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American Journal of
Environmental Economics (AJEE) 

Achieving Sustainable Development: Balancing Carbon Dioxide Emissions Reduction 
and Poverty Alleviation in Less Developed Countries

Famanta Mahamane1*, Junhong Bai1, Yuanchao Bian1, Nengzhi Yao1, Jing Gao2, Abid Ali Randhawa1

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

DOI: https://doi.org/10.54536/ajee.v2i1.1981
https://journals.e-palli.com/home/index.php/ajee

Article Information ABSTRACT

Received: September 08, 2023

Accepted: October 11, 2023

Published: October 17, 2023

Carbon dioxide emissions (CO₂) reduction and poverty have always been conflicting subjects 
regarding national policy implementation, particularly in less developed countries (LDCs). 
Based on energy consumption as a driving mechanism, this study investigates whether 
reducing CO₂ aggravates poverty in LDCs. To achieve this aim we analyze a 30-year panel 
data of  15 LDCs and deploy fixed-effects regression models. We used two-stage least square 
(2SLS) regressions to address endogeneity concerns. Our results suggest that (CO₂) emission 
has a negative relationship with poverty, which means reducing CO₂ emissions increases 
poverty in LDCs. Our results provide important implications for policymakers, showing 
that a green structural transformation implementation that highlights the balance between 
environmental protection and economic development would be recommended for LDCs.

Keywords

CO₂ Emissions, Sustainability, 
Poverty Reduction, LDCs

1 School of  business, Nanjing Normal University, Nanjing 210000, China
2 Jiangsu Normal University, Xuzhou 221116, China
* Corresponding author’s e-mail: mfamanta@outlook.com

INTRODUCTION
As countries worldwide strive to lower their greenhouse 
gas emissions to minimize global warming, many need to 
be aware of  these policies’ potential negative effects on 
poverty alleviation. Carbon emissions reduction measures 
can significantly impact low-income households, as 
they may lack the resources necessary to make up for 
any decreases in income resulting from policy changes. 
Reducing carbon emissions and poverty have always 
been conflicting subjects. Indeed, considering the level 
of  economic development and other indicators, the 
impact of  CO₂ reduction differs from one country to 
another. According to the Low Carbon Index, several 
G20 countries have lowered their carbon intensity while 
sustaining GDP growth, including emerging countries 
such as China, India, and Mexico. A thirty-year increase in 
carbon emissions indicates a reduction in poverty in East 
Asia, the Pacific, and South Asia. Sub-Saharan Africa, 
however, has reduced emissions while nearly tripling the 
number of  people living in poverty (World Economic 
Forum). So what is the reason for the contrast observed 
in Sub-Saharan Africa, home to most LDCs?
Regarding carbon emissions reduction, South Africa, 
a developing country, has recently been urged by its 
European and American partners to close its charcoal 
thermal power plants to reduce carbon emissions. Knowing 
that 80% of  South African electricity is produced using 
charcoal and more than 100 000 persons are employed 
in those charcoal thermal power plants, South Africa 
is suffering from a lack of  energy and unemployment, 
increasing at the same time poverty (Zack Mwekassa, 
2022). At the same time, with the Russia-Ukraine crisis, 
Germany opened the biggest charcoal thermal power 
plant in Germany to increase its energy production. 
Even though South Africa is not a LDC, considering the 

given example, is reducing carbon emissions beneficial to 
LDCs? Carbon emissions reduction affects the poverty 
patterns of  a nation. For instance, increasing the economic 
activities within the boundaries of  a country has the 
sporadic effect of  increasing carbon emissions (Amini et 
al., 2021). The progress of  LDCs away from poverty will 
require investing in industries that release effluents, causing 
air, water, and soil pollution (Sarker et al., 2021). Increasing 
the agricultural activities of  LDCs will also result in a rise in 
greenhouse gases due to using chemicals, such as fertilizers 
and herbicides (Kumar et al., 2019).
Further, implementing crucial infrastructure activities in 
these areas increases CO₂ emissions (Stefanakis, 2019). 
The increase in economic activity is not beneficial to 
climate preservation efforts. Not all countries have 
equal CO₂ emissions and do not share the burden of  
climate change due to CO₂ emissions (Meng et al., 2018). 
Furthermore, LDCs typically rely more on fossil fuels 
for energy. So reducing carbon emissions could lead to 
higher energy prices and reduced economic activity. That 
could, in turn, lead to increase poverty. So this research 
uses econometrics way to prove the aforementioned 
assumption.
This study examines whether reducing carbon emissions 
in LDCs exacerbates poverty. The analysis of  the most 
recent research on the topic reveals a shortfall in the 
number of  studies done on LDCs, particularly those 
in Sub-Saharan Africa, where most of  the LDCs are 
located. Most current research material is limited to more 
developed countries like China and the USA (Obaideen 
et al., 2022). Further, there has yet to be a conclusive 
finding regarding the association between CO₂ emissions 
reduction and poverty; hence, more research needs to be 
done. So regarding what is previously said, this study is 
mainly focused on LDCs and takes extreme poverty and 



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carbon emission per capita indicators as main variables.
Another point that this research considers is the 
integration into the analysis of  the Green Structural 
Transformation for LDCs. According to the UNCTAD, 
Structural transformation refers to the transformation 
from low productivity and labor-intensive production 
to high value-added and high productivity economic 
activities. Green structural transformation combines 
responsible economic, social, and environmental growth 
with structural economic transformation. It includes the 
transition from a “recession” carbon-intensive sector to 
a “prosperity” low-carbon economic activity, promoting 
the effective use of  resources (materials, energy, land, 
water) throughout the development process.
This research is essential in providing data that will be 
used for future planning purposes. CO₂ emissions 
reduction activities must consider their impact on Poverty 
mitigation to ensure sustainability among LDCs. A further 
novelty is incorporated through the attempt of  this study 
to contribute to the existing knowledge on the topic. 
Involvement in the research will result in a heightened 
understanding of  the impact of  CO₂ emission reduction 
on poverty in LDCs.
Along with the introduction, this paper is organized as 
follows: the second is the literature review, the third is the 
data and methodology, and the fourth is the discussion 
and conclusion.

LITERATURE REVIEW
Poverty Alleviation
According to an article published by Concern Worldwide 
(a non-governmental organization), around ten per cent 
of  the global population lives in severe poverty. It has 
pointed out eleven leading causes of  extreme poverty: 
inequality, conflict, malnutrition and stunting resulting 
from hunger, inadequate medical systems, particularly for 
women and kids, insufficient access to clean water and 
sanitation facilities, climate change, limited educational 
opportunities, inadequate public works and infrastructure, 
global health crises such as epidemics and pandemics, 
insufficient social services, and an absence of  personal 
security nets. Our focus will be on the extreme poverty 
caused by climate change. According to the same article, 
by 2030, global warming may result in the displacement 
of  over one hundred million individuals into conditions 
of  severe impoverishment. Impoverished communities 
frequently depend on agricultural practices, hunting, and 
gathering as a means of  sustenance or income generation. 
However, there are challenges with these jobs, like 
insufficient food reserves in the case of  a poor harvest--
which is part of  why climate change and natural disasters 
can push them into poverty and make a recovery much 
harder (Concern Worldwide, 2020). 
Wolla pointed out that to close the gap between prosperous 
and less-developed economies, individuals need to focus 
on economic growth. Disparities in a nation’s economic 
advancement often stem from variations in inputs like 
production and productivity (Wolla, 2017). Organizations 

need incentives for innovation and production to improve 
productivity and growth, while the government plays a 
crucial role in enhancing capital resource availability. 
Access to international trade benefits the global economy 
by providing markets for the goods and products 
produced in less-developed countries. In addition, it 
increases productivity by increasing access to capital 
resources. Sarlo  argues that luck, choice, and enablement 
drive poverty. These three things can help explain 
longer duration and persistent poverty. He pointed out 
some ideas that should be tried: reduce the bad luck in 
people’s lives (poor parenting, the genetic transmission 
of  diseases, negative influences on children, etc.; promote 
good choices (giving people opportunities and quality 
education); work to eliminate those things that make 
people stay in poverty (benefits programs that discourage 
or limit work) (Sarlo,2019). Jose analyzes the cause-and-
effect explanations of  poverty in ldcs, focusing on 1092 
undergraduate participants from Nicaragua, El Salvador, 
Chile, and Spain. The world’s economic structure, fate, 
nature, cultural practices, political misconduct, and the 
population of  developing countries were identified as 
components. To identify five distinct subject categories, 
the study used undergraduates’ causal attributions 
of  poverty, country of  origin, perceived social class, 
economic situation, political ideology, and religious 
beliefs. The findings indicate that poverty in developing 
nations is predominantly influenced by the interviewees’ 
home country’s development level, political ideology, and 
economic circumstances (Jose et al., 2017). 
According to Badreldin, poverty has become a global 
issue, particularly in LDCs. Their study examines social 
poverty, measurement techniques, and contributing 
factors. It uses three models: low and medium-income 
countries, medium-income countries, and low-income 
countries. The primary drivers of  human poverty are 
overall spending on education and healthcare and the 
increasing share of  the total GDP. The research suggests 
that governments should prioritize increasing spending 
on education and healthcare to eliminate various types of  
poverty (Badreldin et al., 2014). Gore and Charles suggest 
that understanding chronic poverty requires exploring 
poverty traps, identifying underlying factors, and 
analyzing poverty at household, community, and national 
levels. Globalization requires a global perspective, as 
the prevalence and persistence of  poverty in LDCs are 
attributed to an international poverty trap reinforced by 
trade and finance relationships (Gore & Charles, 2003). 
Castaneda identifies individuals experiencing severe and 
low poverty, focusing on those with daily incomes below 
$1.90. Rural and youthful populations dominate extreme 
poverty, with children under 15 comprising over 45%. 
The study highlights the importance of  rural households 
and urbanization in mitigating poverty, with improved 
educational achievement and urbanization contributing 
to poverty alleviation (Castaneda et al., 2016). Mahembe 
analyzes the prevalence of  extreme poverty in developing 
nations and evaluates the Millennium Development Goal 



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of  reducing extreme poverty by 50 percent by 2015. The 
research employs a descriptive methodology to classify 
countries into the following five geographic regions: 
sub-Saharan Africa, East Asia, South Asia, Europe, and 
Latin America and the Caribbean. The global poverty 
rate is 9.6%, but the concentration of  severe poverty in 
sub-Saharan Africa and Southern Asia poses challenges 
(Mahembe et al., 2018).

Carbon Emission Reduction in LDCs
Zhang examines the association between carbon 
emissions and five influential factors in 50 developing 
nations from 1995-2017. Results show steady-
state equilibrium, with some countries showing the 
Environmental Kuznets Curve phenomenon. Energy 
consumption impacts CO₂ positively, while international 
trade negatively affects developing countries, fossil fuel 
usage affects the environment (Zhang et al., 2019). Hanif  
examines the influence of  sustainable and non-sustainable 
energy consumption and economic development on 
carbon emissions in developing Asian economies. It 
finds that sustainable energy sources reduce emissions, 
while non-sustainable sources increase them. Shifting 
to sustainable energy sources is crucial for carbon-free 
economic growth and regional collaboration (Hanif  et 
al., 2019). Hu examines the impact of  renewable energy 
consumption and commercial services trade on global 
carbon emissions from 1996-2012. They find a significant 
relationship between economic growth and greenhouse 
gas emissions; a rise in renewable energy usage is crucial 
for mitigating emissions (Hu et al., 2018). Wang et al. 
(2011) analyze CO₂ emission patterns and economic 
progress in 128 countries and regions using models like 
σ-convergence, absolute β convergence, and conditional 
β-convergence. Results show a convergence in emissions 
over the past four decades, with LDCs narrowing the 
gap. Factors influencing emissions include GDP per 
capita, population size, and resource utilization (Wang et 
al., 2011). Pareto, Romer, Georgescu-Roegen, Coase, and 
Arthur Cecil Pigou are renowned academics researching 
the ramifications of  polluting agents on human conduct.
Pareto espoused the notion that a laissez-faire economic 
system would result in the optimal allocation of  resources. 
However, externalities, such as pollution, can give rise 
to sub-optimal outcomes. Coase proposed a solution 
to address the issue of  pollution by recommending 
that polluters be compensated with tradable goods by 
consumers. In 1979, Georgescu-Roegen proposed the 
notion of  entropy, which suggests that the depletion 
of  the environment is a consequence of  economic 
expansion. Theories of  endogenous growth posit that 
innovation can be fostered by environmental resources, a 
view espoused by economists such as Romer and Aghion-
Howitt, as a means of  achieving sustained growth over the 
long run. Nevertheless, there is a contention that altering 
consumption patterns is imperative to alleviate the 
burden on natural resources. The crux of  the argument 
centers on the valuation of  natural resources and the 

appropriate means of  incorporating such valuation into 
pricing mechanisms.

Relationship between Carbon Emission Reduction 
and Poverty Alleviation
Some more recent research and papers about the 
relationship between CO₂ emissions and poverty 
reduction initiatives have been carried out. Jin applied 
the extended linear expenditure system model to 
measure poverty levels among Chinese citizens. The CO₂ 
emissions accounting method was then utilized to keep 
track of  the CO₂ emissions in the country to establish 
the relationship between CO₂  production and poverty 
levels. The research found a decoupling relationship 
between CO₂ emissions and economic development, 
which means the research confirmed the existence 
of  a correlation between CO₂ emissions and poverty 
levels (Jin et al., 2020). Malerba noted that despite calls 
by the international community to address poverty and 
climate degradation simultaneously, more was needed to 
know about the relationship between the two variables. 
The study attempted to demystify this undertaking by 
establishing the carbon intensity of  poverty reduction 
(CIPR) as a combined model to measure poverty and 
CO₂ emissions. The research demonstrated that the 
CIPR in most countries was heterogeneous; economic 
growth was found to harm CIPR below a certain income 
level (Malerba, 2020). Fu reviewed the research data 
obtained from the provinces within China to identify 
a coupling relationship between carbon emissions and 
poverty alleviation activities. The research established that 
implementing poverty reduction initiatives in China was 
responsible for the economic growth between 2009 and 
2019. The researchers observed that increased economic 
development reduced CO₂ emissions in 26 provinces. 
Only three provinces in China showed an increase in 
CO₂ production. The research showed that investment 
in poverty alleviation does not necessarily result in CO₂ 
emissions increase (Fu et al., 2021). Leal Filho  established 
that there needed to be more research articles addressing 
the issue of  the relationship between climate change 
and economic outcomes (Leal Filho, 2019). This aspect 
limited the guidance to different nations regarding the 
most appropriate interventions they needed to implement 
to address poverty (Miladinov, 2020).
The researchers noted that although some regions were 
ideally placed to address climatic stressors, such as 
increased urban heat, few did so. Wang and Li utilized 
the grey Verhulst model to determine the impact of  
economic development on climate change. Using the 
model, it was evident that the current economic growth 
within China would result in growth in CO₂ emissions. 
However, the research established that the peak CO₂ 
emissions will not be attained by 2030, which provides 
the Chinese government with enough time to implement 
conservation efforts (Wang & Li, 2019). The U.N. 
Framework Convention on Climate Change (UNFCCC) 
intends to maintain the rate of  warming to 2°C and 



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eliminate extreme poverty. Achieving poverty elimination 
objectives does not threaten climate goals, but achieving 
a minimum expenditure of  $2.97 PPP could impact 
emission targets. The top 10% of  global income earners 
contribute 36% of  households’ carbon footprint, so 
addressing poverty does not jeopardize the climate target 
(Hubacek et al., 2017).

History of  Emission Reduction Policies in LDCs
LDCs face unique challenges in formulating policies 
related to global warming due to their reliance on 
natural resources, insufficient adaptation capabilities, and 
limited economic progress. To address this issue, LDCs 
have implemented various policy decisions, including 
international involvement, national adaptation plans, 
access to climate finance, capacity building, regional and 
international cooperation.
International involvement involves expressing concerns 
and promoting a global initiative to reduce greenhouse gas 
emissions. National adaptation plans outline the nation’s 
susceptibilities, adjustments, and fiscal requirements to 
improve indigenous communities, economic sectors, and 
ecological systems. Access to climate finance, such as the 
Green Climate Fund, is crucial for supporting mitigation 
and adaptation actions. Capacity building involves 
providing personnel training, technology transfer, 
fortification of  national institutions, and public awareness. 
Regional and international cooperation is a common 
strategy employed by LDCs to enhance their standing 
in global climate negotiations, fostering collaboration 
and coordination among member nations. This involves 
knowledge sharing, exchange of  best practices, and 
resource allocation to tackle shared challenges related to 
global warming.
The aforementioned political decisions were made within 
the context of  established international conventions, 
the historical background of  which can be outlined as 
follows: (1) The UNFCCC, ratified in 1992, is a global 
accord aimed at stabilizing the levels of  greenhouse 
gases in the atmosphere. LDCs (LDCs) have ratified this 
convention and have pledged to undertake measures to 
alleviate the impact of  climate change. (2) The Kyoto 
Protocol was adopted in 1997 under the UNFCCC. The 
established objectives aimed at reducing greenhouse 
gas emissions for developed nations. LDCs (LDCs), 
categorized as developing nations, were not obligated to 
decrease their emissions under the protocol. However, 
they were qualified to obtain financial and technological 
assistance to implement mitigation measures. (3) The 
Global Environment Facility (GEF) was founded in 
1991 to provide financial assistance to Least Developed 
Countries (LDCs) and other developing nations to 
facilitate environment-related undertakings, such as 
climate change initiatives. LDCs (LDCs) have derived 
advantages from the financial support provided by the 
Global Environment Facility (GEF) to implement 
measures to adjust and minimize the impacts of  climate 
change. (4) The Rio+20, which took place in 2012, aimed 

to foster intergovernmental discourse on sustainable 
development among United Nations member countries. 
LDCs (LDCs) have underscored the necessity for 
heightened support to tackle the obstacles posed by 
climate change, with a particular focus on adaptation. 
(5) In 2015, the Paris Agreement was adopted at the 21st 
Conference of  the Parties (COP21) to the United Nations 
Framework Convention on Climate Change (UNFCCC). 
The agreement requires all countries, including those 
categorized as LDCs (LDCs), to implement actions 
with the goal of  limiting the increase in worldwide 
temperatures to a degree substantially lower than 2 
degrees Celsius above the levels documented during the 
pre-industrial period. The Paris Agreement acknowledges 
the significance of  heightened assistance for developing 
nations, particularly the LDCs, in addressing the obstacles 
posed by climate change. Following the adoption of  
the Paris Accord, the global warming policies of  the 
LDCs (LDCs) have centered on the execution of  their 
Nationally Determined Contributions (NDCs) and 
facilitating climate finance to bolster their endeavors in 
adaptation and mitigation.

Energy Consumption as A Driving Mechanism of  
Carbon Emission in LDCs
In a context of  concern, the function of  energy in the 
economic transformation of  LDCs is a critical issue 
that deserves further attention (UNCTAD, 2015). In 
a growing context of  the international community 
regarding climate change, renewable energy options 
have been put forward to meet this unmet theoretical 
demand without compromising efforts to reduce global 
GHG emissions (IEA, 2020). Access to energy services 
is crucial for economic development and structural 
transformation, contributing to sustainable and inclusive 
structural transformation. This is essential for eradicating 
poverty and achieving other Sustainable Development 
Goals. However, neglecting the economic dimension of  
the problem has led to a neglect of  this crucial aspect. 
The productive use of  electricity is essential in this regard 
because it provides access to energy for the economy’s 
structural transformation and helps create sufficient 
demand for investment in generation and distribution to 
be sustainable. 
To reduce global energy consumption and mitigate 
the impacts of  climate change, many countries have 
implemented policies to reduce energy consumption, 
including ratifying the Paris Climate Agreement. 
However, this has hurt some LDCs, as poverty rates have 
risen due to rising energy costs (UNCTAD, 2015; IEA, 
2019; World Bank, 2020). 
Energy consumption is a critical driving mechanism of  
carbon emissions in LDCs (IPCC, 2014). These countries 
require significant energy to develop their economies, 
improve living standards, and reduce poverty (UNDP, 
2018). However, the vast majority of  energy production 
in these countries is derived from fossil fuels, a significant 
carbon emission source (World Bank, 2019).



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LDCs face unique challenges regarding energy 
consumption and carbon emissions (IEA, 2020). They 
are at a crucial economic development stage and require 
significant energy to power their industries, transport 
systems, and households. However, they need more 
financial resources and technological capacity to invest in 
renewable energy sources or implement energy-efficient 
practices. As a result, they rely heavily on fossil fuels to meet 
their energy needs, contributing to high carbon emissions.
Population growth is one of  the main factors driving 
energy consumption and carbon emissions in LDCs. 
Many of  these countries have high population growth 
rates, which puts pressure on their energy systems. As 
the population increases, so does the demand for energy 
to power homes, schools, hospitals, and other essential 
services. Using fossil fuels is often the most cost-effective 
way to meet this demand, leading to high levels of  carbon 
emissions (IPCC, 2014).
Industrialization is another significant factor driving 
energy consumption and carbon emissions in LDCs. 
These countries are often rich in natural resources 
and have vast coal, oil, and gas reserves. They seek to 
exploit these resources to develop their economies and 
improve their citizens’ living standards. However, these 
resources extraction and processing require significant 
energy, leading to high carbon emissions (IEA, 2020). 
Transportation is another significant factor driving 
LDCs’ energy consumption and carbon emissions. These 
countries are experiencing rapid urbanization, which has 
led to a surge in the number of  vehicles on their roads. 
Most of  these vehicles run on fossil fuels, contributing to 
high carbon emissions. In addition, many less-developed 
countries lack adequate public transportation systems, 
which forces people to rely on private vehicles, further 
exacerbating the problem (World Bank, 2019). The role 

of  international trade and investment in driving energy 
consumption and carbon emissions in LDCs must be 
considered. Many developed countries outsource their 
manufacturing and other industries to LDCs, contributing 
to high energy consumption levels and carbon emissions 
in these countries. The demand for goods and services 
in developed countries also contributes to high energy 
consumption levels and carbon emissions in LDCs 
(UNFCCC, 2021).

LDCs’ Poverty Rate And CO₂ Emissions Reduction 
Features Description
An Overview of  Poverty in LDCs
Ninety percent of  the world’s population lived in extreme 
poverty before the 19th century; capitalism led to 
increased human well-being (Dylan et al., 2023). However, 
it remains a significant challenge worldwide, especially in 
LDCs in the 21st century. Generally, some indexes are 
used to quantify or give an overview of  the poverty 
phenomenon. Those indexes are the Extreme Poverty 
Index, the Poverty Headcount Ratio, the Multidimensional 
Poverty Index, the Income Inequality Index, the Child 
Poverty Index, Hunger and Malnutrition Index, the Gender 
Disparities Index, and Rural Poverty Index.
In 2021, approximately 706 million individuals lived in 
extreme poverty, with a daily income below $1.90. This 
poverty headcount ratio is prevalent in many LDCs, with 
41% of  the population in Sub-Saharan Africa experiencing 
extreme poverty in 2020. The Multidimensional Poverty 
Index (MPI) identifies 1.3 billion individuals across 101 
nations as being multidimensionally impoverished, with 
a significant proportion in regions with lower levels of  
development, such as South Asia and Sub-Saharan Africa. 
Income inequality is a significant issue in LDCs, with 
Oxfam’s 2021 report showing a $5.3 trillion increase in 

Figure 1: Share of  the population living in extreme poverty
Source: World Bank Poverty and Inequality Platform



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billionaires’ wealth while a 200 million increase in poverty. 
Child poverty is a significant concern, with UNICEF 
estimating 385 million children in extreme poverty, 
accounting for almost 50% of  the global population of  
impoverished individuals. Persistent hunger affected 690 
million individuals in 2019, primarily in less developed 
regions, particularly in Sub-Saharan Africa and Southern 
Asia. Gender disparities are also a significant issue 
in LDCs, with 330 million females, including girls, 
experiencing restricted access to educational institutions, 
medical services, and financial opportunities. Rural 
poverty is a prevalent issue in LDCs, with 80% of  the 
global population living in extreme poverty concentrated 
in rural areas, where access to basic amenities and 
economic prospects is often restricted. The graph below 
displays the share of  the population living in extreme 
poverty within our sample group.

Energy Consumption in LDCs
LDCs typically exhibit low levels of  energy consumption. 
These countries face significant challenges in terms of  
energy access and energy infrastructure development.
In many LDCs, a significant portion of  the population 
lacks access to electricity. Traditional energy sources such 

as wood fuel and coal are often used to meet basic energy 
needs. However, these energy sources could be more 
efficient and positively impact health and the environment.
Energy consumption in LDCs is also limited due to the 
need for developed industries. Energy-intensive sectors 
such as manufacturing and steel production are often 
underdeveloped or non-existent in these countries. 
Moreover, LDCs often struggle with energy supply issues. 
Production, transmission, and distribution infrastructure 
for electricity are often inadequate, limiting energy 
availability for local populations and businesses. It is 
important to note that energy consumption in LDCs can 
vary significantly from country to country. Some LDCs 
have access to significant natural resources such as oil 
or hydroelectric power, which can increase their energy 
production and consumption capacity.
Many LDCs are implementing initiatives to improve 
energy access and promote clean and sustainable energy 
sources to address these challenges. This may include 
the deployment of  solar, wind, and hydroelectric 
technologies and policies aimed at improving energy 
efficiency and reducing dependence on fossil fuels. The 
graph below shows an overview of  some LDCs energy 
consumption.

Figure 2: The different primary energy sources of  LDCs, 2014
Source: UN DESA (2016b)

MATERIALS AND METHODS
Samples Selection
According to Marcil , poverty is when some members 
of  a nation do not have the income required to afford 
essential utilities, such as food and shelter (Marcil et 
al., 2021). While poverty exists in every country, some 
countries experience it more than others (Karpman et al., 
2020). For research purposes, poverty will be expressed 
as a percentage, which depicts the number of  poor 
people in a country (Lakner et al., 2022). As a result, the 

higher the percentage, the more people live in poverty 
(Sherman et al., 2021). The list of  countries classified as 
less developed has been derived from the World Bank, a 
reliable source of  information regarding which countries 
fall under the classification of  poverty-burden nations 
(World Population Review, 2022). 
A LDC has a lower standard of  living than other more 
industrialized nations. These countries are typically 
characterized by a higher infant mortality rate, lower 
gross national income (GNI) per capita, higher levels of  



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poverty, higher level of  inequality, dependence on natural 
resources, lack of  economic diversification, reliance 
on agriculture, etc. However, not all of  the LDCs are 
included in this study. Due to the quality and availability 
of  data, 15 counties (table 1) are included in the study as a 
sample. However, unbalanced panel data was utilized for 
thirty years for each country. The range selected was from 
1990 to 2020. Using panel data increased the accuracy of  
the findings.
Further, the research process has resulted in the collection 
of  raw data on the poverty indexes of  the countries 
included in the study, along with their corresponding 
CO₂ emissions rates. The CO₂ emission rates are 
graded based on each country’s per capita metric ton 
emissions (Helmers et al., 2021). This data is useful when 
determining the relationship between poverty and CO₂ 
emissions in LDCs. The CO₂ emissions per capita data 
were collected from the Global Carbon Project, and the 
poverty rate from Global Extreme Poverty. Both sources 
are reliable.

a result, it is not sufficient to convert consumption levels 
by market exchange rates alone.

Key Independent Variable
The logarithm form of  CO₂ per capita (LnCo₂) has 
been selected as the key independent variable. Emissions 
of  CO₂ result from the combustion of  fossil fuels and 
the production of  cement. Included are CO₂ from the 
combustion of  solid, liquid, and gaseous fuels, as well as 
gas venting (World Bank). The LnCo₂ will be an indicator 
for measuring carbon emission in this research.

Control Variables
According to some researchers, poverty reduction cannot 
be solely attributed to economic growth, as evidenced by 
the works of  Datt and Ravallion (Datt & Ravallion, 1992) 
and Fosu (Fosu, 2017). The authors posited that alterations 
in inequality have a statistically noteworthy impact on the 
mitigation of  poverty, as evidenced by the works of  Datt 
and Ravallion (Datt & Ravallion, 1992) and Kraay (Kraay, 
2006). Considering those previous arguments, the GINI 
index (1) has been selected to indicate inequality. The 
Gini coefficient is a statistical measure that quantifies the 
degree of  income inequality within a given population. 
Elevated values denote an increased degree of  disparity. 
The highest level is 100 or 1.

Agriculture Annual Growth Rate 
The aggregates for agriculture in 2015 are formulated 
using a fixed value in US dollars. The agriculture domain 
is classified under ISIC divisions 01-03, including forestry, 
hunting, fishing, and crop cultivation. Value added is the 
final output of  a sector, obtained by aggregating outputs 
and deducting intermediate inputs. The calculation does 
not consider depreciation or natural resource depletion. 
The origin of  value added is established by the World 
Bank’s ISIC, revision 4. First, this indicator was employed 
to measure the impact of  agriculture on carbon emissions 
as a driving mechanism, and second, it was used as a 
control variable for the poverty rate.

Electricity Access
Access to electricity is the proportion of  the population 
that has access to electricity. The World Bank collects 
information regarding electrification from industry, 
national surveys, and international sources. (World Bank). 
This indicator was used as a driving mechanism of  energy 
consumption on carbon emissions in LDCs and as a 
control variable for the poverty rate.

Mortality Rate
The gross mortality rate is a statistical measure of  fatalities 
per 1000 individuals. Natural growth rate is calculated by 
subtracting crude mortality rate from birth rate, resulting in 
population change rate without immigration (World Bank).

Health Expenditure 
The health expenditure of  the government, with the 

Table 1: List of  the sample countries
 Country Poverty index 

(30 years)
Metric tons CO₂  
per capita (30years)

Bangladesh 26.49% 0.31
Burkina Faso 61.95% 0.12
Burundi 80.05% 0.22
Comoros 16.64% 0.32
Lesotho 46.14% 1.11
Malawi 69.11 0.08
Mali 61.91% 0.12
Mauritania 17.87 0.53
Nepal 38.71% 0.19
Niger 66.22% 0.07
Senegal 45.59% 0.49
Tanzania 66.12% 0.13
Togo 52.87% 0.29
Uganda 53.77% 0.08
Zambia 55.93% 0.25

Variables Selection
Dependent Variable 
The logarithm form of  the poverty rate (LnPov) has been 
selected as the dependent variable. The poverty rate in 
this specific case, as defined by the United Nations, is the 
share of  the population living on less than 1.90 us dollars 
per day qualified as extreme poverty. The poverty rate has 
been added as a key measurement for poverty alleviation 
observation. Measuring global poverty has difficulties, 
such as the difference in price levels in different countries. 
In order to understand how consumption levels differ 
from country to country, it is mandatory to adjust for 
differences in purchasing power which are also associated 
with differences in market exchange rates between 
countries (Purchasing Power Parity adjustment PPP). As 



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exception of  social security, is primarily borne by the 
central, state/regional, and local government departments, 
while social insurance plans are excluded from this 
category. This refers to entities under government units’ 
control and is primarily funded through non-market and 
non-profit means, as defined by the OECD.

Birth Rate
The term “crude birth rate” denotes the annual number 
of  live births per 1000 individuals in a given population. 
The computation of  the natural growth rate involves the 
deduction of  the crude mortality rate from the crude 
birth rate, resulting in the population change rate in the 
absence of  immigration (World Bank).

Household Final Consumption 
Refers to the market value of  household goods and 
services includes enduring items like cars, laundry, 

and computers. It excludes real estate purchases 
and government payments for licenses and permits. 
This indicator also includes non-profit institutions’ 
expenditures. (World Bank).

The Level of  Opening up
Is the sum of  exports and imports of  a country accounts 
for its share of  GDP.

Education Level
The gross enrolment ratio measures the proportion 
of  enrollment in secondary education, which is 
the next stage after primary school. It serves as the 
foundation for basic education and personal growth. 
Secondary education aims to provide discipline and 
skill development through highly qualified instructors, 
ensuring enduring education and personal growth. 
(World Bank).

Table 2: List of  the different variables
Variables Log form Measure units Sources
Poverty rate LnPov Headcount ratio at 1.90 USD a day Global Extreme Poverty
Carbon emission per capita LnCo2 Metric tons Global Carbon Project
Gini index LnGini Gini index 100 World Income Inequality 

Database 
Agriculture's annual growth rate LnAgri Total percentage World Bank WDI
Electricity access LnElec Total Percentage World Bank WDI
Mortality rate LnDeath Crude per 1000 people World Bank WDI
Birth rate LnBirth Crude per 1000 people World Bank WDI
Health expenditure LnHeal Percentage of  GDP World Bank WDI
Households final consumption LnHouse USD World Bank WDI
Level of  openness LnTrade Share of   imports and exports in GDP Penn World Table 10.0
Education level LnSchool Gross enrolment ratio World Bank WDI

Specification of  the Econometric Model
Integrating econometric models in the research is essential 
to determining the association between CO₂ emissions 
reduction and poverty in LDCs. An econometric model 
consists of  two necessary parts: equations derived from 
economic theory and a set of  variables applied to the 
equation (Maciejewski & Wach, 2019). Econometrics 
has been selected since it enables the investigation of  
real-world phenomena of  the association between CO₂ 
emissions reduction and poverty. The study has selected 
the regression model as the most appropriate econometric 
model. It is one of  the simplest econometric models for 
researchers (Werth & Sigman, 2021). Regression is an 
effective tool for determining the relationship between 
the variables in a data set (Kibria & Lukman, 2020). These 
variables are mapped on a curve, which is essential for 
modeling and analyzing the data. The model establishes 
a relationship between the direct and indirect variables 
in the research (Rajabov & Mustafakulov, 2020). The key 
independent variable is CO₂ emissions per capita, while 
the dependent variable is the poverty rate. 
The three main regression models for panel data are the 

Pooled Ordinary Least Square, Fixed effect, and Random 
Effects models. The right model selection has been 
decided according to the results of  the different tests. In 
this study, the Hausman test and the Breuch and Pagan 
Lagrangian Multiplier test were conducted. According 
to the P-value Hausman test (0.0119) and the L.M. test 
(0.0000) of  the respective test, the fixed effect model has 
been selected as the most accurate for this research. The 
equation of  the fixed effect model is:
LnPovit= αi+β1 LnCo2it+ β2 LnGiniit+β3 LnAgriit+β4 
LnElecit+β5 LnBirthit+β6 LnDeathit+β7 LnHealit+β8 
LnHouseit+β9 LnTradeit+β10 LnSchoolit+εit                  (1)                                
Where i represents entity, t represents time, αi (i=1 ::: 
n) is the unknown intercept for each entity (n entity-
specific intercepts), Yit is the dependent variable LnPov; 
Xit represents the independent and control variables; βi 
is the coefficient for respective independent and control 
variables; and εit is the error term.
In order to verify the veracity of  energy consumption as 
the driving mechanism of  carbon emissions in LDCs, the 
following intermediary effect test model is constructed 
referencing to the methodology introduced by Wen Zhonglin 



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Table 3: Descriptive statistics
Variable Obs  Mean Std. Dev.  Min  Max
Poverty rate 450 50.628 22.059 4.782 87.11

CO₂  per capita 455 .25 .253 .02 1.535

Gini index 450 43.788 10.071 25.876 65.756
Agriculture's annual growth rate 463 2.983 9.122 -33.071 68.112
Electricity access 395 27.352 22.056 .53 96.2
Birth rate 465 39.145 7.847 17.549 55.485
Death rate 465 11.621 4.275 5.481 22.97
Expenditure on health 300 5.163 1.974 2.064 11.579
Households final consumption 385 4.595e+10 9.577e+10 6.000e+08 6.200e+11
Openness 450 56.904 32.008 18.972 201.331
School enrollment 310 30.234 17.601 5.221 85.522

and Ye Baojuan (Wen Zhonglin & Ye Baojuan, 2014):
                 (2)
                 (3)
                 (4)
Where LnCo2it represents the dioxide carbon 
emissions,LnPov represents the poverty rate, LnElec 

represents the mediation variable, control represents the 
control variables, t represents the year, μi  the individual 
regional effect, λt  the time effect, and εit  the random error 
terms.

Descriptive Statistics

Table 4: Benchmark estimation results
Variables (1) (2) (3)
LnCo2 -0.795*** -0.674*** -0.521***

(0.184) (0.0517) (0.111)
LnGini 0.0103 0.155

(0.124) (0.215)
LnAgri -0.00414 -0.00618

(0.00657) (0.00811)
LnElec -0.0951*** -0.0187

(0.0308) (0.0940)
LnBirth 2.669***

(0.571)
LnDeath -1.256***

(0.268)
LnHeal -0.128

(0.114)
LnHouse -0.324

(0.273)
LnTrade -0.103

(0.126)

RESULTS AND DISCUSSION
Benchmark Estimation Results
Fixed effects regression is a method that allows the item 
to be linked with observed self-reliant things. It limits 
selection bias in the association by minimizing massive 
incidences of  variation (Mummolo & Peterson, 2018). 
A fixed effect model involves the independent variable 
being constant. Meanwhile, the dependent variable 
will shift based on the fluctuations of  the independent 
variables. Model (1) is the regression outcome without 

any control variables, model (2) is composed of  three 
control variables, and model (3) represents the return 
result containing all control variables. The outcomes in 
Table 4 below display that the per capita CO₂ coefficient 
is -0.52 (model 3); therefore, the CO₂ per capita increase 
reduces the poverty rate. All control variables are negative 
coefficients except for the Gini index and birth rate. The 
p-value of  CO₂ per capita is 0.000, and its range is less 
than 0.05. Therefore, the correlation between per capita 
CO₂ and the poverty rate is very significant.



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Table 5: Robustness and 2sls regression 
Variables (4) (5) (6) (7)
LnCo₂ 0.392*** -0.193**

(0.0352) (0.0841)
LnGini -0.158* 0.188 -0.217** 1.259***

(0.0844) (0.143) (0.101) (0.278)
LnElec 0.428*** -0.323*** 0.520*** -0.465***

(0.0210) (0.0265) (0.0187) (0.0675)
LnAgri 0.00516 -0.00356 0.00510 -0.0108

(0.00447) (0.00720) (0.00509) (0.0139)
LnBirth 1.467***

(0.192)
LnDeath 0.537***

(0.181)
LnHeal -0.00300

(0.128)
LnHouse 0.261***

Robustness and Endogeneity Test
This study’s dependent and key independent variables are 
poverty rate and carbon emissions per capita, respectively. 
In order to check the robustness of  the results, we use an 
alternative variable and rerun the regression model. We 
use the gross domestic product (LnGdp) and per capita 
ecological footprint (LnEco) as alternative variables to 
run the model. 
The ecological footprint measures resource consumption 
and waste generation in relation to nature’s ability 
to absorb them. It calculates total requirements for 
biologically productive areas, such as cropland, cotton, 
and forests, and converts them into standardized units 
called global hectares. This aggregate Ecological Footprint 
is equivalent to the sum of  the global hectares needed to 
sustain an individual. 
Our variable replacement method is divided into three 
phases. The first phase implies the substitution of  only 
the dependent variable; the second phase implies the 
substitution of  the key independent variable. The last 
phase implies the substitution of  both dependent and 
independent variables. In model (4), the dependent 
variable (LnPov) has been replaced by the GDP (LnGdp). 
The result of  model (4) shows that an increase in 

carbon emissions boosts the economy. In model (5), the 
independent variable (LnCo₂) has been replaced by the 
ecological footprint per capita (LnEco).
Moreover, the result shows that an increase in (LnEco) 
leads to decreased poverty (LnPov). In model (6), the 
poverty rate (LnPov) and the carbon emissions per 
capita (LnCo₂) have been both replaced by (LnGdp) and 
(LnEco), respectively. The result shows a positive effect 
on GDP which confirms our first finding. Therefore, our 
results are robust. It is to be noted that the data (LnGdp) 
and (LnEco) variables are from the World Bank database. 
The results obtained from this regression confirm the 
robustness of  our first result. Since LnEco has a positive 
impact on LnGdp, the increase of  LnEco contributes to 
poverty reduction, which has been supported by some 
researchers on economic growth and poverty reduction, 
such as Roemer and Gugerty (Roemer & Gugerty, 1997).
In order to avoid the endogenous problem of  variables, 
referring to Coles  (Coles et al., 2008),  McKnight and Weir 
(Mcknight & Weir, 2009), we conducted a 2sls regression 
model using the lagged value of  endogenous variables 
as the instrument variable. The coefficients of  our key 
independent variables are still negative and significant 
(model 7).

LnSchool -0.143
(0.138)

Constant 2.341*** 2.771*** 4.261
(0.334) (0.494) (7.967)

Observations 441 371 177
R-squared 0.514 0.547 0.736
LM test Prob > chibar2 = 0.0000
Hausman test Prob>chi2 = 0.0009
Number of  cid 15 15 13

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1



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Table 6: Intermediary analysis results
Variables (8) (9) (10)
LnCo₂ -0.538*** 0.00432 -0.517***

(0.0669) (0.0879) (0.0605)
LnElec 0.125***

(0.0436)
Constant -12.08*** -17.42*** 6.382

(2.730) (5.522) (3.877)
Bootstrap test coef coef  = -0.08, p<0.000
Confidence interval (-. 130208 -. 0431908)
F.E. Yes Yes Yes
Control variable Yes Yes Yes
Control time Yes Yes Yes
Observations 331 297 297
R-squared 0.714 0.813 0.790
Number of  cid 14 14 14

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Intermediary Effect Analysis
The following are the basic steps of  the intermediary 
effect analysis proposed by Baron and Kenny (Baron & 
Kenny, 1986). Mediation analysis included three sets of  
regression: X → Y, X → M, and X+M → Y. In Table (6), 
we examine the association between energy consumption 
using electricity consumption as a measurement and 
poverty rate. After controlling the year, LnElec (electricity 
consumption) is selected as the mediation variable. The 
regression results confirm a mediating effect between 
electricity consumption and poverty because both have 
significant coefficients. Indeed, after running the bootstrap 
test (coef  = -0.08, p<0.000) and with a confidence interval 
of  (-. 130208 -. 0431908), a significant mediator effect was 
found between LnElec and LnPov (see table 6).

emissions reduction policies can substantially affect 
low-income households, given their potential inability 
to compensate for any reduction in income resulting 
from such measures. LDCs register diminished socio-
economic indicators and low human development 
index ratings. CO₂ emissions are a critical indicator of  
environmental degradation and conservation. The list 
of  LDCs was sourced from the World Bank, a reliable 
data source on the economic potential of  different 
regions. The validity and reliability of  the study were 
also considered and determined to be high. This 
outlook underlined the study as a competent source of  
information on the topic.
The research utilized panel data from 15 LDCs from 
1990 to 2020, and a fixed effect regression model was 
run. The study finds that:
(1) A significant association between carbon emissions 
and poverty rates in LDCs. Carbon emissions reduction 
aggravates poverty in LDCs through energy consumption 
reduction policies. (2) The research indicates an inverse 
relationship between CO₂ emission and poverty rate. 
Its finding shows that poverty alleviation can occur by 
setting up viable economic opportunities for people in 
LDCs. (3) Energy consumption is a driving mechanism 
of  CO₂ emission in LDCs.
LDCs depend extensively on fossil fuels for their energy 
needs, and reducing emissions would mean reduced 
demand for these fuels. That, in turn, would lead to 
less investment in these countries and higher energy 
prices, which would disproportionately impact people 
experiencing poverty.

Policy Implications
The Outcomes Mentioned Above Hold Significant 
Policy Implications for LDCs
First, poverty alleviation can occur by setting up 
viable economic opportunities for people in LDCs. 
Opportunities can be implemented with little consequence 
to the CO₂ emissions. However, a concern arises when 
poverty alleviation activities are implemented without 
regard for the resulting impact on the climate. While the 
attempts to reduce the endemic poverty in these regions 

(0.0357)
LnTrade -0.152

(0.165)
LnSchool 0.541***

(0.0980)
LnEco -0.616*** 0.380***

(0.126) (0.0891)
Constant 22.92*** 4.082*** 22.06*** -13.78***

(0.336) (0.552) (0.391) (1.911)
Observations 371 365 365 177
R-squared 0.816 0.390 0.730 0.769
Number of  cid 15 15 15

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

CONCLUSIONS 
As nations globally endeavor to diminish their carbon 
emissions to counteract climate change, they must 
consider the possible adverse impacts of  such policies 
on poverty alleviation. The implementation of  carbon 



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are justified, they must be done sustainably. Organizations 
such as the U.N. and the World Bank need to consider 
investing in projects that enhance the earning potential 
of  the citizens in LDCs without causing severe damage 
to the climate of  those regions.
Second, the “Green Structural Transformation.” 
Researchers like the Chinese scholar Justin Yifu pointed 
out the concept of  structural transformation as a main 
way for LDCs to end poverty and engage in economic 
development. The LDCs are in a “traditional economy” 
and “dual structure” where agriculture dominates, the 
transition period to initial industrialization. We must first 
increase the labor productivity of  agriculture and solve 
the fundamental survival problem of  “sufficiency.” Then, 
we must liberate the labor force from agriculture and 
transfer it to the labor-intensive processing industry. At 
the same time, we must also promote urbanization and 
build export processing zones to improve infrastructure 
and transportation conditions. This series of  structural 
transformations, if  there is no role of  the government 
and only relies on the market mechanism, will either take 
a long time or will not happen at all. However, it is to be 
noted that for the sake of  climate change, the economic 
structural transformation must change its traditional 
application to a more “green” application called “Green 
Structural Transformation.”
Third, the pursuit of  CO₂ reduction activities must be 
strengthened within the more developed countries since 
they have low poverty levels. It is imperative for developed 
nations to allocate resources toward sustainable energy 
sources as a means of  mitigating CO₂ emissions. In the 
future, LDCs shall bear more burden of  carbon emission 
reduction, but for now, it will negatively impact their 
economies and people. Two-thirds of  the population of  
LDCs need access to electricity, particularly in Africa. The 
African continent, where most less advanced countries are 
located, consumes less energy than a developed country 
like Spain (Koffi Annan, 2015).

REFERENCE
Aghion, P., & Howitt, P. (1995). Technical progress in 

the theory of  economic growth. Economics in a 
Changing World: Volume 5 Economic Growth and 
Capital and Labour Markets, 101-122.

Blomquist, J., & Westerlund, J. (2013). Testing slope 
homogeneity in large panels with serial correlation. 
Economics Letters, 121(3), 374-378.

Charlier, D., & Legendre, B. (2021). Fuel poverty in 
industrialized countries: Definition, measures and 
policy implications a review. Energy, 236, 121557. 
https://doi.org/10.1016/j.energy.2021.121557

Coase, R. H. (1960). Law economics. Journal of  Law and 
Economics, 3, 1-44.

Datt, G., & Ravallion, M. (1992). Growth and 
redistribution components of  changes in poverty 
measures: A decomposition with applications to 
Brazil and India in the 1980s. Journal of  Development 
Economics, 38(2), 275-295.

Fosu, A. K. (2017). Growth, inequality, and poverty 
reduction in developing countries: Recent global 
evidence. Research in Economics, 71(2), 306-336.

Fu, R., Jin, G., Chen, J., & Ye, Y. (2021). The effects 
of  poverty alleviation investment on carbon 
emissions in China are based on the multiregional 
input-output model. Technological Forecasting and 
Social Change, 162, 1-35. https://doi.org/10.1016/j.
techfore.2020.120344

Gassebner, M., Gaston, N., & Lamla, M. J. (2008). 
Relief  for the environment? The importance of  an 
increasingly unimportant industrial sector. Economic 
Inquiry, 46(2), 160-178.

Georgescu-Roegen, N. (1986). The entropy law and the 
economic process in retrospect. Eastern Economic 
Journal, 12(1), 3-25.

Sullivan, Dylan, and Jason Hickel. (2023). Capitalism 
and extreme poverty: A global analysis of  real wages, 
human height, and mortality since the long 16th 
century. World Development, 161, 106026.

Grunewald, N., Klasen, S., Martínez-Zarzoso, I., & Muris, 
C. (2017). The trade-off  between income inequality 
and carbon dioxide emissions. Ecological Economics, 
142, 249-256.

Guo, L. (2014). CO 2 emissions and regional income 
disparity: Evidence from China. The Singapore Economic 
Review, 59(01), 1450007.

Helmers, E., Chang, C. C., & Dauwels, J. (2021). Carbon 
footprinting of  universities worldwide: Part I—
objective comparison by standardized metrics. 
Environmental Sciences Europe, 33(1), 1-25. https://link.
springer.com/article/10.1186/s12302-021-00454-6

Ifeoma, A. R., Purity, N. O., & Yusuf, A. E. (2018). Effect of  
entrepreneurship development on poverty alleviation 
in Nigeria. Journal of  Business and Management, 20(2), 80-
87. https://www.researchgate.net/profile/Rita Anekwe/
publication/328492559_Effect_of_Entrepreneurship_
Development_on_Poverty_Alleviation_in_Nigeria/
links/5bd0e8a445851537f598f367/Effect-of-
Entrepreneurship-Development-on-Pover ty-
Alleviation-in-Nigeria.pdf

Jin, G., Guo, B., & Deng, X. (2020). Is there a decoupling 
relationship between CO2 emission reduction and 
poverty alleviation in China? Technological Forecasting 
and Social Change, 151, 119856-119877. https://doi.
org/10.1016/j.techfore.2019.119856

Karpman, M., Zuckerman, S., Gonzalez, D., & Kenney, 
G. M. (2020). The COVID-19 pandemic is straining 
families’ abilities to afford basic needs. Washington, 
DC: Urban Institute, 500-567. https://www.urban.
org/sites/default/files/publication/102124/the-
covid-19-pandemic-is-straining-families-abilities-to-
afford-basic-needs_2.pdf

Khatun, F., & Al Amin, M. (2016). Carbon emission, 
energy consumption, deforestation and agricultural 
income in LDCs: lessons for post-2015 development 
agenda. In Southern Perspectives on the Post-2015 
International Development Agenda (pp. 108-122). 



Pa
ge

 
49

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

Am. J. Environ Econ. 2(1) 37-51, 2023

Routledge.
Kibria, B. M., & Lukman, A. F. (2020). A new ridge-type 

estimator for the linear regression model: Simulations 
and applications. Scientifica, 2020, 1-13. https://doi.
org/10.1155/2020/9758378

Kraay, A. (2006). When is growth pro-poor? Evidence 
from a panel of  countries. Journal of  Development 
Economics, 80(1), 198-227.

Kumar, R., Kumar, R., & Prakash, O. (2019). Chapter-5 The 
Impact of  Chemical Fertilizers on Our Environment 
and Ecosystem. Chief  Ed, 35, 69-77. https://
www.researchgate.net/profile/Randeep-Kumar/
publication/331132826_The_Impact_of_Chemical_
Fertilizers_on_our_Environment_and_Ecosystem/
links/5c66ebe492851c1c9de446eb/The-Impact-
of-Chemical-Fertilizers-on-our-Environment-and-
Ecosystem.pdf

Kynčlová, P., Upadhyaya, S., & Nice, T. (2020). 
Composite index as a measure of  achieving 
Sustainable Development Goal 9 (SDG-9) industry-
related targets: The SDG-9 index. Applied Energy, 
265, 114755-114794. https://doi.org/10.1016/j.
apenergy.2020.114755

Lakner, C., Mahler, D. G., Negre, M., & Prydz, E. B. 
(2022). How much does reducing inequality matter 
for global poverty? The Journal of  Economic Inequality, 
1-27. https://link.springer.com/article/10.1007/
s10888-021-09510-w

Leal Filho, W., Balogun, A. L., Olayide, O. E., Azeiteiro, 
U. M., Ayal, D. Y., Muñoz, P. D. C., & Li, C. (2019). 
Assessing the impacts of  climate change in cities 
and their adaptive capacity: towards transformative 
approaches to climate change adaptation and 
poverty reduction in urban areas in a set of  
developing countries. Science of  the Total Environment, 
692, 1175-1190. https://doi.org/10.1016/j.
scitotenv.2019.07.227

Maciejewski, M., & Wach, K. (2019). What determines 
export structure in the E.U. countries? The use of  
the gravity model in international trade is based on 
the panel data for the years 1995-2015. Journal of  
International Studies, 12(1), 1-26.https://www.ceeol.
com/search/article-detail?id=787135

Malerba, D. (2020). The trade-off  between poverty 
reduction and carbon emissions, and the role of  
economic growth and inequality: an empirical cross-
country analysis using a novel indicator. Social Indicators 
Research, 150(2), 587-615. https://link.springer.com/
article/10.1007/s11205-020-02332-9

Marcil, L. E., Hole, M. K., Jackson, J., Markowitz, M. A., 
Rosen, L., Sude, L., & Vinci, R. J. (2021). Anti-poverty 
medicine through medical-financial partnerships: 
a new approach to child poverty. Academic Pediatrics, 
21(8), S169-S176. https://doi.org/10.1016/j.
acap.2021.03.017

Meng, J., Mi, Z., Guan, D., Li, J., Tao, S., Li, Y., & Davis, S. 
J. (2018). The rise of  South–South trade and its effect 
on global CO₂ emissions. Nature Communications, 9(1), 

1-7. https://www.nature.com/articles/s41467-018-
04337-y

Mummolo, J., & Peterson, E. (2018). Improving the 
interpretation of  fixed effects regression results. 
Political Science Research and Methods, 6(4), 829-835.

Nambiar, E. S. (2021). Small forest growers in tropical 
landscapes should be embraced as partners for 
Green-growth: Increase wood supply, restore land, 
reduce poverty, and mitigate climate change. Trees, 
Forests and People, 6, 1-21. https://reader.elsevier.
com/reader/sd/pii/S2666719321000935?token=5B
C3ED68982B9D90ED20F036B63842E546C37DA
29F71552BDA20412087A60C3EDA9CF6CE1044
B07A4E03F0BC445F7B5F&originRegion=eu-west-
1&originCreation=20220717133317

Ndonga, D., Laryea, E., & Chaponda, M. (2020). Assessing 
the potential impact of  the African continental free 
trade area on LDCs: A case study of  Malawi. Journal 
of  Southern African Studies, 46(4), 773-792. https://doi.
org/10.1080/03057070.2020.1767888

Obaideen, K., Shehata, N., Sayed, E. T., Abdelkareem, M. 
A., Mahmoud, M. S., & Olabi, A. G. (2022). The Role 
of  Wastewater Treatment in Achieving Sustainable 
Development Goals (SDGs) and Sustainability 
Guidelines. Energy Nexus, 100112.https://doi.
org/10.1016/j.nexus.2022.100112

Pareto, V. (2014). Manual of  political economy: a critical 
and variorum edition. OUP Oxford.

Pawlak, K., & Kołodziejczak, M. (2020). The role of  
agriculture in ensuring food security in developing 
countries: Considerations in the context of  the 
problem of  sustainable food production. Sustainability, 
12(13), 5488-5501. https://doi.org/10.3390/
su12135488

Pesaran, M. H., & Yamagata, T. (2008). Testing slope 
homogeneity in large panels. Journal of  Econometrics, 
142(1), 50-93.

Pigou, A. C. (1920). The report of  the Royal Commission 
on the British income tax. The Quarterly Journal of  
Economics, 34(4), 607-625.

Rajabov, N., & Mustafakulov, S. I. (2020). Econometric 
analysis of  the impact of  the investment climate on 
the sustainability of  socio-economic development 
of  the Navoi region. Science Web, 1-17. https://
tsue.scienceweb.uz/index.php/archive/article/
download/3776/2785

Ravallion, Martin, Mark Heil, and Jyotsna Jalan. (2000). 
Carbon emissions and income inequality. Oxford 
Economic Papers, 52.4, 651-669.

Rob, V. O. S., & Cattaneo, A. (2021). Poverty reduction 
through the development of  inclusive food value 
chains. Journal of  Integrative Agriculture, 20(4), 964-978. 
https://doi.org/10.1016/S2095-3119(20)63398-6

Russell, Stephen. (2014). World Resources Institute. 
www.wri.org, 29 May 2014, www.wri.org/insights/
everything-you-need-know-about-agricultural-
emissions.

Sarker, B., Keya, K. N., Mahir, F. I., Nahiun, K. M., Shahida, 



Pa
ge

 
50

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

Am. J. Environ Econ. 2(1) 37-51, 2023

S., & Khan, R. A. (2021). Surface and groundwater 
pollution: causes and effects of  urbanization and 
industrialization in South Asia. Scientific Review, 7(3), 
32-41. https://doi.org/10.32861/sr.73.32.41

Sarlo, C. A. (2019). The causes of  poverty. Fraser Institute.
Sherman, A., Zippel, C., Gordon, R., & Trisi, D. (2021). 

Widespread Economic Insecurity Pre-Pandemic 
Shows Need for Strong Recovery Package. CBPP, 14, 
1-14. https://www.cbpp.org/sites/default/files/7-
14-21pov.pdf

Stefanakis, A. I. (2019). The role of  constructed wetlands 
as green infrastructure for sustainable urban water 
management. Sustainability, 11(24), 6981-7007. 
https://doi.org/10.3390/su11246981

Swamy, P. A. (1970). Efficient inference in a random 
coefficient regression model. Econometrica: Journal of  
the Econometric Society, 311-323.

Uddin, M. M., Mishra, V., & Smyth, R. (2020). Income 
inequality and CO₂ emissions in the G7, 1870–2014: 
Evidence from non-parametric modeling. Energy 
Economics, 88, 104780.

Tripathi, A., & Pandey, M. (2022). Climate Change: It’s 
Causes, Inflicted Hazards, Adopted Strategies and 
Opportunities in Agriculture of  Nepal: A Detailed 
Review. American Journal of  Environmental Economics, 
1(1), 1-12.

Roemer, M., & Gugerty, M. K. (1997). Does economic 
growth reduce poverty? Cambridge, MA: Harvard 
Institute for International Development.

Baron, R. M., & Kenny, D. A. (1986). The moderator–
mediator variable distinction in social psychological 
research: Conceptual, strategic, and statistical 
considerations. Journal of  Personality and social psychology, 
51(6), 1173.

Bommae Kim. (2016). Introduction to Mediation 
Analysis. The University of  Virginia Library. Retrieved 
from https://data.library.virginia.edu/introduction-
to-mediation-analysis/

Lin Yifu. (2014). New Structural Economics: Rethinking 
the theoretical framework of  economic development 
and policy. Peking University Press.

United Nations Conference on Trade and Development 
(UNCTAD). (2015). LDCs Report 2015: 
Transforming Rural Economies. United Nations 
Publications.

World Bank. (2020). Energy & Extractives. World Bank.
International Renewable Energy Agency (IRENA). 

(2017). Renewable Energy and Jobs - Annual Review 
2017. IRENA.

United Nations Development Programme (UNDP). 
(2018). Unlocking the Potential of  Frontier Markets 
Through Renewable Energy. UNDP.

United Nations. (2015). Transforming our world: the 
2030 Agenda for Sustainable Development. United 
Nations.

International Energy Agency (IEA). (2020). Sustainable 
Recovery. IEA.

Sabroso, L. M., Duyo, J. M. A., & Salapa, A. C. (2023). 

Unraveling the Intricate Nexus of  Philippine 
Environment and Economy: An Empirical Analysis 
Using Multiple Regression. American Journal of  
Environmental Economics, 2(1), 24-28.

José, Juan, Vázquez., Sonia, Panadero., Isabel, Pascual., 
Xavier, G., Ordoñez. (2017). Causal attributions of  
poverty in LDCs: Comparing among undergraduates 
from nations with different development levels. 
Interamerican Journal of  Psychology, 51(1), 29-43. https://
doi.org/10.30849/RIP/IJP.V51I1.109

Badreldin, Mohamed, Ahmed. (2014). Human Poverty: 
a practical and analytical study on LDCs. Arab 
Economic and Business Journal, 9(1), 1-11. https://doi.
org/10.1016/J.AEBJ.2012.05.001

Gore, Charles. (2003). Globalization, the International 
Poverty Trap and Chronic Poverty in the Least 
Developed Countries. CPRC Working Paper 
No. 30. Available at SSRN: https://ssrn.com/
abstract=1754435 or http://dx.doi.org/10.2139/
ssrn.1754435

Castañeda, Raúl et al. (2016). Who are the Poor in the 
Developing World? Development Economics: 
Microeconomic Issues in Developing Economies 
eJournal. Retrieved from SSRN: https://ssrn.com/
abstract=2767714

Beegle, Kathleen G. et al. (2013). The state of  the poor: 
where are the poor, where is extreme poverty harder 
to end, and what is the current profile of  the world’s 
poor? World Bank Publications, 1-8.

Mahembe, Edmore and Nicholas M. Odhiambo. (2018). 
The Dynamics of  Extreme Poverty in Developing 
Countries. Studia Universitatis „Vasile Goldis” Arad 
– Economics Series, 28, 18-35.

Hoeller, Peter et al. (2005). MACROECONOMIC 
IMPLICATIONS OF REDUCING GREENHOUSE 
GAS EMISSIONS: A SURVEY OF EMPIRICAL 
STUDIES.

Zhang, Xingping et al. (2019). Economic growth, energy 
consumption, and carbon emission nexus: fresh 
evidence from developing countries. Environmental 
Science and Pollution Research, 26, 26367-26380.

Hanif, Imran et al. (2019). Carbon emissions across the 
spectrum of  renewable and nonrenewable energy use 
in developing economies of  Asia. Renewable Energy, 
143, 586-595.

Hu, Hui et al. (2018). The role of  renewable energy 
consumption and commercial services trade in carbon 
dioxide reduction: Evidence from 25 developing 
countries. Applied Energy, 211, 1229-1244.

Wang, Kai et al. (2011). Carbon emission patterns in 
different income countries. International Journal of  
Energy and Environment, 2, 447-462.

Grandjean, A., Blanchet, E., & Finidori, E. (2014). Study 
of  4 trajectories of  the DNTE. A pedagogic view 
of  the 4 trajectories studied within the frame of  the 
National Debate on Energy Transition; Etude des 4 
trajectoires du DNTE. Une vision pedagogique des 4 
trajectoires etudiees dans le cadre du Debat National 



Pa
ge

 
51

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

Am. J. Environ Econ. 2(1) 37-51, 2023

sur la Transition Energetique.
Ewane, E. B., & Ewane, E. I. (2023). Foreign Direct 

Investment, Trade Openness and Environmental 
Degradation in SSA Countries. A Quadratic Modeling 
and Turning Point Approach. American Journal of  
Environmental Economics, 2(1), 9-18.

Wen Zhonglin & Ye Baojuan (2014). Analysis of  
intermediary effect: Methods and Model Development. 
Progress in Psychological Science, 22(5), 731-745.

Werth, J., & Sigman, M. S. (2021). Linear Regression 
Model Development for Analysis of  Asymmetric 
Copper-Bisoxazoline Catalysis. ACS catalysis, 11(7), 
3916-3922. https://pubs.acs.org/doi/abs/10.1021/

acscatal.1c00531
Wolla, S. A., & Sullivan, J. (2017). Education, income, and 

wealth. Page One Economics®.
World Population Review. (2022). Poverty rate by 

country in 2022. World Population Review. https://
worldpopulationreview.com/country-rankings/
poverty-rate-by-country

Wu, J., & Si, S. (2018). Poverty reduction through 
entrepreneurship: Incentives, social networks, and 
sustainability. Asian Business & Management, 17(4), 
243-259. https://link.springer.com/article/10.1057/
s41291-018-0039-5


