openness, FDI is foreign direct investment, 
gross capital formation as proxy for capital investment 
(GFCF), and εt is the error term. Trade openness, foreign 
direct investment, and gross fixed capital formation 
served as control variables in this analysis. These control 
variables were employed with the support of  research 
by Mesagan (2015), Acheampong and Opoku (2023), 
and Gershon et al. (2024). Moreover, it is expected that 
all of  the control variables will have positive coefficients. 
However, it is anticipated that either a positive or negative 
effect on economic growth will result from environmental 
degradation as measured by carbon dioxide emissions.
However, equation (7) was converted into an ARDL 
Model result as follows in order to investigate the impact 
of  environmental degradation on economic growth 
in Nigeria using the Autoregressive Distributed Lag 
Technique (ARDL):

where ln stands for natural logarithms, P is the population, 
and E is the amount of  carbon emissions. The number of  
years is indicated by the subscript “t” on the RHS, while 
the first two terms are intercept parameters. It is assumed 
that, while emissions per capita may vary throughout 
nations at a given income level, income elasticity is 
constant across nations at a given income level. 
However, in order to facilitate empirical modeling in this 
research, GDP per capita will be used to represent the 
dependent variable, output growth (GDP), and carbon 
emissions (CO2) will be used to represent environmental 
degradation. This will make it possible for the study to 
demonstrate how environmental degradation affects 
Nigeria’s economic growth. Equation (2) functional form 
is written below as:
GDPPC= f (CO2)                   (3)
Where GDPPC stands for gross domestic product 
per capita as a measure of  economic growth and CO2 
stands for carbon emissions as a proxy for environmental 
degradation. 
Explicitly, equation (3) can be written as: 
GDPPC= α + β (CO2) + ε                (4)

METHODOLOGY
The ex-post facto design is the chosen research design 
for this work. An empirically based study design that 
establishes the cause-and-effect link between the 
independent and dependent variables is called the ex-post 
facto design. The incapacity of  the researcher to alter the 
data being studied is what distinguishes this method.

Model Specification
The model used in this paper is based on the 
Environmental Kuznets Curve (EKC) theoretical 
framework and a modified version of  the model of  

The bounds test is conducted by testing the null 
hypothesis (H0) against the alternative hypothesis (H1) 
using the following equations: H0: β0 = β1 = β2 = β3 = β4 
= β5 and  H1: β0 ≠ β1 ≠ β2 ≠ β3 ≠ β4 ≠ β5

The bounds test results based on the computed F-Statistic 
are similar to those of  Pesaran et al. (2001), who rejected 
the null hypothesis of  no cointegration and concluded 
that cointegration between the series is present if  the 



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F-Statistic is greater than the upper bound I (1) in each 
case.
Furthermore, after a long-run linear relationship 

(cointegration) between the variables has been confirmed, 
the short-run coefficients are computed using the short 
run / Error Correction Model provided in Equation 9.

ECTt-1 = lagged Error correction term. The ECT records 
the output evolution process that agents employ in 
response to the previous period of  prediction errors. The 
variables utilized in the paper are listed in Table 1.
Because of  its many advantages, the Autoregressive 
Distributed Lag Technique (ARDL) is chosen above 
other methods for predicting long-term linkages 
between variables. One advantage is that it can be 
applied whether or not the underlying independent 

variables are I(0), I(1), or mutually co-integrated 
(Pesaran et al. 2001). Its superiority over the Engle and 
Granger co-integration tests in small sample sizes is its 
second advantage (Allege & Ogundipe, 2013; Johansen, 
1991; Philips & Hansen, 1990). The third advantage 
of  employing the ARDL approach is the capacity to 
estimate an unconstrained conditional error-correction 
model (UECM) by treating each variable as a dependent 
variable in turn.

Table 1: Description of  Variables
Variable Description Sources
Environmental 
Degradation

The degradation of  the environment is gauged by terrestrial CO2 emissions, 
which come from burning fossil fuels as a result of  both industrial processes 
and human activities. In the analysis, the variable "metric tons per capita" is 
considered independent.

WDI, 2023

Economic Growth In this study, economic growth is measured using real GDP per capita (constant 
2015 US$), which is the total value of  goods and services produced by a country 
divided by its population. This variable is considered a dependent variable in the 
study.

WDI, 2023

Trade Openness Trade openness measured with trade (% of  GDP). This served as control variable WDI, 2023
Foreign direct 
investment

An investment made by a business or individual from one country into business 
ventures located in another. The measurement unit is Net Inflows (% of  GDP).

WDI, 2023

Gross Fixed 
Capital Formation

This is a proxy for capital investment and served as control variable. WDI, 2023

Source: Researchers’ Compilation, 2024

RESULTS AND DISCUSSIONS
Descriptive Statistics
Table 2 presents the descriptive statistics for the paper.
The summary data in Table 2 demonstrate that all the 
variables have positive mean values, with GDPPC and C02 
having the highest and lowest mean values, respectively. 
Also, each variable’s standard deviation offers a more 

accurate and comprehensive representation of  dispersion 
than an outlier, which has the potential to greatly exaggerate 
the range of  data. CO2 exhibits the least departure from the 
mean, while GDPPC exhibits the largest. The probability 
values of  the Jarque–Bera statistics, with the exception of  
FDI, suggest that the residual is normal and that the null 
hypothesis is not rejected.  

Table 2: Descriptive Statistics
 GDPPC CO2 TOP FDI GFCF
 Mean  1632.967  0.678296  35.71788  1.578278  28.46303
 Std. Dev.  812.5669  0.122238  9.550595  1.211996  11.24335
 Skewness -0.015194  0.364401 -0.096016  1.798286  0.360175
 Kurtosis  1.833415  1.840752  2.333943  6.754132  2.069240
 Jarque-Bera  1.872535  2.578137  0.660700  37.16465  1.904676
 Probability  0.392088  0.275527  0.718672  0.000000  0.385838
Observations  33  33  33  33  33

Source: Authors Computation, 2024 (Eviews-12)



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Am. J. Environ Econ. 3(1) 82-92, 2024

Unit Root Test
Time series data often show tendencies that can be 
handled with differencing, mostly for the purpose of  
figuring out how stationar the data is. An important 

stage in time series analysis is the Augmented Dickey-
Fuller (ADF) unit root test, the results of  which are 
displayed in Table 3 and which determines if  the series 
is stationary

Table 3: Unit Root Test Result
Variable
 

ADF Test Statistics
ADF Critical Value Order of  Integration

GDPPC -4.583012 -4.284580* I(1)
C02 -5.714705 -4.284580* I(1)
TOP -5.593623 -4.296729* I(1)
FDI -3.930156 -3.562882** I(0)
GFCF -4.835999 -4.284580* I(1)

Note: *,**,*** significant at 1%, 5% and 10%
Source: Authors Computation, 2024 (Eviews-12)

Table 4: Result of  ARDL Bounds Test for Cointegration
Null Hypothesis: No Long-run Relationships Exist
Test Statistic Value K
F-Statistic  4.899666 4
Critical Value Bounds
Significance Lower Bound Upper Bound
5% 2.56 3.49

Source: Researcher’s Computations based on E-Views 12

The result of  the Augmented Dickey-Fuller (ADF) 
unit root test indicate that the variables are integrated 
in a mixed-order fashion, with FDI being integrated at 
the level and the other variables being integrated at first 
order. I (0) and I (1) are hence the integration order.

Cointegration Test
Table 4 presents the analysis of  cointegration, using the 
ARDL bounds technique. 
The Table unequivocally demonstrates that the null 
hypothesis (H0: No cointegration) is rejected at 5%, where 

the F-statistic is greater than the critical values at both the 
top and lower bounds. This indicates that the variables 
have a lasting relationship. As a result, it can convincingly 
support the notion that the analysis’s variables are related 
over the long term.

Autoregressive Distributed Lag Estimates
Given the cointregration of  the dependent variable 
with the regressors and the mixed-order of  integration 
obtained from the unit root analysis, Table 5 presents the 
results of  the Linear ARDL estimate.

Table 5: ARDL Regression Results
Co-integrating Estimates (ECM Estimates)
Variable Coefficient Std. Error t-Statistic Prob.
DLOG(CO2) -0.455485 0.506049 -0.900081 0.3944
DLOG(CO2(-1)) 0.947917 0.510096 1.858313 0.1002
DLOG(TOP) -0.017559 0.176442 -0.099518 0.9232
DLOG(TOP(-1)) 0.525079 0.164458 3.192786 0.0127
DLOG(FDI) -0.195061 0.054162 -3.601467 0.0070
DLOG(FDI(-1)) -0.845424 0.167632 -5.043343 0.0010
DLOG(GFCF) -0.237726 0.383215 -0.620348 0.5523
DLOG(GFCF(-1)) 1.121922 0.402690 2.786070 0.0237
CointEq(-1)* -0.951930 0.137727 -6.911710 0.0001
R-squared 0.869433
Adjusted R-squared 0.718779
Durbin-Watson stat 1.917854



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Am. J. Environ Econ. 3(1) 82-92, 2024

In accordance with the estimated ARDL regression result, 
environmental degradation in Nigeria, as determined by 
carbon dioxide emissions (kt), has a negative relationship 
with economic growth. The negative sign indicates that, 
both in the short and long terms, respectively, economic 
growth declines by about -0.455% and -0.893% ceteris 
paribus as the level of  carbon emissions rises. 
The EKC hypothesis—which holds that degradation 
of  the environment impedes economic progress in 
Nigeria—is impliedly confirmed by these findings. 
Previous studies (Grossman & Krueger, 1995; Sisay & 
Balázs, 2019; Hammed et al. 2020; Maneejuk et al. 2020; 
Acheampong & Opoku, 2023 and Mohammed et al. 2024) 
have demonstrated that environmental degradation, as 
measured by carbon dioxide emissions, has a negative 
impact on economic growth. 
Most estimates of  the control variables, however, differ 
from the literature. In contrast to previous studies, trade 
openness and foreign direct investment are expected 
to favorably influence economic growth, as found by 
Mesagan (2015) and Acheampong and Opoku (2023). In 
contrast, trade openness appears to have a positive and 
statistically significant one-period lagged coefficient in 
the short term, indicating that trade openness influences 
economic growth. This result is in line with the research 
conducted by Mesagan (2015) and Acheampong and 
Opoku (2023).  In contrast, there was a positive and 
significant correlation between economic growth and the 
short-run, one-period delayed coefficient of  gross fixed 
capital production in Nigeria during the study period.
More precisely, in the medium and long terms, 
respectively, a percentage increase in carbon emissions 
will result in a decrease in economic growth of  roughly 
-0.455% and -0.893%. These results suggest that the EKC 
theory—which holds that environmental deterioration 
impedes economic progress in Nigeria—is validated 
there. Previous studies have shown that environmental 
degradation, as assessed by carbon dioxide emissions, has 
a negative effect on economic growth (Acheampong & 
Opoku, 2023; Mohammed et al., 2024, among others).
It has been discovered that trade openness and economic 
growth in Nigeria are negatively correlated, both in the 
short and long terms. This is contrary to theoretical 
assumptions, since one would expect trade openness to 
positively affect economic growth. 
Economic growth will contract by -0.018% in the 
near term for every percentage point increase in trade 

openness. On the other hand, trade openness hinders 
economic growth in the long run. Most of  these 
findings go counter to the findings of  Mesagan (2015) 
and Acheampong and Opoku (2023), who discovered 
a positive relationship between trade openness and 
economic growth. Conversely, a positive, statistically 
significant, and short-run period-lag coefficient of  trade 
openness suggests that, over the studied time, trade 
openness expansion promotes economic growth. This 
result is in line with the research conducted by Mesagan 
(2015) and Acheampong and Opoku (2023).
Additionally, Table 5’s findings showed that foreign direct 
investment considerably impedes economic growth in the 
near run. Therefore, there will be a short-term decline in 
economic growth of  -0.195% for every percentage point 
rise in foreign direct investment. Nevertheless, there is a 
robust and favorable relationship between foreign direct 
investment and economic growth. This is in line with 
theoretical expectations.
Likewise, gross fixed capital creation, which is a proxy 
for capital investment, is expected to have adverse 
effects on Nigeria’s economic growth both in the short 
and long run. Specifically, Nigeria’s economic growth 
will fall by -0.2375% and -0.706% in the short and long 
terms, respectively, for every 1% rise in gross fixed 
capital creation. Capital investment is not a substantial 
contributing factor to Nigeria’s economic growth, 
according to the statistically insignificant influence of  
gross fixed capital formation on economic growth. This 
conclusion might have something to do with Nigeria’s 
extremely low level of  capital investment.

Post-Estimation Test Results
To evaluate the validity of  the findings and the model’s 
stability and applicability, the study ran a few diagnostic 
tests. The results shown in Table 6 indicates that the model 
did not exhibit serial correlation or heteroskedasticity 
during the research period. The residuals are 
homoscedastic, according to the heteroscedasticity tests. 
According to the findings of  the diagnostic tests for 
heteroscedasticity and serial correlation, the data appears 
to be quite well-behaved. Additionally, the residues’ 
normal distribution is indicated by the fact that the 
p-value for the normality test for the research period is 
greater than 0.05. As a result, the residuals have a uniform 
distribution. Consequently, the null hypothesis regarding 
the normal distribution was not rejected.

Long Run
Variable Coefficient Std. Error t-Statistic Prob.
LOG(CO2) -0.893197 1.103891 -0.809135 0.4418
LOG(TOP) -0.722653 0.511378 -1.413149 0.1953
LOG(FDI) 0.758438 0.277434 2.733764 0.0257
LOG(GFCF) -0.706332 0.536579 -1.316362 0.2245
C 11.66561 2.825789 4.128267 0.0033

Source: Researcher’s Computation Using EViews-12 (2024)



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Am. J. Environ Econ. 3(1) 82-92, 2024

Table 6: Diagnostic Test Results
Test Null Hypothesis T-Statistic Prob
Jarque-Bera There is a normal distribution 0.614 0.74
Breusch-Godfrey LM No serial correlation 0.151 0.86
Heteroskedasticity: Breusch-Pagan-Godfrey No conditional heteroscedasticity 2.381 0.10

Source: Researcher’s Computations based on E-Views 12

Stability Test Result
The stability test in Figure 1 demonstrated the stability 
of  the economic growth model during the inquiry period 

because the chart plots at the 5% significant level fall 
within the crucial constraints.

Figure 1: Stability Tests Result
Source: Researcher’s Plot using E-Views 12

CONCLUSION 
The study examined the connection between 
environmental deterioration and economic growth, 
and using the Autoregressive Distributed Lag (ARDL) 
technique, it evaluated the impact of  environmental 
degradation on economic growth in Nigeria from 1990 
to 2022. The regression analysis showed a negative 
correlation between economic growth and environmental 
degradation. These results suggest that the EKC 
theory—which holds that environmental degradation 
impedes economic progress in Nigeria—is validated here. 
Furthermore, a one-period lag in positive sign for carbon 
emissions indicated that, in the short run, economic 
growth increases by roughly 0.948% ceteris paribus as 
carbon emissions rise. This outcome is also in line with 
theoretical hypotheses that growth is initially associated 
with increasing emissions. However, when economies 
of  scale, money, and innovation improve, increased 
productivity turns into environmentally sustainable 
progress. As a result, the study showed that there is 
an inverse U-shaped association between economic 
expansion and environmental degradation. Many people 
agree that this result represents the Kuznets inverted 
U-shaped curve.
This article has shown that carbon emissions and 
economic growth in Nigeria are negatively correlated. 
Therefore, in order to lower greenhouse gases—the 
main contributors to climate change—it is imperative to 
increase energy efficiency in the world’s energy mix. Thus, 
it is important to exercise caution while implementing 
both national and international environmental policies 
so as not to foster closed-form connections that might 
impede economic progress. Furthermore, the positive 
correlation that exhibited a one-period lag between 

carbon emissions and economic growth implied that 
some degree of  environmental deterioration is necessary 
in order to stimulate economic growth. Therefore, 
policymakers need to be cautious in the fight against 
environmental pollution because most instruments and 
strategies meant to lower greenhouse gas emissions also 
have the potential to change patterns of  production and 
consumption and impede economic growth. Therefore, 
planning for mitigation, restoration, and sustainable 
practices should serve as the foundation for policies that 
support economic growth, particularly in the early phases 
of  development.

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