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Are Mercosur economies going green or going away? An empirical
investigation of the association between technological innovations,
energy use, natural resources and GHG emissions
Muhammad Usman
a,
⇑
, Daniel Balsalobre-Lorente
b,c
, Atif Jahanger
d,e
, Paiman Ahmad
f,g
a
China Institute of Development Strategy and Planning, and Center for Industrial Economics, Wuhan University, Wuhan 430072, China
b
Department of Applied Economics I. University of Castilla-La, Mancha, Spain
c
Department of Applied Economics, University of Alicante, Spain
d
School of Economics, Hainan University, Haikou City, Hainan 570228, China
e
Institute of Open Economy, Hainan province, Haikou, 570228 China
f
Department of Law, College of Humanity Sciences, University of Raparin, Ranya, Iraq
g
International Relations and Diplomacy Department, Faculty of Administrative Sciences and Economics, Tishk International, University, Erbil, Iraq
article info
Article history:
Received 1 August 2022
Revised 20 October 2022
Accepted 21 October 2022
Available online 28 October 2022
Handling Editor: M. Santosh
Keywords:
GHG emissions
Technological innovations
Natural resources
Renewable energy
Mercosur countries
abstract
The dynamic association between technological innovations, human capital and greenhouse gas (GHG)
emissions has serious environmental repercussions. On the other hand, as significant as it is, this domain
is inadequately investigated by environmentalists, and explored the indefinite and vague outcomes. In
addition, these studies do not deal with the function of natural resources in determining environmental
quality, in an explicit and precise manner. For that reason, realizing the need for a further rigorous and
significant assessment of the intricacies concerned in studying GHG emissions, this study investigates the
influence of technological innovations, economic growth, renewable energy, natural resources, and
human capital on GHG emissions in Mercosur countries from 1990 to 2018. The analysis follows a
second-generation perspective, which generates reliable and robust outcomes in the presence of slope
heterogeneity and cross-sectional dependency. The long-run elasticity estimates calculated through
the Driscoll and Kraay approach suggest that economic growth and natural resources significantly
increase GHG emissions, while technological innovations, renewable energy, and human capital help
to curtail them. The outcomes of the country-specific analysis are consistent with the Mercosur panel
estimates. The findings of the Dumitrescu and Hurlin non-causality test reveal a feedback hypothesis
between economic growth, human capital, renewable energy, and GHG emissions. The conservation
hypothesis from technological innovation and natural resources to GHG emission is also discovered.
Mercosur economies should increase their technological innovation activities and human capital to
ensure that new firms and established companies can innovate rapidly in favour of sustainable develop-
ment and lifestyle quality. The empirical findings of this study allow us to propose various policy sugges-
tions for the Mercosur countries.
Ó2022 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.
1. Introduction
Since the dawn of the industrial age, numerous studies have
reported that the level of pollution released into the environment
has expanded massively. In the last two to three decades, varia-
tions in the atmosphere have been recognized as the greatest cli-
mate dilemma facing the globe. This is a complex phenomenon
involving intricate connections between three fundamental
parameters such as energy, economics, and the environment
(EEE). Energy (both fossil fuel and renewable) is essential for pro-
duction and hence for the gross domestic product (GDP) and social
welfare; however, it is also considered a leading cause of green-
house gas (GHG) emissions (Belaïd and Zrelli, 2019). Environmen-
tal pollution has been identified as a factor in many severe
diseases, for instance, stroke, lung cancer, respiratory and heart
disease, etc., and in 2012, environmental pollution was also the
cause of approximately 7 million deaths (Azam and Khan, 2016).
Environmental emissions are responsible for depleting natural
https://doi.org/10.1016/j.gr.2022.10.018
1342-937X/Ó2022 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.
⇑
Corresponding author.
E-mail addresses: usman399jb@gmail.com (M. Usman), Daniel.Balsalobre@uclm.
es (D. Balsalobre-Lorente), atif_jahanger@hotmail.com (A. Jahanger), paiman@uor.
edu.krd (P. Ahmad).
Gondwana Research 113 (2023) 53–70
Contents lists available at ScienceDirect
Gondwana Research
journal homepage: www.elsevier.com/locate/gr
resources, reducing areas of cultivation, and infrastructure damage,
loss of biodiversity and human deaths (Usman et al., 2020). In the
1980 s, the debate centered on the dynamic link between GDP
growth, environmental pollution, and carbon dioxide (CO
2
) emis-
sions contributing approximately 59 % of total GHG emissions.
The GHG emissions are predicted to rise by around 52 % by 2050,
with no successful environmental strategies in place (Sohag
et al., 2017). In this alarming scenario, the Paris Conference of
the Parties (COP21) agreement which took effect in mid-
December 2015, reported that the world CO
2
emissions would
climb in 2020 and that temperature variations would be below
2°C(Pachauri et al., 2014).
There is an urgent need in the modern era to implement new
technological innovation (TECH) policies. TECH in developing
countries has grown from 0.11 million to 1.74 million patent appli-
cations since 1980 (World Bank, 2020). Private-Public support for
TECH is essential for a sustainable environment and economic
development, particularly to connect in highly work tentative
areas (Alola et al. 2021). This study aims to provide fresh evidence
of the linkage between innovation processes and environmental
degradation, in line with those who validated the existence of a
direct linkage between sustainable growth and the promotion of
dynamic innovation processes. Consequently, our results advance
in this line, offering a new battery of variables that connects inno-
vation with environmental degradation. As noted in the empirical
literature, if TECH is given the attention and significance it
deserves, it can contribute to achieving sustainable economic
growth through the well-organized utilization of natural resources.
This would allow the world’s economic system to resolve the scar-
city of natural resources and accommodate the rapid increase in
population levels through technological innovation without affect-
ing environmental quality. From this standpoint, the shift from
conventional to eco-friendly technologies, including recycling
and the adaptation of innovative processes, reprocessing and pro-
duct use to replace the consumption of natural resources, will
boost income growth and ultimately reduce environmental dam-
age (Sinha et al., 2020; Bekun et al., 2019; Usman and Hammar,
2021; Ali et al., 2022). In the course of the industrial revolution,
sustainable growth, technological progress, and the use of renew-
able and cleaner energy sources to reduce the reliance on non-
renewables will significantly help to achieve social and economic
development. This can be considered a win–win situation that
increases GDP growth and environmental security.
There is an urgent need to reorganize renewable and clean
energy sources due to the specific and unavoidable nature of the
energy demand in the production process. Renewable energy
sources are inexhaustible, safe, and clean as compared to conven-
tional energy sources like fossil fuels. Renewable energy sources
are predicted to become a part of the dynamic arrangement and
improve on other non-renewable energy resources. Since 1990,
demand for renewable energy has been rising at a rate of 8 % per
annum, highlighting the public’s awareness of environmental qual-
ity (Salahuddin et al., 2020). The utilization of renewable energy
sources is crucial to attaining European Union (EU) 2030 energy
evolution targets. The TECH obsessed with public expenditures
on research and development (R&D) is a chief driver (Bointner
et al., 2016). In view of the importance of renewables in recent
years, they now play a vital role in new sustainable development,
energy security and controlling atmosphere variations for three
reasons; (i) clean energy sources are abundant in Latin America,
and if developed, can supply energy over a long period; (ii) they
can support the supply of renewable energy to rural areas where
people have difficulty being served by the electricity grid; and
(iii) they can offset the share of foreign exchange needed for oil
imports. In order to enhance the supply of sustainable energy,
there is a pressing need to promote and invest in renewable energy
resources even as fossil fuel consumption declines. This situation
reveals a conflict between cleaner energy and fossil fuel energy,
the selection of which can affect issues of investment, the environ-
ment and economic growth (Ackah and Kizys, 2015; Khan et al.,
2021).
The other objective of the present study is to determine how
natural resources influence GHG emissions. Natural resources are
closely linked to an economy’s per capita income levels. During
the initial stages of economic development in an economy, people
consume extra energy by using more natural resources and ignore
the environmental consequences. However, at the threshold level,
such as in the more developed economic stages with improved life-
styles, they demand energy-efficient products, a cleaner atmo-
sphere, and the protection of natural resources. It has been
extensively documented that the depletion of natural resources
due to economic growth and industrialization can contribute sig-
nificantly to a rise in CO
2
emissions. The over-use of natural
resources has various severe environmental costs such as defor-
estation, rising temperatures and global warming. Due to the
increase in world population, deforestation is threatening to exac-
erbate global temperatures and environmental damage (Graham
et al., 1990). It is therefore essential to scrutinize the role played
by technological innovation, human capital and natural resources
in the association between GDP growth and GHG emissions in
the Mercosur region. Based in Birdsall et al. (2001), confirmed
the positive impact that human capital exerts on the reduction in
natural resources over-exploitation. Unlike most previous work,
which only considers each of these effects separately, this research
explores the moderation effect that human capital exerts on natu-
ral resources and the effect on environmental damage. This
hypothesis is in line with Zalle (2019), who explored the condi-
tional effects that human capital exerts on natural resources, con-
cluding that institutions’ quality would determine the interaction
among these variables.
The Mercosur trade-block, recognized by the Asunción agree-
ment, was created in 1990 to encourage the free and open trade
of goods and services and the free mobilization of labour and cap-
ital across the member countries, namely Brazil, Argentina, Uru-
guay, Paraguay and Venezuela (Koengkan, 2018). In Mercosur
countries, renewable energy consumption began in the 1970s, with
hydropower and biofuels in 1973 and 1975, respectively, for Brazil.
Wind, biomass, hydropower, geothermal, biogas, waves and photo-
voltaic energy began in Argentinean 1998; Uruguay and Paraguay
initiated hydropower in 1973, and Venezuela started using hydro-
power in 2001 (IRENA, 2016). The Mercosur market for alternative
modern renewable energy is also the most experienced and
dynamic in economic development in both the consumption of
renewable energy and domestic investment (Fuinhas et al.,
2017). This trend has been fueled by rising energy demand, the
abundance of natural resources, high energy prices, over-
dependence on fossil fuels, and energy security concerns
(Koengkan, 2018; Bekun, 2022). The sharp rise in energy demand
in Mercosur countries has been accompanied by a rapid increase
in per capita economic growth, encouraged by the different politi-
cal deregulation and economic restructuring approaches in the last
four decades. It is imperative to include renewable energy use and
natural resources by assessing GHG emissions, which is the study’s
overarching aim. Fig. 1 presents the trend analysis of the candidate
variables such as GHG emissions, technological innovations, eco-
nomic growth, renewable energy, natural resources, and human
capital development for Mercosur countries.
Many previous studies have already investigated the signifi-
cance of technological innovation in accomplishing environmental
sustainability. This attention is intuitive from various areas of
knowledge, such as economics, energy, engineering, policy, and
entrepreneurship. These domains wrap up the social influence of
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
54
sustainable economic growth, technology, technologies for the
development process, human capital such as education and e-
learning (especially during the COVID-19 era), natural resources
and technological forecasting, social enterprises, sustainability
and cleaner energy technology, among others. In this pursuit,
technological progression in developed economies promotes eco-
friendly output by the heartening financier to utilize modern tech-
nologies for a carbon-free environment (Omri 2018). Moreover,
developing countries should devote severe exertion to consuming
and producing modern technologies to recognize the long-run
equilibrium between GDP growth and environmental conservation
that comprises a public good. Hence, emerging domestic technolo-
gies intend to recycle GHG emissions through energy, human, and
natural resources. The series from those approaching commercial-
ization like electrocatalytic diminution to new expertise being sur-
vived in the lab atmosphere, such as CO
2
polymerization,
biohybrids, and photocatalytic, is only currently being anticipated,
technologies related to molecular appliance. Following the view of
the new growth theory, the technological revolution is a funda-
mental significance to treaty with ecological harms, generally air
contamination and atmosphere variation. Many studies have been
fascinated by this association by viewing that diminishing GHG
emissions outlay that not be able to reserve at a ‘‘reasonable” level,
not including an exploiting technological portfolio that exits extre-
mely far from what is now presented. It is observed that there are
many motives in the course of understanding the technological
innovation’s importance in diminishing GHG, specifically: the
more consumption of energy-proficient technologies, end-of-pipe
technological systems, and the modification in fuel mix (De
Bruyn, 1997).
In front of this vast attention on the function engaged in recre-
ation by technological innovation in encouraging sustainability,
some methodological and theoretical gaps distinguish in the cur-
rent literature. In theory, researchers have spotlighted the wrong
question in its place of gazing for whether technological improve-
ment is companionable with sustainable growth supports. They
have merely judged the technological advancement influence on
each aspect distant of sustainable expansion. However, they do
not execute their work with three (energy, human capital, and nat-
ural resources) in a unified structure. Hence, given this strand,
there is still a major gap regarding the capability of technological
innovation, energy consumption, human capital, and natural
resources to accomplish the environmental and socio-economic
sustainability targets in an integrated structure.
United Nations defined the 17 Sustainable Development Goals
(SDGs) and ‘‘climate action” specifically (SDGs 13) as one of them
Fig. 1. Trend analysis of candidate variables.
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
55
to save precious lives and protect the environmental quality,
mainly targeting SDGs 13 (United Nations, 2018). First, this study
intends to investigate the dynamic impact of technological innova-
tions, natural resources, renewable energy sources, and endow-
ment in human capital with the moderating role of natural
resources and human capital on GHG emissions in Mercosur coun-
tries in a multivariate framework. Second, unlike most of the pub-
lished literature, this study uses GHG emissions to accurately
picture the environment, as it reveals anthropogenic actions on
the atmosphere in soil, water, and air requirements. Third, previ-
ous studies did not use the new technological innovation index
with principal component analysis (PCA) for the Mercosur panel.
Several earlier types of research have applied longitudinal data to
examine this impact, but may well suffer from the aggregation bias
restraint (Meo et al., 2018). Our study conducts a multi-country
analysis at the disaggregated (country-wise) level. Fourth,
although all Mercosur countries have transformed their resources
from agriculture to industry-based economies, their economic
growth relies on natural resources. This study also includes the
human capital indicator (based on education return and years of
schooling) in the empirical model and incorporates natural
resources with human capital. It is essential to include the interac-
tion term in the function (natural resources with human capital),
as ecological concerns are anthropogenic and human capital and
natural resources can play a vital role in protecting environmental
excellence. More efficient and suitable policies are recommended
to increase human development and a sustainable environment.
Finally, this research applies a second-generation approach that
produces robust outcomes even in the presence of heteroscedastic-
ity, serial correlation, endogeneity, cross-sectional dependency,
and slope heterogeneity as well.
The remaining sections of this paper are structured as follows:
Section 2 contains our view of the earlier literature. Section 3
describes the data sources and the empirical econometric model.
Section 4 provides the econometric framework of this study. Sec-
tion 5 discussed the results and their interpretation, while the dis-
cussion is provided in Section 6. Finally, Section 7 presents the
conclusion, policy suggestions and study limitations.
2. Literature review
Several studies in the empirical literature on energy consump-
tion, economic growth and the environment (EEE) have used a
range of indicators to determine environmental quality: for exam-
ple, sulfur dioxide (SO
2
) in the extensive research of Danish and
Baloch (2018); Nitrous Oxide (NO
2
) used by Tiba and Belaid
(2020); and carbon dioxide (CO
2
) emissions used by (Wada et al.
2021). However, very few studies have applied GHG emissions as
a proxy for ecological deterioration (Baloch et al., 2021).
To date, the empirical literature on the link between technolog-
ical innovation and environmental degradation has proved to be
inconclusive. Several studies have been conducted since 1990 on
policies involving technological innovation and environmental
deterioration, although very few studies have investigated the
influence of expenditure on research and development (R&D) on
economic growth and environmental degradation in emerging
countries (Fisher-Vanden and Wing, 2008). Another study exami-
nes the impact of R&D expenditure in the energy and power sec-
tors on environmental degradation in developed economies, and
several studies scrutinize the varying effect of technology, such
as Li et al. (2019), who revealed that the effect of high technology
on environmental pollution and economic growth contributed to
achieving the SDGs in 30 different provinces in China. Likewise,
several other studies have investigated information and communi-
cation technology (ICT) and its link with environmental
degradation (Avom et al., 2020), while others have explored the
environmental consequences of technology policy and regulations
(Lewis, 2016). Using the Method of Moment Quantile-Regression
(MM-QR), Afshan et al. (2022) examined the influence of environ-
mental policy stringency, ecological innovation, and renewable
energy on environmental pollution in the Organisation for Eco-
nomic Cooperation and Development (OECD) region from 1990
to 2017. The econometric outcomes disclosed an adverse link
between the studied indicators with the environmental pollution
across all quantiles. Furthermore, Yi (2012) revealed the impact
of environmental innovations and regulations, beginning with
technological innovations, on reducing environmental pollution
in the Chinese economy. Several common types of research
addressed other aspects of innovation and technology applied to
environmental degradation (Ganda, 2019; Usman and Hammar,
2021). Khattak et al. (2020) examined the dynamic relationship
between renewable energy, technological advancement and envi-
ronmental degradation in Brazil, Russia, India, China and South
Africa (BRICS) countries and found that technological innovation
and economic growth significantly contribute to increasing envi-
ronmental pollution in India, Russia, China and South Africa. In
G7 countries, Churchill et al. (2019) explored the association
between R&D intensity and carbon emission from 1870 to 2014,
revealing a diverse impact of technological innovation on environ-
mental degradation. More specifically, technological innovation
activities extensively contribute to enhancing environmental per-
formance. Sinha et al. (2020) studied the interaction between tech-
nological innovation, economic growth, renewable energy sources
and population capacity on the environmental performance index,
and showed that technological innovation significantly increases
environmental deterioration in N-11 countries. The governments
of these countries should enable/produce more employment
opportunities in activities related to technological innovations. As
most of the N-11 countries are in their initial growth stage and
use conventional technology, they produce environmental pollu-
tion. Furthermore, Baloch et al. (2021) examined the link between
economic and financial growth, energy innovation, and GHG emis-
sions from 1990 to 2017 for OECD economies. The empirical results
explore that financial and economic growth endorse energy inno-
vation and protect ecological eminence.
After this conclusive evidence of the negative impact of fossil
fuels on environmental quality, the trend in the investigation is
to study the impact of renewable energy on the environment. In
the Organization of the Petroleum Exporting Countries (OPEC),
Fakher (2019) explored the influence of GDP growth on environ-
mental damage from 1996 to 2016 and reported that population
intensity, energy use, and per capita economic growth significantly
increase environmental pollution in the long-run. Similarly, Usman
et al. (2020) investigated the impact of economic growth, non-
renewable and renewable energy use on environmental degrada-
tion. They found that non-renewable energy consumption and eco-
nomic growth are responsible for increasing the pollution level,
while renewable energy reduces it in the 15 most polluted coun-
tries. Bekun et al. (2021) also explored the effect of institutional
quality and renewables in E-7 countries. The second- generation
outcomes validate the EKC hypothesis in the E-7 bloc. Moreover,
renewable energy is seen as a solution to overcome the pollution
level in these economies over the sampled period. Using the aug-
mented mean group (AMG) estimator, Solarin and Al-Mulali
(2018) found that environmental degradation is triggered by
energy use and economic growth in 20 countries. Destek and
Sinha (2020) conducted a study to determine the influence of
renewable energy on the ecological footprint in 24 OECD countries
over the period 1980–2014. As expected, renewable energy
increases environmental quality in the region, while non-
renewable energy consumption significantly reduces it. In very
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
56
recent literature, Ozturk et al. (2022a) have called for the use of
renewable energy to curb environmental pollution level in the
Saudi Arabia.
Against this background, researchers have begun to focus on
human capital and natural resources. Several empirical studies
have examined the association between human capital, natural
resources and environmental degradation with diverse findings.
Zafar et al. (2019) used the autoregressive distributive lag (ARDL)
method to probe the influence of natural resources, GDP growth,
human capital, energy use and foreign direct investment (FDI)
inflows on environmental degradation in the case of the United
States during the period from 1970 to 2015. Their outcomes show
that human capital, natural resources and FDI significantly reduce
environmental pollution, while GDP growth and energy use con-
tribute to increasing the pollution level. A one-way causality is also
observed from natural resources to environmental degradation.
Considering the BRICS countries, Nathaniel et al. (2021a) studied
the impact of economic growth, natural resources, renewable
energy, human capital and urban population on environmental
degradation from 1992 to 2016. The findings revealed that renew-
able energy, urbanization and human capital promote environ-
mental quality.
In contrast, economic growth and natural resource rent increase
environmental pollution in the long-run. By applying the ARDL
method, Hassan et al. (2019) highlighted the relationship between
natural resources, human capital, GDP growth and environmental
damage in Pakistan from 1971 to 2014. Their findings show that
GDP growth and natural resources significantly raise pollution
levels. Likewise, Ulucak and Bilgili (2018) studied the effect of
human capital on environmental deficit within the Environmental
Kuznets Curve (EKC) framework by isolating the countries into
high-, middle- and low-income countries. This study found that
human capital is mainly responsible for reducing environmental
degradation for countries in all income categories.
In conclusion, we found that energy use, natural resources and
GDP per capita are the key determinants of GHG emissions. The
very small number of available empirical studies on the association
between these variables motivated us to investigate this gap in the
literature. This study used a second-generation approach to exam-
ine the combination of technological innovation and human capital
on GHG emissions by integrating economic growth, natural
resources, energy use and the interaction of natural resources with
human capital in the primary GHG emissions function.
3. Data sources and empirical econometric model
3.1. Data sources
The econometric analysis data are extracted from the World
Development Indicators (WDI) and the Penn World Table (PWT).
The variables selected for this study are greenhouse gas emissions,
economic growth, renewable energy, human capital, natural
resources, and three different technological innovation indicators
(TECH): total patent applications, total trademark applications
and total technical cooperation grants. The human capital index
was taken from the PWT database, which is more comprehensive
and widespread than earlier research studies, and was designed
by comparing and combining the datasets of Cohen and Leker
(2014) and Barro and Lee (2013) for years of schooling. The educa-
tion returns rate was calculated by means of the Mincer equation,
although the most recent human capital index is applied in the
recent studies of Ahmed and Wang (2019) and Ahmed et al.
(2020). TECH consists of the three most important technological,
research and development indicators following Sinha et al. (2020).
(i) Total number of patent applications
(ii) Total number of trademark applications
(iii) Technical cooperation grant in current US$
TECH
it
¼b
0it
þb
1it
PAT A
it
þb
2it
TRAD A
it
þb
3it
TEC G
it
þ
e
it
ð1Þ
Economic growth is measured as gross domestic product per
capita (GDP) in constant 2010 US dollars, renewable energy con-
sumption is calculated as a percentage of total final energy use,
and natural resource rent is measured in % of GDP. Longitudinal
data for the empirical analysis were acquired from 1990 to 2018
for five Mercosur countries (Argentina, Brazil, Paraguay, Uruguay
and Venezuela). The data span depends entirely upon the availabil-
ity of data on the TECH and REU indicators. Table 1 provides more
detailed information about the data sources for each variable.
3.1.1. Principal component analysis
This study used the modern technological innovation index
(TECH), which is the weighted index of all three technological
innovation indicators, and principal component analysis (PCA) to
generate the weighted technological innovation index. This TECH
index generates a relatively single weighted index that includes
all the technological innovation variables. The country-wise
weighting of all three technological innovation indicators is
expressed in Table 2.
The PCA results for the technological innovation index are listed
in Table 3. Panel A in Table 3 shows that the highest eigenvalue is
2.786244 of the initial factor, the second-factor value is 0.181569,
and the minimum/lowest factor value is 0.032187. The first factor
is the patent application, which has a wide proportional variation
of 92.87 %, followed by the proportion of the second and third fac-
tors by 6.05 % and 1.07 %, respectively. Panel B in Table 3 shows the
eigenvectors (loadings) for all three principal component loading
factors, namely PC1, PC2 and PC3. The two extensive factor load-
ings, i.e., PC2 and PC3, have a negative value and quite low values
(in most cases). As a result, we applied only PC1 factor loading to
build the technological innovation index. Finally, panel C in Table 3
shows the ordinary correlation between all three technological
innovation variables, with a strong and positive correlation
between technological grants and trademark and patent applica-
tions (i.e., 0.945709 and 0.911145). A robust positive correlation
was also found between technical grants and trademark applica-
tions (i.e., 0.820994) in the case of Mercosur countries. The scree
plot (ordered eigenvalues) after conducting the PCA can also be
seen in Fig. 2.
Table 1
Variables Description and data sources.
Variables Acronym Definition Sources
Greenhouse gases GHGs Total Kt of CO
2
equivalent WDI
Patent applications PAT_A Total numbers of patent
applications
WDI
Trademark
applications
TRAD_A Total number of trademark
applications
WDI
Technical
cooperation
grants
TEC_G Technical cooperation grants in
current US$
WDI
Economic growth GDP per capita GDP constant 2010
US dollars
WDI
Renewable energy
utilization
REU % of total final energy
consumption
WDI
Human capital HC Human Capital Index
calculated in terms of
schooling years and return to
education
PWT
Natural resource
rent
NR Natural resource rent in % of
GDP
WDI
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
57
3.2. Proposed econometric model
This study builds on the previous work of Usman and Hammar
(2021) and Sinha et al. (2020) by including human capital and the
amount of natural resources with the interaction term of human
capital and natural resources. The econometric model in this study
can be articulated in Eq. (2) as:
GHGs
it
¼f TECH
it
;GDP
it
;REU
it
;HC
it
;NR
it
;HC
NR
it
ðÞð2Þ
In order to reduce the probability of multicollinearity and data
sharpness, the natural log transformation of Eq. (2) can be devel-
oped in Eq. (3) as follows:
ln GHGs
it
ðÞ¼d
0
þd
1
ln TECH
it
ðÞþd
2
ln GDP
it
ðÞþd
3
ln REU
it
ðÞ
þd
4
ln HC
it
ðÞþd
5
ln NR
it
ðÞþd
6
ln HC
NR
it
ðÞþ
l
it
ð3Þ
Where i, t and
l
it
refer to the Mercosur countries, time period
and the stochastic error term. Additionally, d
0
denotes the constant
term, and d
1
;d
2
;d
3
;d
4
;d
5
;and d
6
show the long-run elasticity of
regressors on GHG emissions. The pre-estimation or expected sign
of the TECH coefficient will be negative due to the protective nat-
ure of TECH, which will enhance environmental quality. The main
expected sign of the economic growth coefficient will be positive
as more production requires greater energy use. We predict a neg-
ative coefficient for renewable energy due to the constructive role
of renewable energy in reducing environmental degradation. The
expected sign of human capital on GHG emissions is negative.
The estimated coefficient of human capital likely increases envi-
ronmental quality due to its eco-friendly nature. The role of natural
resources is expected to be negative because natural resource
abundance is sufficient to meet the country’s domestic energy pre-
requisites, decrease its reliance on conventional fossil fuel imports,
and protect environmental sustainability (Ulucak and Khan, 2020).
In contrast, natural resources are observed to have a negative influ-
ence on environmental degradation (Bekun et al., 2019).
4. Methodological framework
4.1. Cross-section dependency tests
Of the many issues associated with estimating the longitudinal
dataset, cross-sectional dependence (CSD) is considered a signifi-
cant problem that should be tackled before embarking on the sub-
sequent empirical analysis. If this problem is not considered, CSD
can produce bias and inconsistent and inefficient parameters with
misleading information (Ng et al., 2020). CSD will occur in a data-
set due to unobserved mutual shocks, externalities, latent common
issues, geographical effects and financial and economic assimila-
tion. In order to address the problem of CSD, this study used four
different CSD tests, namely, Pesaran scaled LM, Breusch-Pagan
LM, Pesaran CSD and bias-corrected scaled LM as developed by
Pesaran (2004), Breusch and Pagan (1980), Pesaran et al. (2008)
and Baltagi et al. (2012). The mathematical expression of the CSD
test is presented in Eq. (4):
CSD ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2T
NN1ðÞ
sX
N1
i¼1
X
N
j¼iþ1
b
q
ij
!
N0;1ðÞi;jð4Þ
CSD = (1, 2, 3 ......... 50 ....... N)
R¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2T
NN1ðÞ
sX
N1
i¼1
X
N
j¼iþ1
b
W
ij
!
TKðÞ
b
W
2
ij
TKðÞ
b
W
2
ij
Var T KðÞ
b
W
2
ij
2
43
5ð5Þ
4.2. Panel stationary tests
After inspecting the CSD through cross-sections, the next
empirical analysis is to check the stationarity property of the vari-
ables. This study provides newly developed panel unit root tests as
proposed by Pesaran (2007), namely Cross-section Im, Pesaran and
Shin (CIPS), and Cross-section Augmented Dickey-Fuller (CADF)
tests that can address the problem of CSD and slope heterogeneity
in a particular panel dataset. Moreover, Breitung and Das (2005)
proposed another unit root test to tackle the problems of CSD
and heterogeneity (Usman and Hammar, 2021). The mathematical
expression of the CADF panel stationary test is articulated in Eq. (6)
as:
D
X
it
¼b
i
þ
p
i
x
i;t1
þk
i
x
t1
þd
i
D
x
t
þ
e
it
ð6Þ
Including the one lag value (t-1) in the result of Eq. (6), the sub-
sequent Eq. (7) is expressed as:
Table 3
Principal component analysis (PCA) for technological innovation index.
Panel A: Eigenvalues of the observed matrix
Eigenvalues: (Sum = 3, Average = 1)
Number Value Difference Proportion Cumulative Value Cumulative Proportion
1 2.786244 2.604676 0.9287 2.786244 0.9287
2 0.181569 0.149382 0.0605 2.967813 0.9893
3 0.032187 — 0.0107 3.000000 1.0000
Panel B: Eigenvectors (loadings)
Variables PC 1 PC 2 PC 3
PAT_A 0.592307 0.103902 0.798985
TEC_G 0.573637 0.641973 0.508735
TRAD_A 0.565785 0.759655 0.320643
Panel C: Observed correlation
Variables PAT_A TEC-G TRAD_A
PAT_A 1.000000 — —
TEC_G 0.945709 1.000000 —
TRAD_A 0.911145 0.820994 1.000000
Table 2
Weights of technological innovation indicators.
Countries W1 (PAT) W2 (T-GRA) W3 (TRD)
Argentina 0.32495 0.35921 0.31584
Brazil 0.42281 0.18588 0.39128
Paraguay 0.22656 0.41911 0.35431
Uruguay 0.38546 0.37975 0.23496
Venezuela 0.34051 0.31628 0.31621
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
58
D
X
it
¼b
i
þ
p
i
x
i;t1
þk
i
x
t1
þX
p
j¼0
d
ij
D
x
tj
þX
p
j¼1
U
ij
D
x
i;tj
þ
e
it
ð7Þ
Where x
tj
and
D
x
i;tj
denote the mean value of the lag level of
each Mercosur country and first difference I(1) operator. The CIPS
unit root test is also articulated in Eq. (8) as:
CIPS ¼N
1
X
N
i¼1
p
i
N;TðÞ ð8Þ
Where the coefficient
p
i
N;TðÞindicates the panel CADF unit
root test statistics that can be replaced by the term shown in Eq.
(9) as:
CIPS ¼N
1
X
N
i¼1
CADF
i
ð9Þ
4.3. Westerlund bootstrap cointegration test
In this article, the Westerlund (2007) cointegration test is used
to verify a long-run relationship among the variables. Westerlund
proposed an error correction method (ECM) by means of a long-run
cointegration test to estimate the panel data that generate consis-
tent and reliable findings in the presence of CSD. The Westerlund
cointegration test is based on the two group-mean test statistics
(Gt, Ga) and two-panel test statistics (Pt, Pa). All Westerlund’s
group-mean and panel test statistics are proposed from the ECM
model, which is also presented in Eq. (10) as follows:
D
X
i;t
¼
a
0
i
d
i
þb
i
X
i;t1
d
0
i
Y
i;t1
þX
q
j¼1
b
i;j
D
X
i;tj
þX
q
j¼0
g
i;j
D
Y
i;tj
þ
e
i;t
ð10Þ
Where b
i
denotes the speed of adjustment that the system cor-
rects back to the long-run equilibrium. However, Westerlund
error-correction-based cointegration tests consist of four different
test statistics such as G
t
,G
a
,P
t
and P
a.
The two group statistics G
t
and G
a
are calculated by Eq. (11) and (12) as follows:
G
s
¼1
NX
N
i¼1
d
i
SE b
d
i
ð11Þ
G
a
¼1
NX
N
i¼1
Td
i
d
0
i
1ðÞ ð12Þ
The two panel cointegration test statistics (P
t
and P
a
) are calcu-
lated by Eqs. (13) and (14) as follows:
P
s
¼b
d
i
SE b
d
i
ð13Þ
P
a
¼b
d
Tð14Þ
4.4. Panel long-run elasticity estimation approaches
4.4.1. Driscoll and Kraay (D-K) regression tests
This research applied the Driscoll and Kraay (1998) estimation
approach to investigate the impact of analyzed regressors on
GHG emissions. This estimation technique is non-parametric,
which produces more reliable outcomes even over long (T > N)
periods. The D-K approach is also suitable for the balanced and
unbalanced panel dataset, besides having many other advantages.
For instance, it provides robust estimators, tackles heteroscedastic-
ity issues, deals with missing values and spatial dependency and is
also appropriate for addressing the CSD and slope heterogeneity
problem (Driscoll and Kraay, 1998; Baloch et al., 2020). This study,
therefore, benefits from using the D-K algorithm for OLS assess-
ment through a simple linear model. The linear mathematical
expression in the D-K approach can be written in Eq. (15) as:
lnGHGs
it
¼
p
0
þX
it
dþ
l
it
i¼1;2;3;N
t¼1;2;3;T
ð15Þ
Where lnGHGs
it
denotes the explained variable and X
it
presents
the regressors (i.e., TECH
it
;GDP
it
;REU
it
;HC
it
;NR
it
;HC
NR
it
). By
evaluating the D-K regression coefficient for robustness and valid-
ity, the study also applied the fully modified ordinary least square
(FMOLS) and dynamic ordinary least square (DOLS) approaches.
4.4.2. Full modified ordinary least square (FMOLS)
We use the fully modified ordinary least square (FMOLS) esti-
mator to examine the long-run elasticity. This econometric tech-
nique is more suitable, reliable and appropriate as it addresses
the problem of serial correlation and endogeneity. The functional
Fig. 2. The graph explains the correlation between different technological indicators.
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
59
form of the FMOLS estimation technique is shown in Eq. (16) fol-
lowing Pedroni (2001):
b
b
FMOLS
¼N
1
X
N
n¼1
b
b
FMOLS;n
ð16Þ
Where b
b
FMOLS;n
indicates the FMOLS estimators of all cross-
sections, and this FMOLS estimator t-statistic is represented in
Eq. (17) as:
tb
b
FMOLS
¼N
1=2
X
N
n¼1
t
b
FMOLS;n
ð17Þ
Where tb
b
FMOLS
denotes the t-statistic of the FMOLS estimator.
4.4.3. Dynamic ordinary least square (DOLS)
To estimate long-run elasticity, the DOLS parametric estimation
approach tackles the issue of serial correlation and endogeneity
(Herzer and Donaubauer, 2017). It is preferable to the simple OLS
and FMOLS approach when using the Monte Carlo simulation. This
study applies the DOLS technique, which is shown in Eq. (18):
y
it
¼bþ
g
X
it
X
N
2
j¼N
i
Z
ij
D
X
itþj
þ
l
it
ð18Þ
Where the maximum lag length of the OLS estimator distribu-
tion is presented by
g
,q
1.
The term
D
X
it
shows the first difference
in the explanatory variables to eliminate the endogeneity issue,
and
l
it
denotes the error term.
4.5. Dumitrescu and Hurlin (D-H) panel non-causality test
After verifying the CSD, unit root issues, and long-run cointegra-
tion the next step in the econometric analysis is to detect the flow
of the panel causality relationship among the variables (Jahanger
et al., 2022; Usman and Makhdum, 2021). This study, therefore,
applied the Dumitrescu and Hurlin (D-H) panel causality test
developed by Dumitrescu and Hurlin (2012) due to the existence
of CSD and slope heterogeneity across Mercosur countries. The
D-H panel method gives more efficient results even in an unbal-
anced panel dataset. This test is a better technique than standard
causality tests and provides efficient and reliable panel test statis-
tics for small samples, even with the existence of possible CSD
(Chandio et al., 2022). The D-H causality test also includes the
mean Wald statistic of non-causality across Mercosur countries.
The mathematical expression of the D-H panel causality test is
expressed in Eq. (19) as follows:
Y
it
¼h
i
þX
J
j¼1
p
j
i
Y
itjðÞ
þX
J
j¼1
d
j
i
x
itjðÞ
þ
l
it
ð19Þ
Where Y and X represent the observables, and the terms
p
j
i
and
d
j
i
denote the auto-regressive (AR) parameters and regression coef-
ficients, respectively. The null hypothesis (H
0
) of the panel causal-
ity test is tested through the Wald W
HNC
N:T
test statistic as
expressed in Eq. (20):
W
HNC
N:T
¼N
1
X
N
i¼1
W
i;T
ð20Þ
H
0
and the alternative (H
1
) hypothesis of this function is
expressed in Eqs. (21) and (22):
H
0
:h
i
¼0for8ið21Þ
H
1
:h
i
–0foralli ¼N
1
þ1;2;3;::Nh
i
¼0foralli
f
¼1;2;3;;N
1
gð22Þ
5. Results and discussion
5.1. Descriptive statistics and correlation matrix
Table 4 shows the descriptive information of the variables ana-
lyzed for Mercosur countries, specifically the mean and median
values, highest and lowest values, standard deviation, skewness,
kurtosis and Jarque-Bera (J-B) test statistics. The mean value of
lnGHG emissions is 12.1853, the lowest value is 9.57372, and the
highest value is 14.9951 over the given period. Another important
variable, namely lnTECH, has a mean value of 15.4353; its lowest
value is 14.1414 and its highest value is 16.5441. These statistics
show a very low level of technological innovation activities com-
pared to the figure worldwide. The mean value of lnGDP is
9.04659 US dollars; the lowest value is 8.13336, and the highest
value is 9.61229. The mean of lnREU is 3.34784, the lowest value
is 2.02942 and the highest value is 4.37134, indicating that renew-
able energy is insufficient for these countries. However, natural
resources are abundant in this region, as the mean value of lnNR
is 1.00143, the lowest value is 1.73792 and the highest value is
3.39416. Finally, the mean value of lnHC is a 2.43128 % share of
lnGDP, the highest value is 3.06546 and the lowest value is
1.71669. The data for these variables also indicate the peak of each
observation as revealed by kurtosis, and the normal distribution
pattern was determined with the Jarque-Bera test. The country-
wise descriptive statistics are listed in Appendix A1.
The corresponding Pearson bivariate correlation matrixes for all
the variables analyzed are expressed in Table 5. The lnGHG is pos-
itive and significantly correlated with lnTECH and lnNR; lnGDP and
lnHC*lnNR are also consistent and positively correlated with lnGHG
emissions. In contrast, lnGHG emissions are adversely correlated
with lnREU and lnHC, and lnTECH is adversely correlated with all
the variables except lnREU. Similarly, lnHC is adversely correlated
with all the variables except lnGDP per capita. The interaction term
lnHC*lnNR is positively correlated with lnGDP and lnNR.
5.2. Results of cross-sectional dependence (CSD) tests
This study applied the four different cross-sectional (CSD) tests
as developed by Pesaran (2004) to explore the potential depen-
dence among the series across Mercosur countries: greenhouse
gas emissions, technological innovations, real per capita income,
renewable energy consumption, natural resources and the interac-
tion term of human capital and natural resource. Table 6 contains
the results of all four CSD tests, and shows that the Null hypothesis
(H
0
) of no CSD is rejected for the whole dataset; that is, it indicates
that the presence of cross-correlation among the variables ana-
lyzed and shocks occurring in one Mercosur country may spillover
to other countries.
5.3. Results of panel unit root tests
After testing the possible CSD problem in the dataset, all the
variables must be checked for stationarity to ensure the regression
estimation method is reliable and appropriate (second-
generation). There are three different stationarity tests to estimate
panel data: the CIPS, CADF and Breitung and Das panel unit root
tests, which have recently gained a reputation in the panel estima-
tion literature. Table 7 shows the results of these tests on the data-
set. The H
0
of these stationarity tests is that the variables follow the
unit root process, meaning that their data has non-stationarity. All
the series were found to have non-stationarity at level I(0); how-
ever, all the variables became stationary at their first integration
order I(1), indicating there is no unit root problem among the
variables.
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
60
Table 4
Descriptive statistics.
Stats. lnGHGs lnTECH lnGDP lnREU lnNR lnHC
Mean 12.1853 15.4353 9.04659 3.34784 1.00143 2.43128
Median 12.4896 15.5729 9.11509 3.73389 0.80024 2.49754
Maximum 14.9951 16.5441 9.61229 4.37134 3.39416 3.06546
Minimum 9.57372 14.1414 8.13336 2.02942 1.73792 1.71669
Std. Dev. 1.50976 0.68037 0.43072 0.75559 1.13256 0.32072
Skewness 0.22484 0.16401 0.80721 0.31592 0.25051 0.26458
Kurtosis 2.02048 1.85755 2.59245 1.40797 2.65349 2.24622
Jarque-Bera 7.01843 8.53561 16.7503 17.7249 2.24206 5.12456
Probability 0.02992 0.01401 0.00523 0.00014 0.32594 0.07713
Sum 1766.87 2238.07 1311.75 485.438 145.208 352.533
Sum Sq. Dev. 328.233 66.6597 26.7149 82.2119 184.709 14.8126
Table 5
Pearson correlation matrix.
Series lnGHGs lnTECH lnGDP lnREU lnNR lnHC
lnGHGs 1.0000
lnTECH 0.6334*
[9.788]
1.0000
lnGDP 0.3545*
[4.535]
0.3705*
[-4.763]
1.0000
lnREU 0.2897*
[-3.619]
0.1499***
[1.802]
0.5307*
[-7.483]
1.0000
lnNR 0.4325*
[5.737]
0.1284***
[-1.947]
0.4305*
[5.747]
0.4762*
[-6.472]
1.0000
lnHC 0.5314*
[-3.616]
0.1829**
[-2.226]
0.3269*
[4.176]
0.3727*
[-4.832]
0.1217***
[-1.817]
1.0000
Note: *, ** & *** denote the significance 1%, 5% and 10% level respectively. The t-statistics are presented in [ ].
Table 6
Results of cross-sectional dependence tests.
Variables Pesaran scaled LM Breusch-Pagan LM Pesaran CD Bias-corrected scaled LM
Statistic P-value Statistic P-value Statistic P-value Statistic P-value
lnGHGs 23.2613* 0.000 119.0128* 0.000 4.60312* 0.000 23.1729* 0.000
lnTECH 15.4698* 0.000 84.18355* 0.000 6.20914* 0.000 15.3805* 0.000
lnGDP 41.9284* 0.000 202.5098* 0.000 14.1557* 0.000 41.8392* 0.000
lnREU 5.81221* 0.000 40.99293* 0.000 2.4305** 0.015 5.72292* 0.000
lnHC 57.9233* 0.000 274.0409* 0.000 16.5493* 0.000 57.8341* 0.000
lnNR 12.3351* 0.000 70.16452* 0.000 1.82133*** 0.068 12.2458* 0.000
lnHC*lnNR 11.2208* 0.000 65.18093* 0.000 2.24414** 0.024 11.1315* 0.000
Note: *, ** & *** show the significance level of 1%, 5% and 10% respectively.
Table 7
Results of panel unit root tests.
Variables Intercept Intercept and Trend
CIPS CADF Breitung & Das CIPS CADF Breitung & Das
lnGHGs 1.332 1.375 1.572 2.074 1.004 1.572
D
lnGHGs 5.812* 4.400* 6.955* 4.074* 4.233* 6.955*
lnTECH 2.002 1.765 1.879** 2.782*** 3.123** 1.101
D
lnTECH 5.085* 3.846* 6.191* 5.171* 3.843* 4.662*
lnGDP 1.959 1.793 1.786 1.912 1.747 0.056
D
lnGDP 4.600* 2.620** 3.764* 4.990* 4.076* 4.559*
lnREU 1.984 1.782 1.073 2.678 2.576 1.577***
D
lnREU 5.275* 2.959* 6.835* 5.540* 4.948* 8.084*
lnHC 2.063 1.706 1.732 1.316 1.506 3.912
D
lnHC 3.913* 4.817* 4.985* 4.558* 4.859* 2.309**
lnNR 1.852 1.411 1.698 2.622 2.650 1.207
D
lnNR 4.777* 3.858* 3.333* 4.711* 4.055* 3.539*
lnHC*lnNR 1.728 1.404 0.375 2.560 2.670 0.784
D
lnHC*lnNR 5.475* 3.619* 2.358* 5.409* 3.865* 3.577*
Critical Values 1 % 5 % 10 % 1 % 5 % 10 %
2.12 2.25 2.51 2.76 2.94 3.32
Note: *, ** & *** show the significance level of 1%, 5% and 10% respectively. The symbol ‘‘
D
” shows the first difference.
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
61
5.4. Results of the Westerlund bootstrap cointegration test
Table 8 reports the findings of the Westerlund ECM panel coin-
tegration test. Due to the statistical significance of the test statis-
tics, it was deemed appropriate to reject the H
0
of no
cointegration. The results show the presence of a long-run associ-
ation among series, and the parameter of error correction (b
d) from
Eq. (14) as derived by b
d¼
P
a
T
¼
14:649
29
¼0:5051 from the envi-
ronmental model. This value indicates that around 50 % of the
GHG emission level will return to the equilibrium level each year.
Once the long-run cointegration linkages are confirmed, it is
imperative to detect the causality direction using more appropriate
panel data estimators.
5.5. Results of long-run elasticity estimates (overall panel)
After confirming the significant cointegration and causality flow
among variables, it is essential to estimate the long-run coefficient
of the variables in question. Table 9 shows the overall panel elas-
ticity estimates from the D-K, FMOLS and DOLS regression analy-
ses. The D-K method is used for the key estimation of the study,
which is robust in the sense that it counters the possible CSD issue
across cross-sections. The findings of the D-K regression estimates
suggest that a rise in technological innovation significantly pro-
tects environmental quality. Specifically, a 1 % increase in TECH
would cause a 0.5209 % reduction in environmental quality in
the long-term. Balsalobre-Lorente et al. (2017) observed that tech-
nological innovations in the field of energy directly affect the level
of GHG emissions, possibly because TECH significantly contributes
to the manufacture of eco-friendly and energy-efficient products,
which causes less air pollution. TECH is primarily responsible for
shifting the economy to more efficient energy sources, thus reduc-
ing GHG emissions and protecting environmental quality without
imposing higher taxes on domestic firms to improve environmen-
tal sustainability. Mercosur countries should organize a range of
key events to enhance innovation aimed at reducing GHG emis-
sions. These findings support the previous studies of Churchill
et al. (2019).
The economic growth coefficient also indicates that GDP is the
main driver of increasing emission levels in Mercosur countries.
Notably, a 1 % expansion in economic growth will represent a
0.6764 % rise in environmental damages in the long-run for the
Mercosur region. Consistent with these results, it can be seen that
the current rate of economic growth in Mercosur countries is sig-
nificantly reducing unemployment levels, but at the expense of
environmental damage. These findings are rational for Mercosur
economies, as this region is currently at its initial development
stage, where economic growth is mainly responsible for increasing
poverty, income inequality and unemployment rates (Demir et al.,
2019; Usman and Hammar, 2021; Adedoyin et al. 2021).
The renewable energy use has a significant adverse effect on
GHG emissions. Specifically, a 1 % rise in renewable energy would
protect environmental quality by an average of 0.7458 % in the
long-run. Therefore, one policy option open to Mercosur countries
is to build an environment-related central body (consortia) and
implement various feasible initiatives to shift their dirty energy
to green, modern and alternative renewable energy sources with-
out affecting the process of GDP growth. These results are in line
with Wang and Dong (2019), Usman et al. (2021), and Ibrahim
et al. (2022).
The human capital coefficient has a significant and negative
effect on GHG emissions in all (D-K, FMOLS and DOLS) methods,
suggesting that human capital is associated with 0.8456 % protec-
tion of environmental sustainability in the Mercosur region. Inter-
estingly, human capital is mainly responsible for reducing the
overall emissions level in the Mercosur region. This result coin-
cides with the outcomes of previous studies by Ulucak and Bilgili
(2018), Zafar et al. (2019). Conversely, in the case of Pakistan,
Hassan et al. (2019) found that human capital significantly
increases GHG emissions, implying a notable association between
HC and GHG, whose existence is confirmed through ecological
modeling.
Natural resources are more significantly responsible for higher
GHG emission levels in the region. lnNR accounts for an average
0.8604 % increase in GHG emissions in the long-run, implying that
the availability of natural resources leads to more productive activ-
ities, which boosts financial resources and income growth. The
social and economic structure of Mercosur countries is based on
low institutional quality and high corruption levels, which leads
to the unproductive use of natural resources. The constructive role
of natural resources in reducing GHG emissions could be supported
by good management and sustainable choices, combined with the
production and consumption of natural resources. The reduction in
the supply of natural resources reduces environmental pressure
and allows these resources to regenerate. However, the shift from
conventional to modern technologies, including reprocessing,
value-addition, innovation, recycling and artificial resources that
replace natural resources, will boost economic growth and increase
environmental performance (Bekun et al., 2019; Ulucak and Khan,
2020).
Table 9 shows that natural resources have a higher coefficient
(0.8604 %) than human capital (0.8456 %) in the long-term. This
outcome reveals that the influence of natural resources leads to
an adverse impact of human capital on environmental degradation,
indicating the potential of human capital for sustainable environ-
mental preservation. These findings suggest that the Mercosur
countries should increase their human capital at the initial stage
of environmental degradation, known as the scale effect. As
expected, the interaction between lnHC and lnNR (lnHC*lnNR) is
significant, with the adverse sign of the coefficient (0.9934 %),
showing that lnHC can lessen the destructive influence of lnNR
Table 8
Results of Westerlund cointegration test.
Statistic Value Z-value P-value Robust P-value
Intercept
G
s
4.174* 3.113 0.001 0.000
Ga 7.391 2.433 0.993 0.598
P
s
10.820* 4.699 0.000 0.003
Pa 14.649* 5.925 0.005 0.000
Intercept and Trend
G
s
4.021* 2.048 0.020 0.000
Ga 6.465 3.409 0.993 0.855
P
s
10.675** 4.091 0.000 0.015
Pa 5.837 2.766 0.997 0.778
Note: * & ** denotes the significance 1% and 5% level respectively.
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
62
on lnGHG emissions in the region. This points to the need to
develop human capital in Mercosur, as the promotion of lnHC
can mitigate the negative effect of lnNR on environmental degrada-
tion. Assuming that human capital has a moderating role in natural
resources, one of the key objectives is to explore the key conse-
quences of reducing environmental quality. The significant and
adverse coefficient of this interaction expression (lnHC*lnNR) is
sensitive and indicates that human capital has a very significant
ability to diminish the adverse effect of natural resources on envi-
ronmental sustainability and that natural resources and human
capital can reduce environmental damage, even supposing that
natural resources primarily raise pollution levels. The interaction
term confirms that human capital is imperative for natural
resources. Nathaniel et al. (2021b) previously established a similar
linkage between Latin American and Caribbean economies.
This study also attributes this finding to positive externalities,
economies of scale and the provision of public services such as
healthcare facilities, eco-friendly infrastructure and proper waste
management to facilitate the operation, development and struc-
ture of an environment with sustainable natural resources. The
next stage is the expansion of human capital and the progression
to sustainable natural growth-driven and knowledge-based (tech-
nology) industries, and the introduction of sustainability actions in
the natural resources sector to curb environmental pollution in the
region. The graphic presentation of the econometric results is
shown in Fig. 3.
5.6. Results of long-run elasticity estimates (country-wise analysis)
The country-specific results for Mercosur countries are pre-
sented in Table 10. The FMOLS estimator reveals that technological
innovation significantly (0.306 %) enhances environmental degra-
dation in Argentina, a country with a higher capacity for industry
and technological assimilation. Industrial capacity and the increase
in human capital, leading to faster progress in technological inno-
vation, can be linked to rapid production; thus, there is a positive
long-term association between technological development and
environmental degradation (Usman and Hammar, 2021; Sinha
et al., 2020). However, technological innovation has been found
to reduce environmental degradation in all other countries. A 1 %
increase in technological innovation will significantly reduce envi-
ronmental damage by 0.258 % in Brazil and 0.273 % in Venezuela,
implying that policymakers should prioritize technological innova-
tion activities designed to achieve zero-carbon emissions by pro-
moting the transition to sustainability and incorporating
innovation and environmental interests (Foxon and Pearson,
2008). The findings for Paraguay and Uruguay reveal that techno-
logical innovation has a positive and negative but insignificant
association with lnGHG emissions. In this pursuit, one of the pos-
sible solutions is that the reformation of financial allocation for
technological innovations in the energy sector could better realize
the possible prime energy savings. Moreover, there is no precision
as to where research activities, strategies and goals commence in
both countries. The findings of technological innovation are pri-
marily linked to the amplified demand for energy-proficient goods.
It explores that technological innovations in the power sector have
encouraged customers to execute energy-efficient capital, which is
more added towards reducing fossil fuel energy resources.
The empirical findings confirm that economic growth is more
likely to increase environmental damage in the long-run in all Mer-
cosur countries. More specifically, the results also highlight that a
1 % increase in lnGDP would cause to increase lnGHG emissions by
0.633 %, 1.808 %, 0.015 %, 0.195 % and 0.263 % in Argentina, Brazil,
Paraguay, Uruguay and Venezuela respectively, implying that eco-
nomic growth in all these economies is not eco-friendly. Economic
growth has a negative effect on the protection of the environment
in all countries due to their dependence on natural gas for energy
production as a substitute for coal and can be seen to exacerbate
environmental pollution. These findings coincide with the earlier
conclusions of Usman et al. (2020), and Baloch et al. (2020) and
also they suggest that most Mercosur countries have not yet
attained the requisite level of per capita economic growth to allow
a reduction in environmental pollution.
Table 9
Results of long-run elasticity estimates (Overall Panel).
Regressors D-K regression (A) FMOLS regression (B) DOLS regression (C)
Coeff. P-value Coeff. P-value Coeff. P-value
lnTECH 0.5209* 0.000 0.6474* 0.000 0.5776* 0.007
lnGDP 0.6764* 0.005 0.9688* 0.002 0.8231*** 0.053
lnREU 0.7458* 0.000 0.6335* 0.000 0.6816** 0.012
lnHC 0.8456** 0.038 0.8602*** 0.063 0.1353*** 0.092
lnNR 0.8604* 0.000 0.5236* 0.000 0.8131* 0.000
lnHC*lnNR 0.9934* 0.001 1.5496* 0.000 1.7921* 0.000
Wald test 65.93* 0.000
R-square 0.8921 0.6333 0.9767
Note: *, ** & *** denote the significance 1%, 5% and 10% level respectively.
Fig. 3. Graphical scheme of econometric results.
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
63
In contrast, renewable energy significantly preserves environ-
mental excellence in all Mercosur countries except Paraguay. Sig-
nificantly, a 1 % increase in renewable energy consumption will
reduce environmental damage by 0.258 %, 0.988 %, 0.281 % and
0.192 % in Argentina, Brazil, Uruguay and Venezuela, respectively.
The findings reveal that all Mercosur countries consume a greater
share of renewable energy to protect environmental quality and
are moderately successful in achieving their environmental sus-
tainability targets. However, renewable energy consumption is
insufficient to curb the environmental damage to the required
extent, indicating that the recovery of the environmental sustain-
ability of the Mercosur region can only be achieved if all the coun-
tries reinforce their use of modern and renewable energy sources
such as wind, solar, tidal, solar and geothermal (Nathaniel and
Khan, 2020; Usman and Hammar, 2021; Bekun, 2022; Sadiq et al.
2022).
The association between human capital and GHG emissions is
statistically significant and negative in the region, meaning that
human capital significantly reduces per capita pollution in the long
term. Specifically, a 1 % increase in lnHC would lead to a reduction
in lnGHG emissions of 0.791 %, 0.309 %, 1.436 %, 0.481 % and
0.928 % respectively under the FMOLS approach. The constructive
role of human capital in reducing GHG emissions in Mercosur
countries may be associated with their high rates of education
and skilled labor, which ultimately enhance environmental aware-
ness and promote environmental sustainability. Awareness of the
need for environmental protection and knowledge (education) of
the environment influences environmental sustainability by ensur-
ing a high standard of living for individuals. This outcome coin-
cides with earlier research which found an increased marginal
impact of education and skilled labor on pro-environmental
actions such as energy conservation water-saving, and recycling
(Ulucak and Bilgili, 2018; Ozturk et al. 2022b).
The estimated elasticity of natural resources is statistically sig-
nificant in terms of GHG emissions and has a positive relationship
in all Mercosur countries except Argentina. This suggests that a 1 %
rise in lnNR increases lnGHG emissions by 0.143 %, 0.423 %, and
0.094 % 0.304 % in Brazil, Paraguay, Uruguay and Venezuela,
respectively, and shows that an increase in natural resources
enhances environmental damage in the long-run. Natural
resources can be considered to play a role in increasing GHG emis-
sions in Venezuela and to be directly linked with economic growth
in mechanized economies; this hastens the extraction and indefen-
sible use of natural resources and escalates the dependency on
non-renewable energy imports. Traditional energy sources are lim-
ited and unsustainable, which is the main cause of increasing envi-
ronmental pressure. In Paraguay, this action abounds to low carbon
emissions released through the economic growth process. Para-
guay state is working more effectively to protect environmental
quality. This country is also more active in controlling environmen-
tal variations by using modern and clean energy technologies and
building eco-friendly infrastructures compared to other Mercosur
countries. All the other countries in the Mercosur region could
emulate the model of Paraguay. These findings do not agree with
the previous study of Hassan et al. (2019), who investigated a sim-
ilar linkage between these variables and reported an insignificant
affiliation between natural resources and environmental dilapida-
tion in the case of Pakistan. However, the results of this study are
in line with the conclusion of Ulucak and Khan (2020), who found
that natural resources are more responsible for increasing pollu-
tion levels in the region. In contrast, Balsalobre-Lorente et al.
(2018) reported that natural resources significantly enhance envi-
ronmental quality.
5.7. Results of the Dumitrescu and Hurlin causality test
Although this article has uncovered long-run dynamics and
cointegration linkages between technical innovations, economic
growth, renewable energy utilization, human capital, natural
resources and GHG emissions, this does not prove the causality
flow (Usman and Radulescu, 2022). This study applied the D-H
panel non-causality test to determine the flow of the causality rela-
tionship between series. Table 11 shows the various flows of causal
relationships between the variables, considering for the Mercosur
panel: lnGHGs ?lnTECH, lnNR ?lnGHGs and lnTECH,
lnHC ?lnTECH, lnGDP ?lnTECH, lnHC*lnNR ?lnHC, lnGHGs,
lnREU and lnTECH. A bidirectional causality association is observed
to exist between lnGHGs MlnGDP, lnREU and lnHC, lnHC MlnNR,
lnREU and lnGDP, lnNR MlnHC*lnNR, lnREU MlnGDP,
lnTECH MlnGDP, and finally lnREU MlnGDP (see Fig. 4). These
results confirm that the abundance of natural resources plays a
key role in increasing environmental damage, and encourages
technological innovations and real economic growth (Erdog
˘an
et al., 2020). It also shows that the consumption of natural
resources is not beneficial for the long-term environmental sus-
tainability of the Mercosur countries. However, technological inno-
vations, renewable energy use and human capital significantly
influence the GHG emissions in this region, enhancing biocapacity
and further reducing GHG emission levels in the region. The results
support the argument that economic growth has a lot of ability to
increase human development, and technological innovation pro-
cess in the long-term. Moreover, the two-way causality association
between GHG emissions and human capital reveals they are intrin-
sically connected. These outcomes are also consistent with the pre-
vious literature (i.g. Nathaniel and Khan, 2020; Usman and
Hammar, 2021; Ulucak and Khan, 2020).
6. Discussion
In the course of our empirical analysis, this study examined the
impact of technological innovations, economic growth, renewable
Table 10
FMOLS results of long-run elasticity estimates (Country-wise analysis).
Variables Argentina Brazil Paraguay Uruguay Venezuela
Coeff. Prob. Coeff. Prob. Coeff. Prob. Coeff. Prob. Coeff. Prob.
lnTECH 0.306* 0.000 0.258*** 0.091 0.035 0.293 0.081 0.353 0.273* 0.004
lnGDP 0.633* 0.006 1.808** 0.033 0.015** 0.012 0.195* 0.000 0.263*** 0.082
lnREU 0.258*** 0.061 0.988* 0.000 0.281 0.201 0.281* 0.000 0.192** 0.038
lnHC 0.791** 0.019 0.309* 0.000 1.436** 0.035 0.481* 0.002 0.928* 0.000
lnNR 0.029 0.799 0.143*** 0.064 0.423*** 0.000 0.094* 0.000 0.304* 0.000
lnHC*lnNR 0.221*** 0.082 0.584* 0.000 0.881** 0.011 0.271** 0.048 0.928* 0.001
Constant 16.046* 0.000 6.108 0.332 21.382 0.783 7.869*** 0.000 9.193 0.918
R-square 0.6626 0.7319 0.4378 0.9461 0.9193
Note: *, ** & *** denote the significance 1%, 5% and 10% levelrespectively.
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
64
energy use, human capital, and natural resources on GHG emis-
sions in Mercosur countries using a heterogeneous panel data anal-
ysis. The empirical findings of this study convey into view an
extensive choice of acumens in front of us. Firstly, the empirical
results signify that technological innovations put forth adverse
effects on GHGs emissions. This depicts that consortia of Mercosur
countries should spend more budget on technological innovations
activities intended at accomplishing the targets of zero-carbon
emission. This finding coincides with the earlier studies of
Nikzad and Sedigh (2017) for Canada, Du et al. (2019) for 71 econo-
mies, Ganda (2019) for OECD countries, Mongo et al. (2021) for 15
European countries, and in contrast with Sinha et al., (2020) for N-
11 countries and Usman and Hammar (2021) for APEC countries.
During this, the utilization of renewable energy is also observed
to have an adverse impact on the environment. Currently, the Mer-
cosur economies have trying to achieve elevated economic devel-
opment, and this economic growth trajectory is an upshot of the
swift industrialization process in this region. Therefore, it can be
assumed that the technological innovations and the ecological
strategies in this region are mainly targeted at attaining manufac-
turing growth that is conquering even at the environmental excel-
lence cost by generating ambient environmental contamination in
the region. This is supported by Foxon and Pearson (2008), who
decorated that a regime of an efficient technological innovation
strategy should be designed in the course of ecological interests
and assimilating technical innovation to endorse the evolution
for sustainable development.
Similarly, Baumgartner (2011) highlighted that sustainable
technological progression and their activities ought to be based
on relevance (including the capacity to construct and integrate elu-
cidations to them), strictness (established on robust systematic
approaches and principles), dynamic and normative. Economic
growth and environmental damages are mutually being sourced
by the technical modernizations taken up in this region.
Table 11
Results of panel Dumitrescu and Hurlin causality test.
Explained Regressors
lnGHGs lnTECH lnGDP lnREU lnHC lnNR lnHC*lnNR
lnGHGs — 3.2305
[0.9474]
(0.3434)
5.1432*
[2.8939]
(0.0000)
4.4234**
[2.1247]
(0.0207)
5.2904*
[2.8412]
(0.0045)
1.9227
[-0.2549]
(0.7988)
1.4925
[-0.6586]
(0.5151)
lnTECH 6.2522*
[3.1441]
(0.0000)
—— 1.9636
[-0.2173]
(0.8280)
5.8539*
[-3.3181]
(0.0001)
1.1253
[-0.9880]
(0.3232)
3.5819
[1.2747]
(0.2039)
3.5055
[1.2023]
(0.2300)
lnGDP 4.0455***
[1.6967]
(0.0897)
4.2305***
[1.8668]
(0.0619)
—— 4.8062**
[2.3961]
(0.0166)
4.5094**
[2.1231]
(0.0337)
5.8371*
[3.5051]
(0.0023)
7.8769*
[5.5589]
(0.0000)
lnREU 5.3816*
[3.1664]
(0.0000)
4.0575***
[1.7077]
(0.0877)
5.4873*
[4.2641]
(0.0000)
—— 4.1868***
[1.8266]
(0.0678)
4.6178**
[2.2229]
(0.0262)
2.1655
[-0.0317]
(0.9747)
lnHC 6.0224*
[3.5141]
(0.0004)
5.4299*
[2.9694]
(0.0030)
4.5008**
[2.1152]
(0.0344)
4.0364***
[1.6879]
(0.0914)
—— 5.9875*
[3.4821]
(0.0005)
3.1654
[0.8875]
(0.3748)
lnNR 5.5045*
[3.0380]
(0.0024)
6.1550*
[4.8781]
(0.0000)
8.7628*
[6.0336]
(0.0000)
4.1483***
[1.7905]
(0.0733)
7.7371*
[5.0906]
(0.0000)
—— 5.9459**
[-3.1529]
(0.0249)
lnHC*lnNR 4.4206**
[2.0416]
(0.0412)
4.1731***
[-3.0246]
(0.0803)
6.0846*
[3.5714]
(0.0004)
5.1134*
[2.6785]
(0.0074)
7.1281*
[4.5306]
(0.0000)
7.0463*
[-4.1412]
(0.0000)
——
Note: H
0
: X does not homogeneously cause Y. Top values denotes the w-stats, and z-stats and P-values are presented in [ ] and () respectively. *, ** and *** indicate 1%, 5 % and
10 % significancelevel respectively.
Fig. 4. Dumitrescu and Hurlin causality relation flow.
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
65
Consequently, in this view, the current technological-based envi-
ronmental policies should be reorganized to internalize the nega-
tive externalities due to the growth course and ensure
sustainable progress in the region.
In extension to this debate, it must be considered that high
implementation expenditure of alternative and cleaner (renew-
able) energy elucidations might obstruct the pace of economic
development in several modes. This finding is impressive, and
the feasible justification is that all Mercosur countries are partici-
pants in the agreement Kyoto protocol to reduce the environmen-
tal pollution level. Conditionally, the central authority launched
executing cleaner and modern energy solutions throughout the
countries. In that case, an economy will face complexities about
more financial burden, but it might also create the current energy
infrastructure redundant. Hence, these countries should deliberate
the inherent expansion of innovation potentials to ensure eco-
nomic and environmental sustainability. Governments should
compose cleaner energy, a vital possible source of renewable
energy, as it is less harmful in conditions of higher demand for
energy use. The concept of cleaner production is related to green
energy, which is economically and ecologically feasible for the
future of the long-term and the short-term economy (Ulucak and
Khan, 2020; Khan et al., 2022). In this regard, more energy con-
sumption and production with enhancement in industrial develop-
ment could be attained, including cleaner energy resources. As
Mercosur economies have shown their effort to decrease environ-
mental pollution, additional support from the governments will
harmonize such efforts. Following this view, financial, technical,
and natural resources channelizing for R&D in discovering cleaner
and alternative energy elucidations are required with an inspection
to replace the existing fossil fuel-based energy (i.e., non-
renewable) solutions (Sinha et al., 2020). This process will help
to achieve SDGs 7, 9, 10, and 13 (i.g. clean and affordable energy,
industrial innovations, decent work, economic growth, and infras-
tructure and climate action). So far, measly production procedures
replacement might be necessary for adequate ecological awareness
amongst the people. Moreover, it is observed that human capital
exerts a statistically significant and negative effect on environmen-
tal damages, and this negative influence is more towards the
nations with high GHGs emissions. The results are similar to the
conclusion of earlier studies (i.e., Ahmed and Wang, 2019; Zafar
et al., 2019; Ahmed et al., 2020). These findings also endorse a dire
requirement for skilled labour and more towards their awareness
and educational ability to overcome environmental containment.
Few earlier empirical evidence has explored the interaction
between natural resources and human capital (Gylfason and
Zoega, 2006; Zallé, 2019). These studies concluded that human
capital impacts natural resource development, which indirectly
stimulates economic growth. Our study advances the empirical lit-
erature, offering fresh evidence of an interaction between human
capital and natural resources on environmental degradation. The
econometric results suggest that human capital generates efficient
use of natural resources (Gylfason and Zoega 2006), generating
environmental advances. On the other hand, we also need to con-
sider indirect effects related to socio-economic conditions and eth-
nic tensions, which could be significantly adverse, although the
indirect effect of human capital is positive. So, countries character-
ized by precarious socio-economic conditions over-exploit national
resources (Zallé, 2019). The result is instead a capture of natural
resource rents by the ruling elites. Otherwise, the global effect of
natural resources on sustainable growth can diverge from country
to country due to each country’s particular circumstances, and as a
consequence a severe heterogeneity bias (Zallé, 2019).
This step will help to achieve the core objectives of the 4 SDGs
targets (quality education) and can operate as a medium for
managing sustainable environmental protection and economic
development in these countries. The development of the techno-
logical system for domestic production can have a feasible organi-
zation over the ecological containment, and it is also observed in
the technological influence on environmental deterioration. The
Mercosur region has high environmental pollution and exhibits
the adverse influence of human capital and technological advance-
ment, and it illustrates that the mechanism of technical institu-
tions is increasingly converting to be eco-friendly in the region.
The increase in human capital might ultimately diminish the
non-renewable-based revelation and increase the level of energy
efficiency. This experience might encourage the policy formulation
authority to allocate economic wealth by setting sustainable envi-
ronmental and economic development. These growth strategies
might facilitate these countries to achieve the targets of SDG 4
(quality education) and SDG 7 (clean and affordable energy), and
execution of both (SDGs 4 and 7) targets will mutually assist in
accomplishing the SDGs 13 (climate action) objective.
There are other possible choices on top that may be promoted
and explored. The effect of natural resource’s GHGs emissions is
positive and statistically significant in all countries except Argen-
tina. These findings stand with previous studies (Hassan et al.,
2019; Ulucak and Khan, 2020; Nathaniel et al., 2021a;Erdog
˘an
et al., 2020) that consider a positive linkage between natural
resources and environmental degradation. This process is also
owing to both fiscal and monetary actions in this region, which
has augmented the natural resource extraction; hence indefensible
exploitation of natural resources has upsurged the non-renewable
fossil fuels (petroleum, gas, coal, etc.) imports. The excessive con-
sumption of limited natural resources enhances the burden on
environmental eminence. One such alternative is possible to orga-
nize efforts to slow down the pace of natural resource depletion in
the course of productive and better deployment and from side-to-
side investment in carbon-free projects and eco-friendly infras-
tructure. This is enviable as natural resource utilization directly
boosts real income growth and accelerates carbon emissions in
the region (Bekun et al., 2019; Zahoor et al. 2022).
Consequently, the Mercosur policymakers should reorganize
the excessive exploitation of natural resources by creating more
awareness and providing education to its people to alteration their
consumption patterns behaviour through a diminution in fishing
and deforestation, more feasible utilization of energy and water
resources, and the exercise of energy-efficient and high-quality
goods in their daily life. In addition, the industry must be inti-
mately supervised to manage the weird resource consumption;
mainly, the mining sector should be directed to make sure the con-
sumption of modern, cleaner, and energy-efficient machinery in
their daily lifestyle. In addition, natural resources integrated with
human capital exert an adverse effect on GHG emissions. These
growth policies will keenly facilitate to accomplish of the objec-
tives of SDG 4 (quality education), SDG 6 (clean water and sanita-
tion system), SDG 7 (clean and affordable energy), SDG 8 (decent
work and economic development), and SDGs 9 (industry innova-
tion and infrastructure) and implementation of these (SDGs 4, 6,
7, 8, and 9) targets will jointly help to achieve the SDG 13 (climate
action) objective for these countries.
7. Conclusion and policy recommendations
The current study examines the impact of technological innova-
tion, economic growth, renewable energy utilization, human capi-
tal, natural resources and the interaction between human capital
and natural resources on greenhouse gas emissions. This research
applies panel data from 1990 to 2018 for Mercosur countries. To
obtain consistent and efficient elasticity coefficients connected
with CSD and heterogeneity, a second-generation panel estimation
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
66
method was carried out to address the CSD and slope heterogene-
ity. The empirical findings of this robust estimation were those
technological innovations, renewable energy utilization, and
human capital significantly increase environmental quality in all
Mercosur countries except Argentina, where technological innova-
tions play a critical role in increasing environmental pollution.
However, high economic growth and abundant natural resources
significantly drive the pollution level in the Mercosur region due
to their availability, showing that natural resources are not eco-
friendly in all countries if it used unsustainably. Greater rates of
technological innovation and human capital activities have an
increased capacity to reduce pollution levels. The country-wise
findings are also consistent with the panel investigation.
This study provides several efficient policy implications based
on the results of the estimation. Initially, The empirical findings
reveal that there has been a record high consumption and exploita-
tion of natural resources in developing countries, so rules and reg-
ulations for the protection of natural resources should be used with
direct effect where achievable. Moreover, Mercosur countries
should reorganize their environmental regulations to limit the
import of conventional goods and discourage reliance on non-
renewable sources by spending more funds on energy-efficient
and cleaner-energy ventures. The negative influence of energy
use on GHG emissions could be mitigated through more technolog-
ical innovation in cleaner energy projects. To achieve this goal,
both business (private) and government activities should con-
tribute to the industrial modernization of the energy sector. Fur-
thermore, Technological innovations could be massively applied
if they are efficiently and adequately harnessed to ensure the
well-organized extraction of natural resources. Mercosur countries
must transform to relocate their resources and ensure a positive
influence of natural resources from the resource-wealthy area to
the developed sector to boost economic growth (production sector)
and consume these assets efficiently for a sustainable environ-
ment. In view of the rapidly rising levels of production and envi-
ronmental pollution in Mercosur countries, more attention must
be focused on promoting technological advancement in the indus-
trial sector in Mercosur countries in order to curb world emission
levels. With the rapid increase in technological innovation and
industrialization, Mercosur countries should limit the import of
heavily-polluting industrial goods rather than low-cost products
in their trade pattern. The research suggests that alternative
sources of energy must be encouraged in these nations. In parallel
to this, technological innovations, particularly patent applications
and grants should be encouraged. At the outset, innovative activi-
ties that slash energy consumption are enthusiastically projected.
Consequently, rules and regulations designed at upsurging energy
competence in the technological development procedure, as well
as in Public, and household construction (for instance the practice
of smart grid and smart appliances ideas) capacity assist in dimin-
ishing ecological worsening. In addition, the rising supply of clea-
ner renewable energy and its proportion in the overall energy
mix structure, in addition to transitioning from dumpy life cycle
goods and old apparatus to existing tools, might diminish techno-
logical innovations and indirectly persuade on ecological footprint.
in this regard, policymakers and the central government of these
economies must promote the emergence of cleaner and green elu-
cidation by reducing the endowment costs of eco-friendly projects
and technology. Furthermore, any tax on technological advance-
ment products must be either condensed or entirely eradicated
to increase its practice. For healthier ecological fortification, tech-
nological innovation activities should get private and government
sector support. Further, initiatives to elevate public consciousness
concerning the recompense and ecological costs of increasing tech-
nological innovation penetration in Mercosur countries should
stick to an incorporated policy structure that takes into account
the phase of sustainable economic growth in terms of urbanization,
patent application, and grants access level.
Besides, policymakers and central authorities should ensure that
natural resources are consumed in a well-organized way and dis-
courage importing products based on conventional energy sources
such as coal and other fossil fuels. Similarly, strategies targeting
economic growth, technological innovations, natural resources
and GHG emissions should be embedded in the system for periods
of over five years. Finally, environmental awareness instruction and
the activities in the current education policy should emphasize the
pleasure to be gained from the fruits of sustainable environmental
and economic development. The fossil fuel energy demand ensuing
from amplified economic development patterns will hasten the pol-
lution level production, particularly in the Mercosur countries.
Accordingly, the Mercosur nations strive to diminish the reliance
on and usage of non-renewables and append alternative energy
sources to their energy mixes to reduce environmental pollution.
The reason that alternative energy use could efficiently curb ecolog-
ical footprint, as a result, they should progress the sources of
renewable energy proportion to congregate energy desires. Another
alternative to reduce environmental pollution levels is enhancing
investment in the technical segment to offer prospects to introduce
new expertise. Consequently, innovations in the technology sector
could result in overwhelming alternative power use at lower
expenses and rising energy competence. Finally, the transition of
the mode of economic development is useful to shift from fossil
fuels to alternative and renewable energy sources to convene
energy demand and efficiently diminish the GHG emissions figure.
This study is constrained by not including cultural indicators
and institutional activities in the function of GHG emissions for
Mercosur countries, which may have diverse effects on the econ-
omy. Institutional quality and cultural activities (social and politi-
cal indicators) can play a key role in a country’s economic growth,
technological innovations, natural resources and human capital
management. Also, this study does not investigate the pollution
halo, heaven (PHH) and Environment Kuznets curve (EKC)
hypotheses of GHG emission with these analyzed variables. Data
shortening is another major limitation of this study, which could
also be extended by including different demographic indicators
for gender classification and household in the tentative model.
Finally, the findings can be used to broaden similar research (with
the same indicators) for developed and developing countries, in
which future researchers could manipulate these restrictions.
CRediT authorship contribution statement
Muhammad Usman: Conceptualization, Data curation, Soft-
ware, Visualization, Validation, Project administration, Supervi-
sion, Formal analysis, Investigation, Methodology, Resources,
Writing - original draft, Writing - review & editing. Daniel
Balsalobre-Lorente: Writing – review & editing. Atif Jahanger:
Writing – review & editing. Paiman Ahmad: Writing – review &
editing.
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.
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
67
References
Ackah, I., Kizys, R., 2015. Green growth in oil producing African countries: a panel
data analysis of renewable energy demand. Renew. Sus. Energy Rev. 50, 1157–
1166. https://doi.org/10.1016/j.rser.2015.05.030.
Adedoyin, F.F., Ozturk, I., Bekun, F.V., Agboola, P.O., Agboola, M.O., 2021. Renewable
and non-renewable energy policy simulations for abating emissions in a
complex economy: evidence from the novel dynamic ARDL. Renew. Energy 177,
1408–1420. https://doi.org/10.1016/j.renene.2021.06.018.
Afshan, S., Ozturk, I., Yaqoob, T., 2022. Facilitating renewable energy transition,
ecological innovations and stringent environmental policies to improve
ecological sustainability: Evidence from MM-QR method. Renew. Energy 196,
151–160. https://doi.org/10.1016/j.renene.2022.06.125.
Ahmed, Z., Wang, Z., 2019. Investigating the impact of human capital on the
ecological footprint in India: an empirical analysis. Environ. Sci. Pollut. Res. 26,
26782–26796. https://doi.org/10.1007/s11356-019-05911-7.
Ahmed, Z., Nathaniel, S.P., Shahbaz, M., 2020. The criticality of information and
communication technology and human capital in environmental sustainability:
evidence from Latin American and Caribbean countries. J. Clean. Prod. 286,
125529.
Ali, S., Yan, Q., Razzaq, A., Khan, I., Irfan, M., 2022. Modeling factors of biogas
technology adoption: a roadmap towards environmental sustainability and
green revolution. Environ. Sci. Pollut. Res. 1–23. https://doi.org/10.1007/
s11356-022-22894-0.
Alola, A.A., Ozturk, I., Bekun, F.V., 2021. Is clean energy prosperity and technological
innovation rapidly mitigating sustainable energy-development deficit in
selected sub-Saharan Africa? A myth or reality. Energy Policy 158,. https://
doi.org/10.1016/j.enpol.2021.112520 112520.
Avom, D., Nkengfack, H., Fotio, H.K., Totouom, A., 2020. ICT and environmental
quality in Sub-Saharan Africa: effects and transmission channels. Tech. Forec.
Soci. Change 155,. https://doi.org/10.1016/j.techfore.2020.120028 120028.
Azam, M., Khan, A.Q., 2016. Testing the Environmental Kuznets Curve hypothesis: a
comparative empirical study for low, lower middle, upper middle and high
income countries. Renew. Sustain. Energy Rev. 63, 556–567. https://doi.org/
10.1016/j.rser.2016.05.052.
Baloch, M.A., Ozturk, I., Bekun, F.V., Khan, D., 2021. Modeling the dynamic linkage
between financial development, energy innovation, and environmental quality:
does globalization matter? Bus Strategy Environ. 30 (1), 176–184. https://doi.
org/10.1002/bse.2615.
Baloch, M.A., Danish, Khan, S.-D., Ulucak, Z.Sß., Ahmad, A., 2020. Analyzing the
relationship between poverty, income inequality, and CO2 emission in Sub-
Saharan African countries. Sci. Total Env. 740, 139867.
Balsalobre-Lorente, D., Shahbaz, M., Ponz-Tienda, J.L., Cantos-Cantos, J.M., 2017.
Energy innovation in the environmental Kuznets curve (EKC): a theoretical
approach. In Carbon footprint and the industrial life cycle 243–268. https://doi.
org/10.1007/978-3-319-54984-2_11.
Balsalobre-Lorente, D., Shahbaz, M., Roubaud, D., Farhani, S., 2018. How economic
growth, renewable electricity and natural resources contribute to CO
2
Appendix A1. Descriptive statistics (country-wise)
lnGHGs lnTECH lnGDP lnREU lnHC lnNR lnHC*lnNR
Argentina
Mean 12.72991 15.76901 9.078044 2.319905 2.762336 0.860062 0.984706
Median 12.78936 15.73353 9.056940 2.321785 2.761936 0.878664 0.937286
Maximum 12.92757 16.11831 9.294986 2.586860 3.065463 1.796702 1.484634
Minimum 12.49334 15.24506 8.739650 2.029422 2.529030 0.161913 0.557494
Std. Dev. 0.143146 0.234890 0.163504 0.131608 0.153725 0.628959 0.305590
Skewness 0.476087 0.320304 0.268940 0.026915 0.348450 0.038296 0.208050
Kurtosis 1.725699 2.559515 1.904299 2.759307 2.052879 1.543582 1.596464
Brazil
Mean 14.51358 16.33523 9.171801 3.808641 2.263208 1.071760 0.965742
Median 14.31579 16.33705 9.142708 3.813498 2.213049 1.182639 1.070495
Maximum 14.99519 16.54415 9.392119 3.909313 3.015468 1.821502 1.454610
Minimum 14.00776 15.98293 8.960822 3.710305 1.716691 0.190388 0.449719
Std. Dev. 0.336728 0.142814 0.141338 0.058782 0.392693 0.512160 0.340037
Skewness 0.219786 0.407950 0.161757 0.021586 0.370168 0.433596 0.313367
Kurtosis 1.520023 2.644235 1.575772 1.808149 1.966287 1.942311 1.638390
Paraguay
Mean 10.85829 15.65256 8.297481 4.211370 2.266385 0.589118 0.718520
Median 10.99550 15.64772 8.258886 4.205399 2.237154 0.552731 0.700108
Maximum 11.83917 15.99565 8.599788 4.371348 2.632095 0.993939 0.865983
Minimum 9.573727 15.35570 8.133362 4.119402 2.036359 0.189212 0.565897
Std. Dev. 0.579740 0.166705 0.138684 0.068268 0.193242 0.226972 0.078755
Skewness 0.786571 0.022991 0.835565 0.655398 0.479584 0.362859 0.367476
Kurtosis 3.168447 2.391763 2.411522 2.980078 1.853859 2.013592 2.394190
Uruguay
Mean 10.36971 14.65034 9.204942 3.798734 2.535003 0.302910 0.529127
Median 10.39472 14.54031 9.128200 3.763808 2.541529 0.278785 0.496717
Maximum 10.44109 15.22205 9.612299 4.180495 2.753455 0.709184 0.835394
Minimum 10.17013 14.14142 8.836104 3.505511 2.345355 1.737928 0.307804
Std. Dev. 0.070096 0.334533 0.239617 0.172810 0.116172 0.707781 0.186491
Skewness 0.993069 0.197255 0.346025 0.657390 0.004147 0.283187 0.240978
Kurtosis 3.453186 1.741849 1.771995 2.676161 2.213611 1.871938 1.424263
Venezuela
Mean 12.45521 14.76799 9.480703 2.600594 2.329408 2.789141 2.237451
Median 12.48964 14.76750 9.476372 2.602995 2.307681 2.805494 2.222057
Maximum 12.60398 15.16777 9.610488 2.804533 2.857128 3.394163 2.760138
Minimum 12.24544 14.26992 9.193269 2.436768 1.814335 2.151063 1.675125
Std. Dev. 0.112410 0.251035 0.105452 0.073011 0.327876 0.335259 0.285760
Skewness 0.543485 0.180514 0.707588 0.419093 0.054927 0.072478 0.013835
Kurtosis 2.017219 2.005226 3.109331 4.245912 1.687146 2.171669 2.236484
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
68
emissions? Energy Policy 113, 356–367. https://doi.org/10.1016/j.
enpol.2017.10.050.
Baltagi, B.H., Feng, Q., Kao, C., 2012. A Lagrange Multiplier test for cross-sectional
dependence in a fixed effects panel data model. J. Economet. 170 (1), 164–177.
https://doi.org/10.1016/j.jeconom.2012.04.004.
Barro, R.J., Lee, J.W., 2013. A new data set of educational attainment in the world,
1950–2010. J. Deve. Econo. 104, 184–198. https://doi.org/10.1016/j.
jdeveco.2012.10.001.
Bekun, F.V., Alola, A.A., Sarkodie, S.A., 2019. Toward a sustainable environment:
Nexus between CO
2
emissions, resource rent, renewable and non-renewable
energy in 16-EU countries. Sci. Total Environ. 657, 1023–1029. https://doi.org/
10.1016/j.scitotenv.2018.12.104.
Bekun, F.V., Gyamfi, B.A., Onifade, S.T., Agboola, M.O., 2021. Beyond the
environmental Kuznets Curve in E7 economies: accounting for the combined
impacts of institutional quality and renewables. J. Clean. Prod. 314,. https://doi.
org/10.1016/j.jclepro.2021.127924 127924.
Bekun, F.V., 2022. Mitigating emissions in India: accounting for the role of real
income, renewable energy consumption and investment in energy. 670216917.
https://doi.org/10.32479/ijeep.12652.
Belaïd, F., Zrelli, M.H., 2019. Renewable and non-renewable electricity
consumption, environmental degradation and economic development:
evidence from Mediterranean countries. Energy Policy 133,. https://doi.org/
10.1016/j.enpol.2019.110929 110929.
Birdsall, N., Pinckney, T., Sabot, R., 2001. Natural resources, human capital. Resource
abundance and economic development 57, 1 http://expository.org/wider.pdf.
Bointner, R., Pezzutto, S., Grilli, G., Sparber, W., 2016. Financing innovations for the
renewable energy transition in Europe. Energies 9 (12), 990. https://doi.org/
10.3390/en9120990.
Breitung, J., Das, S., 2005. Panel unit root tests under cross-sectional dependence.
Statistica Neerlandica 59 (4), 414–433. https://doi.org/10.1111/j.1467-
9574.2005.00299.x.
Breusch, T.S., Pagan, A.R., 1980. The Lagrange multiplier test and its applications to
model specification in econometrics. Rev. Econo. Stud. 47 (1), 239–253. https://
doi.org/10.2307/2297111.
Chandio, A.A., Sethi, N., Dash, D.P., Usman, M., 2022. Towards sustainable food
production: what role ICT and technological development can play for cereal
production in Asian–7 countries? Compu. Electron. Agri. 202,. https://doi.org/
10.1016/j.compag.2022.107368 107368.
Churchill, S.A., Inekwe, J., Smyth, R., Zhang, X., 2019. R&D intensity and carbon
emissions in the G7: 1870–2014. Energy Econ. 80, 30–37. https://doi.org/
10.1016/j.eneco.2018.12.020.
Cohen, D., Leker, L., 2014. Health and education: another look with the proper data.
CEPR Discuss. Pap. No. DP 9940, 1–25 https://ssrn.com/abstract=2444963.
Danish, Baloch, M.A., 2018. Dynamic linkages between road transport energy
consumption, economic growth, and environmental quality: evidence from
Pakistan. Environ. Sci. Pollut. Res. 25 (8), 7541–7552.
Ulucak, R., Khan, S.-D., 2020. Determinants of the ecological footprint: Role of
renewable energy, natural resources, and urbanization. Sustain. Cities Soc. 54,
101996.
De Bruyn, S.M., 1997. Explaining the environmental Kuznets curve: structural
change and international agreements in reducing sulphur emissions. Environ.
Dev. Econo., 485–503 https://wwwjstor.org/stable/44379190.
Demir, C., Cergibozan, R., Gök, A., 2019. Income inequality and CO2 emissions:
empirical evidence from Turkey. Energy Environ. 30, 444–461. https://doi.org/
10.1177/0958305X18793109.
Destek, M.A., Sinha, A., 2020. Renewable, non-renewable energy consumption,
economic growth, trade openness and ecological footprint: evidence from
organization for economic Cooperation and development countries. J. Clean.
Prod. 242,. https://doi.org/10.1016/j.jclepro.2019.118537 118537.
Driscoll, J.C., Kraay, A.C., 1998. Consistent covariance matrix estimation with
spatially dependent panel data. Rev. Econo. Statis. 80 (4), 549–560. https://doi.
org/10.1162/003465398557825.
Du, K., Li, P., Yan, Z., 2019. Do green technology innovations contribute to carbon
dioxide emission reduction? Empirical evidence from patent data. Tech. Forec.
Soci. Change 146, 297–303. https://doi.org/10.1016/j.techfore.2019.06.010.
Dumitrescu, E.I., Hurlin, C., 2012. Testing for Granger non-causality in
heterogeneous panels. Econ. Model. 29, 1450–1460. https://doi.org/10.1016/j.
econmod.2012.02.014.
Erdog
˘an, S., Çakar, N.D., Ulucak, R., Danish, Kassouri, Y., 2020. The role of natural
resources abundance and dependence in achieving environmental
sustainability: evidence from resource-based economies. Sus. Develop. 29 (1),
143–154.
Fakher, H.A., 2019. Investigating the determinant factors of environmental quality
(based on ecological carbon footprint index). Environ. Sci. Pollut. Res. 26 (10),
10276–10291. https://doi.org/10.1007/s11356-019-04452-3.
Fisher-Vanden, K., Wing, I.S., 2008. Accounting for quality: Issues with modeling the
impact of R&D on economic growth and carbon emissions in developing
economies. Energy Econ. 30 (6), 2771–2784. https://doi.org/10.1016/j.
eneco.2007.04.002.
Foxon, T., Pearson, P., 2008. Overcoming barriers to innovation and diffusion
of cleaner technologies: some features of a sustainable innovation policy
regime. J. Clean. Prod. 16 (1), S148–S161. https://doi.org/10.1016/j.
jclepro.2007.10.011.
Fuinhas, J.A., Marques, A.C., Koengkan, M., 2017. ‘‘Are renewable energy policies
setting carbon dioxide emissions?” the case of latin america countries. Environ.
Sci. Pollut. Res. 24 (17), 15044–15054 https://doi:10.1007/s11356-017-9109-z.
Ganda, F., 2019. The impact of innovation and technology investments on carbon
emissions in selected organization for economic Cooperation and development
countries. J. Clean. Prod. 217, 469–483. https://doi.org/10.1016/j.
jclepro.2019.01.235.
Graham, R.L., Turner, M.G., Dale, V.H., 1990. How increasing CO 2 and climate
change affect forests. BioScience 40 (8), 575–587. https://doi.org/10.2307/
1311298.
Gylfason, T., Zoega, G., 2006. Natural resources and economic growth: the role of
investment. World Econ. 29 (8), 1091–1115. https://doi.org/10.1111/j.1467-
97012006.00807.x.
Hassan, S.T., Xia, E., Khan, N.H., Shah, S.M.A., 2019. Economic growth, natural
resources, and ecological footprints: evidence from Pakistan. Environ. Sci.
Pollut. Res. 26 (3), 2929–2938. https://doi.org/10.1007/s11356-018-3803-3.
Herzer, D., Donaubauer, J., 2017. The long-run effect of foreign direct investment on
total factor productivity in developing countries: a panel cointegration analysis.
Empi. Econ. 54 (2), 309–342. https://doi.org/10.1007/s00181-016-1206-1.
Ibrahim, R.L., Ajide, K.B., Usman, M., Kousar, R., 2022. Heterogeneous effects of
renewable energy and structural change on environmental pollution in Africa:
do natural resources and environmental technologies reduce pressure on the
environment? Renew. Energy. 200, 244–256. https://doi.org/10.1016/j.
renene.2022.09.134.
IRENA (International Renewable Energy Agency), 2016. Renewable Energy Market
Analysis: Latin America, 1–160. ISBN 978-92-95111-49-3.
Jahanger, A., Usman, M., Murshed, M., Mahmood, H., Balsalobre-Lorente, D., 2022.
The linkages between natural resources, human capital, globalization, economic
growth, financial development, and ecological footprint: the moderating role of
technological innovations. Resour. Policy 76,. https://doi.org/10.1016/j.
resourpol.2022.102569 102569.
Khan, I., Hou, F., Le, H.P., 2021. The impact of natural resources, energy
consumption, and population growth on environmental quality: Fresh
evidence from the United States of America. Sci. Total Environ. 754,. https://
doi.org/10.1016/j.scitotenv.2020.142222 142222.
Khan, I., Zakari, A., Ahmad, M., Irfan, M., Hou, F., 2022. Linking energy transitions,
energy consumption, and environmental sustainability in OECD countries.
Gondwana Res. 103, 445–457. https://doi.org/10.1016/j.gr.2021.10.026.
Khattak, S.I., Ahmad, M., Khan, Z.U., Khan, A., 2020. Exploring the impact of
innovation, renewable energy consumption, and income on CO2 emissions:
new evidence from the BRICS economies. Environ. Sci. Pollut. Res. 27 (12),
13866–13881. https://doi.org/10.1007/s11356-020-07876-4.
Koengkan, M., 2018. The decline of environmental degradation by renewable
energy consumption in the MERCOSUR countries: an approach with ARDL
modeling. Environ. Sys. Decis. 38 (3), 415–425. https://doi.org/10.1007/s10669-
018-9671-z.
Lewis, N.S., 2016. Aspects of science and technology in support of legal and policy
frameworks associated with a global carbon emissions-control regime. Energy
Environ. Sci. 9 (7), 2172–2176. https://doi.org/10.1039/C6EE00272B.
Li, K., Fang, L., He, L., 2019. How population and energy price affect China’s
environmental pollution? Energy Policy 129, 386–396. https://doi.org/10.1016/
j.enpol.2019.02.020.
Meo, M.S., Chowdhury, M.A.F., Shaikh, G.M., Ali, M., Masood Sheikh, S., 2018.
Asymmetric impact of oil prices, exchange rate, and inflation on tourism
demand in Pakistan: new evidence from nonlinear ARDL. Asia Pac. J. Tour. Res.
23 (4), 408–422. https://doi.org/10.1080/10-941665.2018.1445652.
Mongo, M., Belaïd, F., Ramdani, B., 2021. The effects of environmental innovations
on CO
2
emissions: empirical evidence from Europe. Environ. Sci. Policy 118, 1–
9. https://doi.org/10.1016/j.envsci.2020.12.004.
Nathaniel, S., Khan, S.A.R., 2020. The nexus between urbanization, renewable
energy, trade, and ecological footprint in ASEAN countries. J. Clean. Prod. 272,.
https://doi.org/10.1016/j.jclepro.2020.122709 122709.
Nathaniel, S.P., Nwulu, N., Bekun, F., 2021a. Natural resource, globalization,
urbanization, human capital, and environmental degradation in Latin
American and Caribbean countries. Environ. Sci. Pollut. Res. 28 (5), 6207–
6221. https://doi.org/10.1007/s11356-020-10850-9.
Nathaniel, S.P., Yalçiner, K., Bekun, F.V., 2021b. Assessing the environmental
sustainability corridor: linking natural resources, renewable energy, human
capital, and ecological footprint in BRICS. Resour. Policy 70, 101924.
Ng, C.F., Choong, C.K., Lau, L.S., 2020. Environmental Kuznets curve hypothesis:
asymmetry analysis and robust estimation under cross-section dependence.
Environ. Sci. Pollut. Res. 27 (15), 18685–18698. https://doi.org/10.1007/
s11356-020-08351-w.
Nikzad, R., Sedigh, G., 2017. Greenhouse gas emissions and green technologies in
Canada. Environ. Dev. 24, 99–108. https://doi.org/10.1016/j.
envdev.2017.01.001.
Omri, A., 2018. Entrepreneurship, sectoral outputs and environmental
improvement: international evidence. Tec. Forec. Social Change 128, 46–55.
https://doi.org/10.1016/j.techfore.2017.10.016.
Ozturk, I., Aslan, A., Altinoz, B., 2022a. Investigating the nexus between CO
2
emissions, economic growth, energy consumption and pilgrimage tourism in
Saudi Arabia. Econ. Res. Ekon. Istraz
ˇ. 35 (1), 3083–3098. https://doi.org/
10.1080/1331677X.2021.1985577.
Ozturk, I., Aslan, A., Ozsolak, B., Atay Polat, M., Turan, Z., 2022b. Impact of fossil fuels
and renewable energy consumption on economic growth in Paris Club
Countries. J. Renew. Sus. Energy 14, (4). https://doi.org/10.1063/5.0086752
045901.
Pachauri, R.K., Allen, M.R., Barros, V.R., Broome, J., Cramer, W., Christ, R., van Ypserle,
J.P., 2014. Climate change 2014: synthesis report. Contribution of Working
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
69
Groups I, II and III to the fifth assessment report of the Intergovernmental Panel
on Climate Change (p. 151). IPCC. https://doi.org/10013/epic.45156.d001.
Pedroni, P., 2001. Purchasing power parity tests in cointegrated panels. Rev. Econ.
Stat. 83 (4), 727–731. https://doi.org/10.1162/003465301753237803.
Pesaran, M.H., 2004. General diagnostic tests for cross-sectional dependence in
panels. Empir Econ. 60 (1), 13–50.
Pesaran, M.H., 2007. A simple panel unit root test in the presence of cross-section
dependence. J. Appl. Econo. 22 (2), 265–312. https://doi.org/10.1002/jae.951.
Pesaran, M.H., Ullah, A., Yamagata, T., 2008. A bias-adjusted LM test of error cross-
section independence. Econometrics J. 11 (1), 105–127. https://doi.org/10.1111/
j.1368-423X.2007.00227.x.
Sadiq, M., Shinwari, R., Usman, M., Ozturk, I., Maghyereh, A.I., 2022. Linking nuclear
energy, human development and carbon emission in BRICS region: do external
debt and financial globalization protect the environment? Nucl. Engin. Tec. 54
(9), 3299–3309. https://doi.org/10.1016/j.net.2022.03.024.
Salahuddin, M., Habib, M.A., Al-Mulali, U., Ozturk, I., Marshall, M., Ali, M.I., 2020.
Renewable energy and environmental quality: A second-generation panel
evidence from the Sub Saharan Africa (SSA) countries. Environ. Res. 191,.
https://doi.org/10.1016/j.envres.2020.110094 110094.
Sinha, A., Sengupta, T., Alvarado, R., 2020. Interplay between technological
innovation and environmental quality: formulating the SDG policies for next
11 economies. J. Clean. Prod. 242,. https://doi.org/10.1016/j.
jclepro.2019.118549 118549.
Sohag, K., Al Mamun, M., Uddin, G.S., Ahmed, A.M., 2017. Sectoral output, energy
use, and CO
2
emission in middle-income countries. Environ. Sci. Pollut. Res. 24
(10), 9754–9764. https://doi.org/10.1007/s11356-017-8599-z.
Solarin, S.A., Al-Mulali, U., 2018. Influence of foreign direct investment on indicators
of environmental degradation. Environ. Sci. Pollut. Res. 25 (25), 24845–24859.
https://doi.org/10.1007/s11356-018-2562-5.
Tiba, S., Belaid, F., 2020. The pollution concern in the era of globalization: do the
contribution of foreign direct investment and trade openness matter? Energy
Econo. 92,. https://doi.org/10.1016/j.eneco.2020.104966 104966.
Ulucak, R., Bilgili, F., 2018. A reinvestigation of EKC model by ecological footprint
measurement for high, middle and low income countries. J. Clean. Prod. 188,
144–157. https://doi.org/10.1016/j.jclepro.2018.03.191.
United Nations, 2018. The Sustainable Development Goals Report 2018. Available
at. https://unstats.un.org/sdgs/report/2018.
Usman, M., Hammar, N., 2021. Dynamic relationship between technological
innovations, financial development, renewable energy, and ecological
footprint: fresh insights based on the STIRPAT model for Asia Pacific
Economic Cooperation countries. Environ. Sci. Pollut. Res. 28 (12), 15519–
15536. https://doi.org/10.1007/s11356-020-11640-z.
Usman, M., Makhdum, M.S.A., 2021. What abates ecological footprint in BRICS-T
region? Exploring the influence of renewable energy, non-renewable energy,
agriculture, forest area and financial development. Renew. Energy 179, 12–28.
https://doi.org/10.1016/j.renene.2021.07.014.
Usman, M., Radulescu, M., 2022. Examining the role of nuclear and renewable
energy in reducing carbon footprint: does the role of technological innovation
really create some difference? Sci. Total Environ. 841,. https://doi.org/10.1016/j.
scitotenv.2022.156662 156662.
Usman, M., Makhdum, M.S.A., Kousar, R., 2020. Does financial inclusion, renewable
and non-renewable energy utilization accelerate ecological footprints and
economic growth? Fresh evidence from 15 highest emitting countries. Sustain.
Cities Soc. 65, 102590.
Wada, I., Faizulayev, A., Bekun, F.V., 2021. Exploring the role of conventional energy
consumption on environmental quality in Brazil: Evidence from cointegration
and conditional causality. Gondwana Res. 98, 244–256. https://doi.org/10.1016/
j.gr.2021.06.009.
Westerlund, J., 2007. Testing for error correction in panel data. Oxford Bulletin of
Economics and statistics 69 (6), 709–748. https://doi.org/10.1111/j.1468-
0084.2007.00477.x.
World Bank, 2020. Patent Applications. https://data.worldbank.org/indicator/IP.
PAT.RESD. (Accessed 13 November 2020).
Yi, W., 2012. Empirical Study on Environmental Regulation, Technology Innovation
and Carbon Emissions in China——Based on Cointegration and Granger
Causality Analysis. J. Indus. Tec. Econ. 6 http://en.cnki.com.cn/Article_en/
CJFDTotal-GHZJ201206020.htm.
Zafar, M.W., Zaidi, S.A.H., Khan, N.R., Mirza, F.M., Hou, F., Kirmani, S.A.A., 2019. The
impact of natural resources, human capital, and foreign direct investment on
the ecological footprint: the case of the United States. Resour. Policy 63,. https://
doi.org/10.1016/j.resourpol.2019.101428 101428.
Zahoor, Z., Latif, M.I., Khan, I., Hou, F., 2022. Abundance of natural resources and
environmental sustainability: the roles of manufacturing value-added,
urbanization, and permanent cropland. Environ. Sci. Pollut. Res. 29 (54),
82365–82378.
Zallé, O., 2019. Natural resources and economic growth in Africa: the role of
institutional quality and human capital. Resour. Policy 62, 616–624. https://doi.
org/10.1016/j.resourpol.2018.11.009.
M. Usman, D. Balsalobre-Lorente, A. Jahanger et al. Gondwana Research 113 (2023) 53–70
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