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Promoting sustainability in developing Countries: A Machine Learning-
based approach to understanding the relationship between green
investment and environmental degradation
Khatib Ahmad Khan
a,b
, Muhammad Khalid Anser
a
, Fahrettin Pala
c
, Abdulkadir Barut
d,f
,
Muhammad Wasif Zafar
e,
⇑
a
School of Business, Xi’an International University, Xi’an, China
b
School of Oriental Studies, Xi’an International Studies University, Xi’an, China
c
Kelkit Aydın Dog
˘an Vocational School, Gümüsßhane University, Turkey
d
Siverek Vocational School, Department of Accounting and Taxation, Harran University, Sanliurfa, Turkiye
e
Ripah School of Business and Management, Riphah International University, Lahore, Pakistan
f
MEU Research Unit, Middle East University, Amman, Jordan
article info
Article history:
Received 14 October 2023
Revised 22 February 2024
Accepted 6 March 2024
Available online 24 April 2024
Handling Editor: A. Sinha
Keywords:
Green investment
Environmental Degradation
Entropy method
Machine learning
EKC
abstract
The main objective of this study is to examine the impact of green investment on environmental degra-
dation in developing countries using machine learning-based estimation combined with robustness tests
of static and dynamic panel data modeling techniques. The scope of this study covers 30 developing
countries for 2009–2019. This study introduces a new index of environmental degradation that uses
the entropy method and includes green gas emissions and deforestation. The study addresses trade open-
ness, the quadratic shape of economic growth, and urbanization in the context of the Environmental
Kuznets Curve Hypothesis (EKC) and the Ecological Modernization Theory (EMT), in addition to green
investment. This study considers the kernel-based regularized least squares (KRLS) approach, the static
panel technique Driscoll & Kraay standards error method, and a dynamic panel technique system gener-
alized moment techniques. The empirical findings from the machine learning method show that green
investment significantly reduces environmental degradation with a higher coefficient resulting from
the static fixed effect estimation. The study also reveals that the main hypotheses, such as EKC and
EMT, are confirmed by all estimation techniques. Based on the results, the study recommends that pol-
icymakers take pragmatic steps toward green investments and increase the financing of green energy ini-
tiatives to combat environmental degradation.
Ó2024 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.
1. Introduction
Greenhouse gas emissions (GHGE) have increased dramatically
in the last several years, and CO2 emissions have increased about
100 times faster compared to the last age of ice (Shang and Luo,
2021). Therefore, to achieve the Sustainable Development Goals
(SDGs), nations across the globe are continually striving to address
climate change crisis, including reducing emissions and enhancing
ecological standards (Duyen et al., 2023). Countries have signed
three aggrements such the Kyoto Protocol, United Nations Frame-
work Convention on Climate Change as well as Paris Agreement to
combat climate warming and provide a global climate system of
governance worldwide (Hu et al., 2023. Hence, investigations into
environmental deterioration and its interplay with various deter-
minants have attracted significant scholarly interest. ED or envi-
ronmental degradation is measured mainly via GHGE or carbon
dioxide emissions in empirical literature, whereas other measures
have largely been ignored (Anwar et al., 2023; Barut et al., 2023; Li
et al., 2023). In this study, the focus is on both GHGE and deforesta-
tion, which are used for constructing a comprehensive ED index.
Deforestation has become a serious environmental concern on a
https://doi.org/10.1016/j.gr.2024.03.013
1342-937X/Ó2024 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.
Abbreviations: SDGs, Sustainable Development Goals; EMT, Ecological Modern-
ization Theory; EKC, Environmental Kuznets Curve; ED, Environmental Degrada-
tion; URB, Urbanization; GDPPC, GDP per capita; TOP, Trade Openness; HC, Human
Capital; GIN, Green Investment; GHGE, Greenhouse Gas Emissions; KRLS, Kernel
Regularized Least Squares; GMM, Generalized method of moments; MMQREG,
Moments and Methods of Quantile Regression; STIRPAT, Stochastic Impacts of
Regression on Population, Affluence, and Technology.
⇑
Corresponding author at: Ripah School of Business and Management, Riphah
International University, Lahore, Pakistan.
E-mail addresses: khatib786@snnu.edu.cn (K. Ahmad Khan), khalidsnnu@yahoo.
com (M. Khalid Anser), fahrettinpala@gumushane.edu.tr (F. Pala), kadirbarut@har-
ran.edu.tr (A. Barut), wasif.zafar6@yahoo.com (M. Wasif Zafar).
Gondwana Research 132 (2024) 136–149
Contents lists available at ScienceDirect
Gondwana Research
journal homepage: www.elsevier.com/locate/gr
global scale in recent years. Deforestation has a range of negative
impacts, such as biodiversity decline, loss of ecosystem services,
and risks associated with climate change (Van Khuc et al., 2018).
Forests have the capacity to stabilize CO
2
levels in the atmosphere
by absorbing carbon emissions. With the increase in deforestation,
this balance is disrupted, and problems such as climate change
become deeper (Njora and Yilmaz, 2022). Deforestation is triggered
by many factors such as economic activities, agricultural practices,
and urbanization. Therefore, it is necessary to focus not only on
industrial carbon emissions but also on factors that directly cause
environmental degradation, such as deforestation (Houghton,
2012). Emphasizing the importance of deforestation in studies is
a critical step toward sustainable development and environmental
protection strategies. In this context, comprehensive research is
required to understand the impacts and causes of deforestation.
Consequently, a more explicit consideration of deforestation in
environmental degradation research is critical for achieving future
environmental sustainability.
While energy takes its place in history as an important require-
ment that people need to sustain their lives, it is also used as an
indispensable input in the production process. The demand for
energy has surged greatly during the period spanning the indus-
trial revolution to the present, in tandem with the advancement
of societal welfare. Nowadays, modern industries heavily rely on
energy sources other than manpower for their production. Today,
energy demand has reached a level that has never been encoun-
tered in any period of history. The surge in the use of fossil fuels
and nuclear energy with the development of technology has also
revealed various forms of degradation while aggrevating the prob-
lem of climate warming (Zhao et al., 2023). At the onset of these
issues lies the recurrent sources of pessimism that are commonly
referred to by various monikers such as worldwide warming, cli-
mate alteration, barometrical contamination, and greenhouse
impact. Notwithstanding these issues, the surge in environmental
consciousness across the globe in recent times has given rise to a
cleaner, more sustainable environmental outlook, resulting in sub-
stantial policy changes in renewable energy by nations aimed at
mitigating environmental challenges. Projects related to environ-
mental awareness have increased, especially with the announce-
ment of the 17 sustainability goals published by the United
Nations (UN). Green investments (GIN) are one of the factors that
are effective in achieving SDG7 (affordable and clean energy),
one of the 17 sustainability goals (Heinkel et al.,2001; Inderst
et al.,2012; Eyraud et al.,2013; Demirtasßet al., 2023). Fig. 1 shows
the expenditures made worldwide for the transition to renewable
energy from 2004 to 2022. It can be seen that green investments,
which was 32 million dollars in 2004, will increase approximately
11 times and will approach 500 million dollars in 2022. Similarly, it
can be said that investments will increase by nearly 50 % in 2022
compared to 2015, when the UN announced the SDG goals.
The SDGs were announced by the UN because of global concern
about the risks of extreme weather and global warming and to
combat the crisis of climate change (Wu et al., 2023). Green invest-
ments (GIN) contribute to these goals by addressing the problem of
emissions and deforestation. GIN can help reduce the destruction
of forests for energy production, which is one of the main causes
of deforestation. While green energy projects meet the energy
demand from bioenergy and other renewable sources, they can
also contribute to the protection of forest areas and prevention of
deforestation. It is also related to green investments and SDG15.
SDG15 includes the target ‘‘Conservation, Restoration, Sustainable
Use of Terrestrial Ecosystems and Sustainable Forestry”. Green
investments can support sustainable forestry practices to reduce
the effects of deforestation and preserve biodiversity (Ogun-
wusi,2013, Parra-Domínguez,et al., 2023). Afforestation projects
and forest restoration works can strengthen the contribution of
green investments to SDG15. Additionally, green investments are
linked to SDG13. SDG13 targets ‘‘Climate Action” covers tackling
climate change. Green investments can help minimize the negative
impacts of deforestation as they focus on reducing carbon emis-
sions and investing in climate-friendly projects. By enhancing their
ability to store carbon, protected forests can lower the quantity of
carbon in the atmosphere and aid in the battle against climate
change (_
Ilbasmısßet al.,2023; Çitil et al.,2023).
The main purpose of developing countries is to ensure eco-
nomic, social, and political development and to improve the qual-
ity of life of their citizens. Since these countries often have low
income levels, inadequate infrastructure, deficiencies in education
and health services, and many other difficulties, a green invest-
ment that encourages investing in environmentally friendly pro-
jects and sustainable economic activities may not be considered
in the right projects and may lead to misapplications. Developing
countries may use low-cost non-renewable (fossil fuel) energy
sources to combat these difficulties, thereby adversely affecting
Fig. 1. Green investment worldwide from 2004 to 2020. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this
article.)
K. Ahmad Khan, M. Khalid Anser, F. Pala et al. Gondwana Research 132 (2024) 136–149
137
environmental quality. For example, the surge in the use of fossil
fuels and nuclear energy with developing technology has revealed
problems that constitute negativity, such as global warming, cli-
mate change, atmospheric pollution, and the greenhouse effect.
Since these emerging nativities threaten the whole world and
humanity, the common goal of these countries is; to increase sus-
tainable environmental quality by increasing adaptability and cli-
mate resilience against the negative effects of climate change.
However, although the energy obtained from renewable sources
in developing countries is still below the expected level, it poses
less danger in terms of ED (Khan et al., 2021). Although there are
many reasons for the low level of renewable energy in these coun-
tries, the main reasons are financial difficulties, lack of technolog-
ical infrastructure and capacity, energy policies, legal environment,
and dependence on fossil fuels.
This study has several distinct advantages over the previous
studies. First, when the literature is analyzed in detail, ED is seen
to be generally evaluated through a single variable. For example
several studies (e.g. Awosusi et al., 2023; Ahmad et al., 2023;
Numan et al., 2023; Hunjra et al., 2023; Huo et al., 2023; Lu
et al., 2023) measured ED through carbon footprint and CO2,
whereas Zambrano-Monserrate et al. (2018) used deforestation
as a measure of ED. However, in this study, GHGE and deforesta-
tion, which are the factors that affect ED the most according to
Donohoe (2003), are considered and a more comprehensive ED
index is created. This offers the potential to measure the multi-
faceted nature of ED more effectively. The majority of studies in
the literature have only concentrated on GHGE or carbon emis-
sions, and deforestation has been largely ignored. But this
approach is not holistic, as according to Lei et al. (2023), forest cov-
erage loss can be linked with erosion of soil, desertification, flash
floods, and overall climate change. Naidoo (2004) stated that
economies with endowments of higher rates of natural forests
may experience higher rates of economic development than econo-
mies with less forest reserves. Climate change can also be miti-
gated through forests as they can absorb carbon dioxide from the
atmosphere and have key roles in reducing the severity of natural
disasters and even in preventing them. Despite these benefits,
trophical deforestation is a matter of concern today in attaining
environmental sustainability (Murshed, 2022). According to Wich
et al. (2011), approximately 17 % of the worldwide GHGE comes
from deforestation each year, which further increases global
warming. As a result, deforestation needs special attention. Defor-
estation not only increases the GHGE but also contributes to loss of
biodiversity by making endgered species extinct and threatens the
ecological habitat (Lei et al., 2023). Therefore, examining both the
GHGE and deforestation at the same time offers a critical and com-
prehensive analysis of ED compared to the previous literature.
Second, while indices are generally constructed by principal
component analysis (PCA) in the literature (Barut et al., 2023; Liu
et al., 2023a; Bux et al., 2023), in this study, the index of ED is con-
structed by the entropy method. The entropy method offers advan-
tages such as considering the relationships between variables in
multiple criteria decision-making processes, flexibility, adaptabil-
ity, complexity, and the ability to manage uncertainty. Shannon
and Weaver first proposed this method in 1947, and it was further
enhanced by Zeleny in 1982. It is now the most objective method
for selecting the weights of the constructs (Wu et al., 2022). It con-
cludes that randomness and speculation issues can be resolved
through the entrophy method, which can exist in the sujective
assignment method. Moreover, it can also take care of the overload
of information among several indicator variables. It is used for
more objective and subjective weighting (Liang and Ashuka, 2022).
Third, research appears to have focused mostly on East Asian
nations, particularly China, with regard to the association between
GIN and ED (Huang and Chen, ,2021; Zhang et al., 2022; Zhou et al.,
2020). This means that the relationship between GIN and ED can-
not be fully elucidated in other country groups. In this context, this
study aims to fill this gap by considering the developing countries.
The reason for considering these developing countries is that stud-
ies mainly focus on developed countries as they have significant
investment in green finance and most activities of innovation occur
in developed nations. However, developing countries will critically
face the consequences of climate change; therefore, it is important
to see how green investment can mitigate the adverse effects of cli-
mate change in developing economies (Sharif et al., 2023). More-
over, in line with the statement of Popp (2012), it can be said
that green technologies required to mitigate adverse climate
effects in developed economies are quite different from technolo-
gies required for developing countries to address climate change.
Therefore, the policy framework suggested in studies focusing
solely on developed economies cannot be used for developing
economies, and there is a need for separate comprehensive policy
suggestions for developing economies, which are currently lacking
in empirical literature.
Fourth, this study uses several methodologies to account for the
relationship between GIN and ED. Specifically, this study uses the
KRLS method, which is a machine learning-based method. This
method is a more powerful estimator than traditional regression
methods and can capture linear and non-linear relationships. Fur-
thermore, Machado and Silva’s (2019) MMQREG methodology is
applied as a robustness study. However, none of these methods
cannot take care of endogeneity issue inherent in modeling the
relationship between GIN and ED. Therefore, this study further
considers the system GMM to take into account endogeneity.
Fifth, this study validates the theories of EKC by re-testing them
on the ED index constructed by the entropy method. This increases
the methodological reliability of the study and shows that the
results obtained are based on solid foundations. Finally, the valid-
ity of ecological modernization theory (EMT), a neglected topic in
environmental studies, was also tested. Addressing EMT provides
an important opportunity to consider various perspectives on envi-
ronmental sustainability.
2. Review of the literature
2.1. Relationship between GIN and ED
The significance of GIN was emphasized during the eleventh
assembly of the G-20 nations, which was held in Hangzhou, China
in 2016 (Liu et al., 2019; Schäfer, 2018). Consequently, this topic
garnered extensive attention and was widely disseminated and
deliberated (Akomea-Frimpong et al., 2022). GIN, as a comprehen-
sive concept, combines financial and business dimensions while
incorporating considerations for environmental repercussions. It
constitutes a framework that encompasses diverse stakeholders,
ranging from individuals, corporations, producers, and consumers
to lenders and borrowers. Unlike conventional financial operations,
GIN places a paramount emphasis on ecological benefits, focusing
on environmental preservation (Wang and Zhi, 2016). Given the
recent surge in the significance of GIN, scholarly exploration has
delved deeper into its ecological implications.
In context, extensive research has been conducted within aca-
demia to explore the environmental consequences of GIN. A sub-
stantial portion of these inquiries (Hunjra et al., 2023; Numan
et al., 2023; Bhattacharyya, 2022; Zhang et al., 2022; Khan et al.,
2022; Iqbal et al., 2021; Muganyi et al., 2021; Wang and Zhi,
2016) consistently highlight that GIN yields positive consequences
for the environment, contributing to the enhancement of environ-
mental quality. However, Huang and Chen (2021) discerned that
while GIN engenders a positive impact on the environmental qual-
K. Ahmad Khan, M. Khalid Anser, F. Pala et al. Gondwana Research 132 (2024) 136–149
138
ity of the nation where it is employed, it simultaneously exhibits a
counterproductive influence by undermining environmental
quality.
2.2. Relationship between GDPPC and ED
Economic expansion and consequent increase in revenue are
considered pivotal determinants of ED. Therefore, numerous schol-
ars have researched this subject matter, and as a result, apparently
conflicting outcomes have emerged. Wang et al.,(2022), and
Boukhelkhal (2022), discovered that economic progress amplifies
ED. However, studies conducted by Adebanjo and Shakiru (2022),
Salazar-Nunez et al. (2022), and Weimin et al. (2022) have proven
that, over an extended period, ED diminishes with the progress of
the economy. The observation of divergent findings within the lit-
erature is a direct consequence of the EKC hypothesis. The EKC pro-
poses that economic growth leads to an initial surge in ED,
followed by a subsequent decline over time. The EKC graph depicts
an upward trend of CO2 until a certain income threshold is
reached, after which a downward trend is observed, resulting in
an inverted U- shaped curve (Arouri et al., 2012). This indicates
that as income increases, carbon emissions initially rise but subse-
quently decrease over time. Ditta et al. (2023) found that income
has a decreasing effect on ED eventually. Dada et al., (2023) found
that income inequality increases ED in African countries. According
to Barut et al. (2023), the five fragile nations are valid for the EKC
theory. Yunita et al. (2023), in their study for Indonesia, found that
GDPPC increases CO2.
There are studies that support the EKC hypothesis (Liu et al.,
2023b; Wang, 2023; Jahanger et al., 2022; Shah et al., 2022;
Tenaw and Beyene, 2021; Alola and Öztürk, 2021; Dogan and
Inglesi-Lotz, 2020; Ahmad et al., 2019; Paramati et al., 2017; Al-
Mulali et al., 2015; Arouri et al., 2012; Diao et al., 2009). However,
it was found that the EKC theory is invalid by Halliru et al., 2020;
Erdogan, et al., 2020; Mikayilov et al., 2018; Aye et al., 2017;
Ahmed and Long, 2013.
Muntasir (2022) analyzed the deforestation-related EKC
hypothesis for Bangladesh by considering time series data from
1971 to 2018. The author used the rate of depletion in net forest,
rate of deforestation, and forest coverage as indicators of deforesta-
tion. The authors found the validity of EKC in terms of deforesta-
tion. Other authors include Andrée et al. (2019) for 95 nations,
Crespo Cuaresma et al. (2017) for 189 economies, Liu et al.
(2017) for Indonesia and South Korea, Joshi and Beck (2016) for
Africa, and Chiu (2012) for 52 developing economies. Motel et al.
(2009) for tropical developing nations found the existence of defor-
estation led EKC in their papers.
2.3. Relationship between TOP and ED
At the beginning of the 21st century, there has been significant
scholarly discourse and inquiry into the correlation between TOP
and ED. This discourse ensued following the recognition of the
affirmative association between TOP and economic progress, as
established by Shahbaz et al. (2017). According to Ewane and
Ewane (2023), the association between TOP and ED has a U-
shaped pattern, with short-term effects that decrease and long-
term effects that increase. According to Bashir and Javaid (2023),
TOP lowers ED and CO2 emissions in Asian economies. Can et al.
(2022) investigated the impact of trade in non-eco friendly prod-
ucts on ED in EU countries and found that non-green trade open-
ness reduces environmental degradation. Ashraf et al., 2023
stated that TOP has a positive impact on ED in 75 BRI countries.
When the literature is examined, many studies (Dauda et al.,
2021; Dou et al., 2021;Mahmood et al., 2019; Solarin et al., 2017;
Ewane and Ewane,2023; Adebayo et al., 2023 ) have found that
TOP increases ED in the long term. However, some researchers
(Zhang et al., 2017a) found a different relationship. It is worth not-
ing that TOP may produce diverse outcomes depending on the
level of development. For instance, Essandoh et al. (2020) revealed
that while TOP reduces ED in developed countries, it has the oppo-
site effect in less developed countries. Moreover, the research con-
ducted by Wang and Zhang (2021) proposes that TOP diminishes
ED in high-income nations.
Numerous studies have investigated the correlation between
trade openness and deforestation. For example, Maji (2017)
showed that increasing trade openness helps to improve the qual-
ity of the environment by reducing deforestation in Nigeria. A sim-
ilar result was also discovered by Nathaniel and Bekun (2020) for
the same country. Tsurumi and Managi (2014) discovered that in
OECD countries, deforestation decreases because of trade open-
ness. However, in the case of non-OECD countries, their study
revealed contrasting findings.
2.4. Relationship between URB and ED
In global nations, there is a prevailing tendency for rural popu-
lations to migrate toward urban areas. Over the last two decades,
more than half of the world’s population has chosen to live in
cities, and this proportion is expected to rise to almost 70 % in
the next 30 years. In Europe, the rate of urban living has reached
approximately 75 % (Oueslati et al., 2015; Sodiq et al., 2019).
EMT has been developed to elucidate the intricate interplay
between urbanization and ED.
EMT constitutes a theoretical stance that revolves around the
restructuring of contemporary society’s central institutions to
grapple with the core quandary of ecological crises (Spaargaren
and Mol, 2009: 68). From an alternate perspective, EMT that
aspires to harmonize capitalism, societal dynamics, and the envi-
ronment (Sahin, 2022). In line with the tenets of EMT, the phe-
nomenon of urbanization triggers an initial escalation in ED;
however, as urbanization advances, it affects a reversal in this tra-
jectory, leading to a decrease in environmental degradation. This
trajectory can be visualized using a graph depicting a reversed U-
shaped trend (Poumanyvong and Kaneko, 2010).
When the literature is examined, some of the studies (Zhang
et al., 2021; Hashmi et al., 2021; Muhammad et al., 2020; Shah
et al., 2020; Sorge and Neumann, 2019; Zi et al., 2016; Martínez-
Zarzoso and Maruotti, 2011; Mol, 1999; Ahakwa,2023) support
the EMT, whereas some studies (Mastrangelo and Aguiar, 2019;
Salahuddin et al., 2019) do not. In addition, some studies
(Adebayo et al., 2021; Yasin et al., 2021; Katircioglu et al., 2018;
Zhang et al., 2017b; Shahbaz et al., 2014; Zhang et al., 2012) have
shown that urbanization positively affects ED in all periods,
whereas some studies (Ehigiamusoe, 2023; Gasimli et al., 2019;
Sharma, 2011; Hossain, 2011) have shown that urbanization nega-
tively affects ED. In another study, Muntasir (2022) found that pop-
ulation growth induces deforestation in Bangladesh. Moreover,
Nathaniel and Bekun (2020) found that an increase in urbanization
causes deforestation in Nigeria.
2.5. Relationship between HC and ED
HC plays a crucial role in preserving the environment (Çakat
et al.,2021). Research shows that as human HC improves, ED
decreases and more innovations are made to protect the environ-
ment (Adikari et al.,2023; Chondrogianni and Tsalaporta,2023).
Green human resources and social capital play key roles in promot-
ing sustainable environmental practices. Investments in human
capital, such as employee training and development, are critical
for long-term sustainable corporate development. In the Chinese
context, different levels of human capital have different impacts
K. Ahmad Khan, M. Khalid Anser, F. Pala et al. Gondwana Research 132 (2024) 136–149
139
on total green factor productivity (Wang et al.,2021). In BRICS
economies, the impact of human capital on CO2 emissions varies
depending on the type of education (Li and Ullah, 2022). Overall,
human capital plays an important role in shaping environmental
outcomes and promoting sustainability.
Çamkaya et al., 2023, in their study of Turkey, found that HC
decreased ED. Appiah-Twum and Long, 2023, in their study of 14
Asia Pacific countries, stated that HC has a positive effect on ED.
Wang et al. (2023) found that the effect of HC on ED varied in their
study of 208 countries. Chondrogianni and Tsalaporta (2023) found
that HC has a positive effect on ED in their study of 14 developing
countries. Asraf and Javed (2023) found that HC has a positive
effect on ED in their study of 102 BRI countries.
2.6. Research gap
This paper fills some gaps in the literature by addressing some
aspects neglected by previous studies on the assessment of ED. A
review of the literature has shown that ED is usually assessed using
a single variable. In this study, in line with Donohoe (2003), a mul-
tifaceted ED index has been created by calculating ED more com-
prehensively through the GHGE and deforestation index, which
affect ED the most. In this context, we have addressed this gap in
the literature by creating an index that includes deforestation. This
study aims to fill the gap in the literature by focusing on develop-
ing countries, whereas previous studies focusing on the relation-
ship between GIN and ED usually focus on developed countries.
This study also provides an opportunity to address various per-
spectives on environmental sustainability by testing the validity
of ecological modernization theory (EMT) in terms of both emis-
sion and deforestation, which has been neglected in environmental
studies.
3. Data and methodology
3.1. Data
The study comprises groups of countries categorized as devel-
oping nations by the IMF. The research sample encompasses 30
developing countries (Countries are given in appendix table A.2).
The temporal span of the study encompasses annual data from
2009 to 2019. Fig. 2 shows the locations of the countries included
in the analysis on the world map.
The core objective of this investigation is to uncover the influ-
ence of GIN on ED within the economies of developing countries.
Given the multifaceted and intricate nature of the factors impact-
ing ED, elucidating ED with a singular factor can prove challenging
(Barut et al., 2023). Consequently, in line with the methodology
proposed by Agliardi et al. (2015), an ED index incorporating total
GHGE and deforestation was established. Recognizing that both
total GHGE and deforestation possess two distinct dimensions,
the entropy method was employed to determine the weights
assigned to these variables. Fig. 3 shows the trend of the ED index.
The entropy approach is described in the appendix, along with
Table A.1, which describes the environmental sustainability rating
methodology.
The G20 Green Investment Working Group articulates GIN as
the provision of funds for investments that yield environmental
advantages, encompassing the reduction of air, water, and soil pol-
lution, alongside the curbing of GHGE. Furthermore, it encom-
passes the augmentation of green energy efficiency and
endeavors to combat climate change. These financial undertakings
are integral to fostering environmentally sustainable development.
The scope of GIN primarily includes topics such as environmentally
friendly investments, eco-friendly funds, sustainable loans, green
securities, and finance related to carbon emissions (Tang et al.,
2022). In this study, data on public investments in renewable
energy (expressed in millions of USD in 2019) is employed to
understand the level of GIN development across the 30 available
data-accessible developing economies. Fig. 4 shows the green
investment sizes of 30 developing countries.
Investments targeted at promoting green technol-
ogy have the potential to decrease pollutant emissions from indus-
tries that are major contributors to pollution, in addition to the in-
fluence economic development has on ED through technological
means (Anwar et al., 2023a,b). Green technology can help reduce
ED and at the same time, it can also ensure that resources such
as forests are utilized effectively (Li et al., 2023). To this end,
GDPPC is incorporated into the study. Fig. 5 shows the GDPPC of
30 developing countries.
The influence of environmentally friendly technology and pro-
duct import/export on ED is nuanced; while these factors can pos-
itively impact ED, unfavorable circumstances may lead to negative
effects. Thus, trade openness (TOP) serves as a control variable in
this study. Increased urbanization levels may have an adverse
impact on ED. Fig. 6 shows the top 30 developing countries.
To measure urbanization, the urban population-to-total popula-
tion ratio is used as a measurable indicator. HC is an important fac-
tor affecting environmental sustainability. Research shows that as
HC improves, ED decreases and more innovations are made to pro-
tect the environment (Li and Ullah, 2022).Fig. 7 shows the trade
openness rate. Table 1.
The study variables are described in Table 1.
The study constructs three models as follows:
ED
it
¼
a
0
þb
1
lnGIN
it
þb
2
lnGDPPC
it
þþb
3
TOP
it
þb
4
URB
it
þb
6
HC
it
þ
l
it
ð1Þ
ED
it
¼
a
0
þb
1
lnGIN
it
þb
2
lnGDPPC
it
þb
3
lnGDPPC
2
o
it
þb
4
TOP
it
þb
5
URB
it
þb
6
HC
it
þ
l
it
ð2Þ
ED
it
¼
a
0
þb
1
lnGIN
it
þb
2
lnGDPPC
it
þb
3
TOP
it
þb
4
URB
it
þðb
5
lnURB
2
Þ
it
þb
6
HC
it
þ
l
it
ð3Þ
In equations 1, 2, and 3, ED represents the environmental degrada-
tion index constructed by the entropy method, GIN represents
green investment, GDPPC represents economic growth per capita,
GDPPC
2
represents the square of growth per person, TOP represents
trade openness, URB represents urbanization, and HC represents
human capital. b
1
,b
2
,b
3
,b4, b
5
, and b6 are the coefficients of these
variables,
a
is the constant number and
l
is the error coefficient.
Here, model 1 represents the model without any squared terms,
model 2 includes the squared term of GDPPC, and model 3 includes
the squared term of URB. Model 2 tests the EKC where if b2 is pos-
itive and b3 is negative, there is an inverted U-shaped EKC hypoth-
esis. However, if b2 is negative while b3 is positive, it indicates a U-
shaped relationship between growth and environmental quality.
Model 3 tests the EMT theory. If b4 is positive and b5 is negative,
the EMT is considered valid. Models 2 and 3 represent the STIRPAT
equation, where population is represented by urbanization, A is
GDP, and T is GIN, which represents green technology investment.
3.2. Methodology
3.2.1. Panel estimation test
The KRLS method is primarily used to analyze the model. The
KRLS method, developed by Hainmueller and Hazlett (2014), is a
machine learning technique used for regression analysis. This
method focuses on modeling complex and non-linear relationships
K. Ahmad Khan, M. Khalid Anser, F. Pala et al. Gondwana Research 132 (2024) 136–149
140
in a dataset using the kernel process. It reduces the risk of overfit-
ting with regularization terms and makes predictions using the
least squares principle. KRLS is suitable for regression and classifi-
cation problems and is powerful in capturing non-linear relation-
ships. However, while it can work better with small datasets, the
computational cost may increase with large datasets. KRLS is a
preferable option when modeling non-linear relationships. In addi-
tion, the Driscoll-Kraay’s (1998) and Arellano and Bover (1995)/
Blundell and Bond (1998) methods, one of the GMM methods,
are used to test the robustness of the findings obtained using the
KRLS method. Driscoll-Kraay’s (1998) estimator consistently pro-
duces results in situations characterized by heteroscedasticity,
autocorrelation, or inter-unit correlation. This particular estima-
tion method is favored because of its stability even in circum-
stances characterized by substantial T and N. Given that
economic occurrences within a given time frame are substantially
influenced by prior economic events, it is vital to include lagged
variable values in the explanatory set when investigating economic
relationships. Moreover, dynamic models offer various benefits.
These models account for residual autocorrelation, consequently
mitigating the risk of spurious regression. This risk leads to incon-
sistent estimations and flawed inferences in static models. Unlike
static models, which neglect the dynamic interplay between vari-
ables, dynamic models effectively capture these dynamic relation-
ships. An important virtue of dynamic panel models lies in their
consistency, particularly when dealing with a limited time dimen-
sion and an extensive cross-sectional dimension (companies, coun-
tries, etc.) (Yıldırım and Kostakog
˘lu, 2015). Given these
considerations, the authors employ the dynamic panel data analy-
sis methods, specifically the estimators proposed by Arellano and
Fig. 2. Countries covered by the study.
K. Ahmad Khan, M. Khalid Anser, F. Pala et al. Gondwana Research 132 (2024) 136–149
141
Bover (1995)/Blundell and Bond (1998), in this study. The endo-
geneity problem is a major problem in econometric research. In
this study, the endogenety problem can arise due to reverse causal-
ity or omitted variable bias. Therefore, the study implements the
GMM technique. In dynamic panel data models, the inclusion of
lagged values of dependent variables as explanatory variables
helps to address the potential endogeneity issue by eliminating
the relationships between variables (Topal and Hayalog
˘lu, 2017).
4. Results and discussion
Before starting the analyses, descriptive information about the
variables and the correlation matrix are given in Table 2 and
Table 3, respectively.
Table 3 shows that ED, lnGIN, and HC are negatively correlated,
whereas ED, LNGDPPC, TOP, and URB are positively correlated.
Table 4 reports the results from the KRLS estimators. Table 5
reports the results of Driscoll & Kraay, Arellano and Bover (1995)
and Blundell and Bond (1998) tests for the robustness of the find-
ings obtained in the KRLS test.
When the results of Table 4 and Table 5 are analyzed, it can be
seen that the results of all three estimators are approximately sim-
ilar. It can be observed that GIN has a positive effect on ED in all
three estimators. This result is similar to those of Triki et al.,
2023; Chin et al., 2022; Tang et al., 2022; Huang and Chen, 2021;
and Li and Gan, 2021. For BRICS, Udeagha and Muchapondwa
(2023) also found that green finance improves environmental sus-
tainability. In another study on an extensive sample of 76 develop-
Fig. 3. Trend of ED index in the selected countries.
Fig. 4. Green Investment in the selected countries. (For interpretation of the
references to colour in this figure legend, the reader is referred to the web version of
this article.)
Fig. 5. GDPPCs of the selected countries.
K. Ahmad Khan, M. Khalid Anser, F. Pala et al. Gondwana Research 132 (2024) 136–149
142
ing economies, Bakry et al. (2023) discovered that green finance
has the capability to reduce CO2 emissions. According to Wang
and Zhi (2016), GIN investments reduce environmental damage
by ensuring a more efficient use of resources. The finding obtained
in this context is in this direction and shows that GIN practices and
investments reduce air pollution and deforestation and positively
affect sustainability. In addition, several potential factors, such as
the transfer of resources and financing to targeted green projects,
increased environmental awareness by the society, green technol-
ogy transfers, and the development of environmental standards,
are thought to be effective in the emergence of this result. Invest-
ments in renewable energy sources, which emit fewer greenhouse
gasses than fossil fuels, are typically included in the GIN. Projects
that utilize renewable energy provide an ecologically sustainable
method for producing electricity, which helps fight climate change.
Furthermore, green investments often support energy efficiency
projects that minimize environmental impacts by saving energy
in industrial processes and buildings. Moreover, green innovation
has become a critical strategy in recent decades to fight environ-
mental issues and obtain sustainable development (Jin et al.,
2024). Therefore, it is plausible to attribute the potential of GIN
in developing countries to reduce ED to environmentally friendly
technologies.
When the relationship between GDPPC and ED is analyzed, the
EKC hypothesis is supported. According to the findings, while
GDPPC has a negative effect on ED in the early stages of economic
development, this negative effect becomes positive as countries
reach a certain level of GDPPC. In this context, it is found that there
is an inverted U relationship between GDPPC and ED. This finding
is similar to the results of Rofiuddin et al. (2019) for low-income
country economies, Adebayo et al. (2021) and Zheng et al. (2015)
for the Chinese economy, Anser et al. (2021) for South Asian coun-
try economies, Prasetyanto and Sari (2021) for the Indonesian
economy, and Barut et al. (2023) for the Fragile Five Economy.
The findings obtained in the analysis for developing countries
show that changes in income levels have different effects on ED
over time. According to this finding, while there is a tendency for
ED with an increase in income in the early periods, the increase
in income has a decreasing effect on environmental degradation
in later periods. This indicates that after the economy exceeds a
certain growth threshold in developing countries, income growth
leads to environmentally friendly technologies and practices that
support environmental sustainability. This result supports the
finding of Bakry et al. (2023) for developing countries where they
also discovered the existence of the EKC theory. They suggested
that the countries will need a substantial amount of time to turn
the gains of the economy into benefits for the environment since
the problems related to ED will not get better until they get worse
before.
When the relationship between TOP and ED is analyzed, it can
be seen that TOP increases ED. Because the countries analyzed
are developing countries, TOP is expected to have a negative effect
Fig. 6. Urban population (% of total population) of the selected countries.
Fig. 7. Trade openness of the selected countries.
Table 1
Description of variables.
Variable Name Meaning of Variables Variable
Abbreviations
Source
Environment degradation Index of GHGE and deforestation ED Authors’ own calculation
Green Investment Public Investments in Renewable Energy (2019 million USD) GIN The International Renewable
Energy Agency (IRENA) Public Finance Database
Income GDP per capita (current USD) GDPPC WD_
I
Trade openness Imports + exports of goods and services (% of GD) TOP WD_
I
Urbanization Urban population (% of total population) URB. WD_
I
Human Capital Years of schooling and returns to education HC Penn world table
K. Ahmad Khan, M. Khalid Anser, F. Pala et al. Gondwana Research 132 (2024) 136–149
143
on environmental sustainability variables in the relationship
between TOP and ED. In other words, countries with higher trade
openness are expected to have lower environmental performance.
This finding also supports the results of previous studies such as
that of Shahbaz et al. (2022) and Ashraf et al. (2023). In other
words, these findings support previous studies in the literature
that trade openness can negatively affect environmental variables
and that this can be observed especially in developing countries.
When examining the impact of modernization on the environ-
ment, environmental modernization theory (EMT) includes not
only economic changes but also societal and institutional shifts.
Within this framework, the influence of modernization on the
environment is meticulously evaluated. Urbanization, a catalyst
for social metamorphosis, is recognized as a pivotal marker of
modernization. Although ED may escalate during the initial and
intermediate phases of development, ensuing modernization
endeavors strive to reduce environmental predicaments. Societies,
cognizant of the imperative of environmental sustainability, dili-
gently endeavor to disentangle ecological repercussions from eco-
nomic expansion through innovative technological strides and a
shift toward knowledge-oriented and service-based sectors. This
viewpoint draws upon the scholarly perspectives of luminaries
such as Huber (1982), Janicke (1985), and Barry and Paterson
(2003).In the context of this study, the observation that URB exhi-
bits a positive correlation while URB
2
demonstrates a negative
relationship underscores the validity of the Ecological Moderniza-
tion Theory within developing nations. This discovery aligns har-
moniously with the findings of research conducted by Hashmi
et al. (2021), Xu and Lin (2015), and Du and Xia (2018).
Finally, the findings show that HC has a positive impact on envi-
ronmental sustainability. HC includes individuals’ education, expe-
rience, and skills, which can influence factors such as
environmental awareness, innovation, and participation in sustain-
able practices. A more educated workforce tends to develop envi-
ronmentally friendly solutions, use resources more effectively,
and be more sensitive to environmental issues. In this context,
the result obtained supports the findings of studies such as (Ç
akar et al., 2021; Li and Ullah, 2022; Adikari et al.,2023;
Chondrogianni and Tsalaporta,2023).
Table 5 reports the analyses for the robustness test.
Both tests for the robustness test were found to support the
KRLS findings. This finding increases the reliability of the obtained
results.
5. Conclusion and policy recommendations
This research investigates how GIN affects ED in 30 developing
nations. In this study, the ED index was calculated using the
entropy method. For this purpose, using data from 30 countries
between 2009 and 2019, the effects of GIN and other variables
on ED were analyzed by panel regression techniques. The main
method in the study is KRLS, and the robustness of the findings
is examined using the GMM system and the Driscoll–Krayy
method.
In the study, GIN was found to reduce ED. For the continuity of
this situation, measures such as government incentives, tax advan-
tages, or financial support may encourage the private sector to
invest in environmentally friendly projects. In addition, increasing
support for green technology R&D can contribute to the emergence
of innovations that reduce environmental impacts. Ensuring that
society understands the importance of green investments through
education and awareness campaigns is also an important step.
Finally, the development of green financing mechanisms to provide
financial resources for projects that reduce environmental impacts
can encourage sustainable investments.
Table 2
Descriptive Statistics.
Variables Obs Mean Std. Dev. Min Max
lnGIN 330 7.662 1.334 4 9.993
ED 330 0.113 0.022 0.0009 0.140
GDPPC 330 0.883 0.218 1.775 1.362
TOP 330 0.596 0.248 0.709 1.378
URB. 330 1.765 0.139 1.365 1.963
HC 330 1.621 0.188 1.210 1.800
Table 3
Correlation Matrix.
Correlation ED LNGIN LNGDPPC TOP URB. HC
ED 1
LNGIN 0.120 1
LNGDPPC 0.045 0.030 1
TOP 0.081 0.125 0.207 1
URB. 0.096 0.100 0.203 0.077 1
HC 0.185 0.065 0.254 0.387 0.654 1
Table 4
KRLS Estimation Results.
Model1 Model2 Model 3
lnGIN 0.008**
[0.004]
0.007**
[0.004]
0.008**
[0.004]
lnGDPPC 0.003***
[0.0009]
0.004***
[0.0009]
0.002***
[0.0009]
lnGDPPC
2
─0.009***
[0.0001]
─
TOP 0.063***
[0.002]
0.065***
[0.002]
0.088***
[0.008]
URB. 0.012***
[0.003]
0.010***
[0.003]
0.012***
[0.003]
URB
2
──0.024***
[0.005]
HC 0.015***
[0.003]
0.013***
[0.003]
0.009**
[0.003]
C───
Lambda 1.595 1.658 1.321
Tolerance 0.214 0.320 0.198
Sigma 5 5 5
Eff. df 22.1 23.4 25.6
Looloss 1.491 1.587 1.387
R-squared 0.28 0.25 0.19
Note: *, **, and *** correspond to significance levels of 10%, 5%, and 1%, respectively.
K. Ahmad Khan, M. Khalid Anser, F. Pala et al. Gondwana Research 132 (2024) 136–149
144
The findings also showed that the GDPPC level had a negative
effect on ED in the early periods. However, it was found that the
negative effect of GDPPC became positive after a certain level. This
finding also supports the EKC hypothesis. Similar to GDP, the URB
rate also increased the ED in the first period, but this negative
effect was found to turn positive over time. This finding supports
the EMT hypothesis. Again, while GIN had a positive effect on ED,
TOP had a negative effect. On the other hand, HC showed a positive
effect on ED.
The finding that EKC is valid in developing countries reveals the
potential of combining environmental sustainability with eco-
nomic growth. In this context, it is important to adopt green
growth policies. These policies should include incentives for envi-
ronmentally friendly sectors and encourage investments in green
technology. Moreover, by supporting energy efficiency measures
and carbon emission reduction projects, a positive balance
between economic growth and environmental sustainability can
be established. In addition, incentives can play an important role
in raising public and business awareness, increasing environmen-
tal consciousness, and adopting sustainability principles. The fact
that TOP increases ED in rapidly developing countries indicates
that environmental regulations should be strengthened and the
environmental impacts of industry should be controlled by tight-
ening inspections. The EMT theory is also found to be valid, and
this finding emphasizes that the negative impact of population
on the environment should be eliminated as soon as possible.
The limitations of this study are that it covers only 30 develop-
ing countries. The scope of the study is between 2009 and 2019.
From this viewpoint, this study can guide future studies. By
expanding the data scope of this study, the effect of GIN on ED
can be examined for the same or different country groups. In addi-
tion, the ED index created in this study can be further expanded by
adding different variables.
6. Consent to participate
Not applicable.
7. Consent to publish
All authors provided explicit consent to submit and publish this
work.
Ethical approval
Not applicable.
Funding
No funding was received for conducting this study.
CRediT authorship contribution statement
Khatib Ahmad Khan: Formal analysis, Investigation, Resources,
Writing – original draft, Supervision. Muhammad Khalid Anser:
Resources, Writing – original draft, Formal analysis, Supervision.
Fahrettin Pala: Conceptualization, Data curation, Software,
Resources, Writing – original draft, Formal analysis, Writing –
review & editing. Abdulkadir Barut: Formal analysis, Resources,
Writing – original draft. Muhammad Wasif Zafar: Resources,
Writing – original draft, Supervision.
Declaration of competing interest
The authors declare that they have no known competing finan-
cial interests or personal relationships that could have appeared
to influence the work reported in this paper.
Appendix
Entopy method
The normalisation of the data was carried out using the formulae
provided in formulae 1 and 2. When factors have a positive impact
Table 5
Driscoll & Kraay and Arellano and Bover (1995) and Blundell and Bond(1998) Estimation Results.
Driscoll & Kraay Arellano and Bover (1995)// Blundell and Bond(1998
Model1 Model2 Model 3 Model1 Model2 Model 3
ED
_1
───1.163***
[0.130]
0.991***
[0.125]
0.954***
[0.130]
lnGIN 0.013*
[0.004]
0.011*
[0.003]
0.011*
[0.003]
0.007*
[0.001]
0.006*
[0.002]
0.009*
[0.004]
lnGDPPC 0.008***
[0.001]
0.011***
[0.009]
0.007***
[0.001]
0.061**
[0.003]
0.026**
[0.008]
0.060**
[0.03]
lnGDPPC
2
─0.023*
[0.004]
── 0.051***
[0.005]
─
TOP 0.009***
[0.0001]
0.005**
[0.001]
0.005**
[0.001]
0.011**
[0.007]
0.013**
[0.007]
0.016**
[0.009]
URB. 0.022***
[0.006]
0.021***
[0.006]
0.021***
[0.006]
0.055***
[0.001]
0.050***
[0.005]
0.013*
[0.005]
URB
2
──0.044**
[0.001]
──0.027*
[0.005]
HC 0.025***
[0.009]
0.022***
[0.006]
0.028***
[0.006]
0.032***
[0.003]
0.035***
[0.003]
0.032***
[0.003]
C 0.167***
[0.015]
0.178***
[0.016]
0.145***
[0.025]
0.790***
[0.004]
1.025***
[0.010]
0.952***
[0.011]
F 28.83 30.04 35.25 ───
Prob > F 0.000 0.000 0.000 ───
R-squared 0.032 0.018 0.010 ───
Prob > chi2 ───0.000 0.000 0.000
Sargan Test ───28.819
(0.996]
15.625
(0.651]
18.214
(0.745]
Auto-Correlation (AR2) ───2.140 (0.354)
Note: *, **, and *** correspond to significance levels of 10%, 5%, and 1%, respectively.
K. Ahmad Khan, M. Khalid Anser, F. Pala et al. Gondwana Research 132 (2024) 136–149
145
on environmental quality, equation 1 is used, and when they have
a negative impact, equation 2 is applied (Ayçin, 2020).
X
ij
¼X
ij
X
min
j
X
max
j
X
min
j
:::::; j¼1;2; :::; nð1Þ
X
ij
¼X
max
j
X
ij
X
max
j
X
min
j
:::::; j¼1;2; :::; nð2Þ
The standardised values Xj in the equations show the highest and
lowest values of each value in all years.
Calculation of weights
The weights of the variables were calculated with the help of
the equation given in Equation 3.
P
ij
¼X
ij
P
m
i
X
ij
ð3Þ
Calculation of entropy value
e
ij
¼k:X
n
j¼1
P
ij
:lnðP
ij
Þi¼1;2; :::; m
v
ej¼1;2; :::; nð4Þ
The value of k in the equation is a constant coefficient defined as
k=(ln(m))-1 and takes value as 0 ej 1. The value of ej is defined
as the uncertainty measure of the j. criterion, or in other words, the
entropy value (Ayçin, 2020).
Calculation of differentiation coefficients of variables
Using the entropy values calculated in the previous step, dj val-
ues, which are the degree of differentiation, were calculated for
each variable with the help of the equation given in Equation 5.
d
j
¼1e
j
j¼1;2; :::; nð5Þ
A high value of dj indicates that the distance or differentiation
between the alternative scores of the variables is high.
Entropy calculation of criterion weights
Equation 6 provided the formula used to obtain the weight val-
ues for the variables.
w
j
¼d
j
P
n
j¼1
d
j
ð6Þ
In this equation, the weight values of the variables were calcu-
lated by proportioning the degree of differentiation of each vari-
able to the total degree of differentiation.
Calculating the sample composite index
The environmental degradation index evaluated within the
scope of the research was calculated with the help of the equation
given in Equation 7.
z
i
¼X
m
j¼1
w
j
X
ij
ð7Þ
Table A1
Evaluation system for environmental and ecological degradation.
Indicator Guideline Level Proportion
Environment
Degradation (ED)
Total greenhouse gas
emissions
0.682
Net forest depletion 0.328
Table A2
Countries covered in the study.
Argentina Poland
Brazil Romania
China Chile
India Colombia
Indonesia Thailand
Mexico Egypt
Turkey Ecuador
Malaysia Bolivia,
Bangladesh Kenya
Botswana Morocco
Costa Rica Nigeria,
Ghana Pakistan
Jamaica Panama
Jordan Peru
Paraguay
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