How does economic complexity affect ecological footprint in G-7 economies: the role of renewable and non-renewable energy consumptions and testing EKC hypothesis

Abstract

The discussion concerning whether and how economic complexity (ECI) affects ecological footprint (EFP) has gained researchers’ consideration, while there are slight empirical evidence to support the subject matter. In the support of theoretical argument, this study provides empirical evidence by investigating the impact of ECI on EFP along with the role of disaggregated energy consumptions by using a panel dataset of G-7 economies between 1996 and 2019. To this end, we applied panel techniques of Fully-Modify OLS and Dynamic-OLS models for cointegration analysis. The results obtained from Fully-Modify OLS and Dynamic-OLS models reveal that ECI deteriorates environmental quality by increasing EFP, while renewable energy reduces ecological pollution by decreasing EFP. In addition, the increasing demand for non-renewable energy and economic growth both degrades environmental quality in G-7 countries. More interestingly, the non-linear (ECI²) relationship between ECI and EFP confirms a U-shaped association (EKC hypothesis), which suggests that after achieving a certain threshold level, economic complexity mitigates environmental degradation in G-7 economies. The empirical results also suggest that other control variables such as population growth, inflation rate, foreign direct investment, and total trade intensity lead to environmental degradation by increasing ecological footprint. Based on empirical results, the following important policy implications are drawn; first, G-7 economies should speed up the level of economic complexity along with renewable energy consumption to protect environmental quality and maintain sustainable growth and development. Secondly, the governments of G-7 countries should introduce greener technologies and promote production that are environmental friendly for drastic reduction in environmental unsustainability.

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Environmental Science and Pollution Research
https://doi.org/10.1007/s11356-022-19094-1
RESEARCH ARTICLE
How does economic complexity affect ecological footprint inG‑7
economies: therole ofrenewable andnon‑renewable energy
consumptions andtesting EKC hypothesis
SalimKhan1,2· WangYahong2 · AbbasAliChandio3
Received: 22 November 2021 / Accepted: 3 February 2022
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
Abstract
The discussion concerning whether and how economic complexity (ECI) affects ecological footprint (EFP) has gained
researchers’ consideration, while there are slight empirical evidence to support the subject matter. In the support of theoretical
argument, this study provides empirical evidence by investigating the impact of ECI on EFP along with the role of disag-
gregated energy consumptions by using a panel dataset of G-7 economies between 1996 and 2019. To this end, we applied
panel techniques of Fully-Modify OLS and Dynamic-OLS models for cointegration analysis. The results obtained from
Fully-Modify OLS and Dynamic-OLS models reveal that ECI deteriorates environmental quality by increasing EFP, while
renewable energy reduces ecological pollution by decreasing EFP. In addition, the increasing demand for non-renewable
energy and economic growth both degrades environmental quality in G-7 countries. More interestingly, the non-linear (ECI2)
relationship between ECI and EFP confirms a U-shaped association (EKC hypothesis), which suggests that after achieving a
certain threshold level, economic complexity mitigates environmental degradation in G-7 economies. The empirical results
also suggest that other control variables such as population growth, inflation rate, foreign direct investment, and total trade
intensity lead to environmental degradation by increasing ecological footprint. Based on empirical results, the following
important policy implications are drawn; first, G-7 economies should speed up the level of economic complexity along with
renewable energy consumption to protect environmental quality and maintain sustainable growth and development. Secondly,
the governments of G-7 countries should introduce greener technologies and promote production that are environmental
friendly for drastic reduction in environmental unsustainability.
Keywords Economic complexity· Renewable energy· Non-renewable energy· Ecological footprint· EKC· FMOLS
Introduction
Since the past few decades, environmental degradation and
climate change have become the topmost challenging and
controversial issues around the globe, while there are grow-
ing international agreements that these severe issues need to
be resolved immediately (Boutabba 2014; Ma etal. 2019).
The last few years are witnessed fast-growing carbon emis-
sions (CO2E) in the world with a very high increase from 22
thousand million tons in 1990 to 37 thousand million tons
in 2018–2019, as reported by World Bank (2020). To this
end, many developed and developing countries have started
to pay serious attention to environmental protection meas-
ures due to increasing ecological pollution, while the global
community has taken serious steps and employed some
important environmental protection measures to mitigate
environmental pollution (Doğan etal. 2020). A significant
Responsible Editor: Roula Inglesi-Lotz
* Wang Yahong
wyahong2009@zzu.edu.cn
Salim Khan
salimkhan18@gs.zzu.edu.cn
Abbas Ali Chandio
alichandio@sicau.edu.cn
1 Business School, Zhengzhou University, Zhengzhou450001,
Henan, China
2 School ofTourism andManagement, Zhengzhou University,
Zhengzhou450001, Henan, China
3 College ofEconomics, Sichuan Agricultural University,
Chengdu611130, China

Environmental Science and Pollution Research
1 3
increase in per capita income (economic growth), urbaniza-
tion, population growth, and population growth have cased
to augmented demand for energy consumption which has
posed pressures on environmental sustainability. In the years
2019–2020, the demand for primary energy consumption
increased globally by 1.29%, while it is projected to reach
3.3% annually by 2025–2026 in many developed economies
(Saidi and Omri 2020). Seeing that, the consumption of pri-
mary energy is essential to achieve higher economic growth
and the main cause of environmental pollution. However, the
relationship between economic growth, energy consumption,
and environmental quality is the most debatable and among
researchers in the current century.
Several researchers analyzed environmental degradation
by incorporating different measures of ecological pollution
over time. Among these measures, greenhouse emissions
(GEs) is of central importance, due to the easy availability
of data and the holding largest shares in GEs. However, a
vast research literature on environmental economics used
ecological footprint (EFP) and CO2E as measures of envi-
ronmental quality. Many researchers (Ahmad etal. 2021a, d;
Baloch etal. 2020a; Danish 2020; Malik etal. 2020; Khan
etal. 2021; Khan and Yahong 2021a) used CO2E emissions
as a proxy for environmental degradation. Similarly, from
a sustainable development perspective, many researchers
(Ahmad and Wu 2022; Baloch etal. 2020b; Bilgili etal.
2020; Chu 2021; Khan and Yahong 2021b; Khan etal. 2022;
Qayyum etal. 2021; Ulucak and Bilgili 2018; Yilanci and
Pata 2020 2020) have been used the EFP to measures envi-
ronmental sustainability and hazards. The EFP is an impor-
tant metric that produces the greenhouse effect and contrib-
utes to climate change and global warming. Over the last few
decades, the average world temperature has raised between
0.4 and 0.8 centigrade, while it is projected that it could be
increased between 1.4 and 5.8 centigrade till 2100 (Danish
etal. 2020). Similarly, global warming and climate change
has created some environmental issues such as the loss of
biodiversity, increase unavailability of fresh drinking water,
melting of polar ice caps, heavy conditions of weather, rising
sea levels, and rapid spread of diseases (Danish etal. 2017).
As a responsible factor for global warming, environmental
degradation, and climate change increased, the levels of GEs
emissions are taken into consideration globally. In particular,
among GEs emissions, CO2E and EFP produced by fossil
fuel burning are considered a key and common factor behind
global warming (Baek 2016).
The current study tried to empirically investigate the
linkages between economic complexity (ECI), renewable
and non-renewable energy consumption, economic growth
(GDP growth), population growth, and EFP in economi-
cally complex G-7 economies. Among them, the ECI and
energies consumptions (renewable and non-renewable ener-
gies) are the main explanatory variables for the current study
because ECI incorporates all aspects of advanced products
such as competency, advanced knowledge, and skill in the
production process (Hausmann etal. 2014). In the recent
past, the economically complex countries such as G-7 econ-
omies remarkably grown along with high social change and
automation. Because of this transformation and industrial
revaluation, the demand for energy consumption has been
increased. To this end, these countries are considered to
have a leading contribution to the increase in per-capita EFP,
while this would determine the main cause of environmental
degradation in the future. Therefore, the current study would
fill the existing gap and would be a crucial addition to the
environmental economics literature.
The main variable in the current study is economic com-
plexity index (ECI) representing well-skilled and knowl-
edge-based production capacity as stated by Can and Gozgor
(2017). The index was first developed by Hidalgo and Haus-
mann (2009). The ECI is simply representing a country’s
total output and measure the production ability of a country
and its productivity (Hausmann etal. 2014). ECI describes
a country’s knowledge and skilled-based effective capac-
ity of production (Can and Gozgor 2017), a country with a
high value of ECI indicating its efficient production capacity.
Meanwhile, the ECI is calculated only with United Nations
(UN) trade data, because the production capacity is based on
exports. It represents the total amount of knowledge that is
incorporated by a country to establish a well-structured and
productive economy (Hailu and Kipgen 2017). According to
Hausmann etal. (2014), a well-structured and more complex
economy provides knowledge and skilled-based production
forum that reduce environmental degradation through the
adaptation of environmental friendly technology.
Through diversification in production and industriali-
zation, complex economies shift to knowledge and skilled
intensive technology, for example, renewable energy gen-
eration, environmentally friendly production, and energy-
efficient product, to maintain a green economy (Ahmad
etal. 2021c; Swart and Brinkmann 2020). The consump-
tion of renewable energy and its generation is the best and
fine solution to reduce environmental pollution along with
sustainable economic growth and development. According
to the International Energy Agency (IEA), the consumption
and generation of renewable energy are rising worldwide
(Energy 2019; Jabeen etal. 2021). Similarly, Raza etal.
(2021) figure out that investment on environmental friendly
technology (clean energy) can decrease environmental pol-
lution and therefore decrease environmental degradation.
According to Neagu and Teodoru (2019), ECI has a close
relationship with human welfare and per capita income
across countries. In the initial phase of economic develop-
ment, the process of production in simple labor-intensive
and agricultural economies creates less environmental deg-
radation (Danish etal. 2020). But, in the second phase of

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economic development (when a country becomes capital-
intensive), economies become more complex with product
diversity and industrialization and higher ECI harming envi-
ronmental quality (Shahzad etal. 2021). At the final phase,
after a certain threshold has been passed, higher ECI can
protect the environment through investing in the mitigation
process, human capital, knowledge, and the development
of technology (Ahmad etal. 2021a, b, c, d, e, f). Structural
changes with the emerging of environmental friendly tech-
nology substitute the old technology that harms environ-
mental quality. Therefore, higher ECI indicates knowledge
and cleaner technology that is necessary to mitigate envi-
ronmental degradation.
Non-renewable energy consumption is also an important
determinant of economic growth and environmental degra-
dation. The higher demand for fossil fuel energy consump-
tion leads to higher environmental degradation because the
excessive use of fossil fuels (oils, gases, and energies) con-
tributes to climate change and ecological imbalance (Ahmad
etal. 2021a; Pata 2018; Saud etal. 2020). Due to the increas-
ing world’s population, the usage of fossil fuels caused geo-
political and military conflict, the volatility of oil prices,
and therefore an increase in ecological problems (Malik
etal. 2020). Contrasting, renewable energy is environmen-
tal friendly and inexhaustible because of the massive usage
of fossil fuel causing destruction and disasters in nature.
Therefore, renewable energy should be replaced with fossil
fuels to provide energy security and diversity and protect the
natural environment (Jabeen etal. 2020; Jebli etal. 2016).
Why G‑7 economies?
The current research only considers G-7 countries (Canada,
Germany, France, Italy, Japan, the UK, and the USA) due to
many reasons. Firstly,in 2019, it is stated that G-7 countries
held 30.7% of the global GDP and consume a large por-
tion of the world’s energy (Khan etal. 2021); therefore, G-7
economies are highly responsible for environmental degra-
dation. Secondly, G-7 countries are economically developed,
and they have passed the transition phase of knowledge and
sophisticated production, while their ECI index is higher
than other developing or emerging economies. Thirdly, in
the current literature, there is a lack of empirical evidence
for G-7 countries, and exclusion of important factors needs
to reinvestigate the effect of ECI on EFP along with energy
consumptions in the context of G-7 economies. Fourth, these
economies are more capable to produce sophisticated and
advanced products, while the production of advance and
sophisticated products and its export needs to have high
energy-intensive production units which may badly affect
environmental quality. Fifthly, to the best of our knowledge,
the effects of ECI and energy consumption on environmental
degradation have been ignored by the existing literature and
have not been studied the determinate role of these vari-
ables in targeted countries. Therefore, by considering above
mentioned reasons, this study will have important policy
implications for policymakers in the light of sustainable
development goals (SDGs).
The motivation for current research would be a huge con-
tribution to the existing literature in many ways. First, the
current study does not only investigate the linear impact of
ECI on environmental pollution but also determines the non-
linear relationship of economic complexity with ecological
pollution. Second, this study also determines the contributing
role of disaggregated energy consumption and explores the
actual effect of renewable and non-renewable energy on envi-
ronmental degradation in terms of EFP. Additionally, the cur-
rent study hashuge contribution regarding methodologies by
incorporation Fully-Modify OLS and Dynamic-OLS models
to analyze the relationship between ECI, REC, NREC, GDP,
POP, and EFP. These applied methodologies are reliable and
robust and enable us to incorporate more instruments, which
would increase the efficiency of the study.
Moreover, the main aim of the current study is to inves-
tigate the relationship between ECI, REC, NREC, and envi-
ronmental degradation in terms of EFP; therefore, it would
help to formulate the sustainable development through a
policy framework and allied economic development policies.
As the G-7 economies are the leading countries in terms
of more complex economic structure, energy utilization,
and environment related issues, therefore, formulating the
policies for complex economies like G-7 countries might
help to address targets of SDGs. Particularly, in designing
the framework of policies, a phase-wise approach should be
incorporated. This will help the whole spectrum of sustain-
able development goals and climate of advance economies
like G-7. To the best of our knowledge, such phase-wise
methodology to report cleaner energy and sustainability has
not been adopted in the previous studies; hence, the policy
level contribution lies in this study.
The remainder of the paper is organized as follows. Sec-
tion II comprises a literature review with a summary table,
Section III provides materials and methods, section IV
provides empirical results and discussion, and section IV
covers the conclusion with the recommendation of policy
implications.
Literature review
Global warming and climate change, as well as increasing
knowledge about these issues, have developed an under-
standing of environmental pollution and its important
elements. In recent times, investigating the relationship
between economic complexity and environmental pollution
has gained considerable interest. Regarding this issue, some

Environmental Science and Pollution Research
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important environmental-related research studies are sum-
marized in Table1.
Econometric strategy
For the analysis of current study, we have collected the
maximum available data of 1996–2019 for G-7 countries.
The objective of this research is to analyze the dynamic and
long-run causal association between economic complex-
ity, renewable energy consumption, non-renewable energy
consumption, economic growth, rural population growth,
and ecological footprint in G-7 countries. In order to ver-
ify the impact of economic complexity, different types of
energy consumptions, and economic growth on ecological
pollution, we have also analyzed several control variables
so that we do not omit any environment influencing vari-
ables. Besides this, all variables including in the models
are transformed into a log form to ensure homogeneity as
shown in Table1. Furthermore, we estimate the following
econometric models:
where lnEFPit represents the logarithmic form of per capita
consumption of ecological footprint, lnECIit is economic
complexity index, lnRECit denotes renewable energy con-
sumption, lnNRECit is non-renewable energy consumption,
lnGDPit is GDP per capita (proxy for economic growth),
lnPOPit is population growth, lnINFit represents inflation
rate, lnINQit is Gini index (proxy for income inequality),
lnTRDit is trade (exports and imports), and ECI2 represents
the non-linear effect ECI. Besides, each i and t represents
countries and years, respectively, while residual (error terms)
in Eqs.1 to 6 is εit. The list of variables used in regression
models along with their definitions, measurement methods,
source, and expected sign are demonstrated in Table2.
(1)
lnEFPit =β
0+β
1lnECIit +β
2lnRECit +β
3lnNRECit +β
4lnGDPit +β
5lnPOPit +ε
it
(2)
lnEFPit =β
0+β
1lnECIit +β
2lnRECit +β
3lnNRECit +β
4lnGDPit +β
5lnPOPit +β
6lnINF +ε
it
(3)
lnEFPit
=𝛽
0
+𝛽
1lnECIit
+𝛽
2lnRECit
+𝛽
3lnNRECit
+𝛽
4lnGDPit
+𝛽
5lnPOPit
+𝛽
6lnINF
+𝛽
7lnFDI
+𝜀
it
(4)
lnEFPit
=
𝛽0
+
𝛽1lnECIit
+
𝛽2lnRECit
+
𝛽3lnNRECit
+
𝛽4lnGDPit
+
𝛽5lnPOPit
+
𝛽6lnINF
+
𝛽7lnFDI
+
𝛽8lnINQ
+
𝜀it
(5)
lnEFPit
=
𝛽0
+
𝛽1lnECIit
+
𝛽2lnRECit
+
𝛽3lnNRECit
+
𝛽4lnGDPit
+
𝛽5lnPOPit
+
𝛽6lnINFit
+
𝛽7lnFDIit
+
𝛽8lnINQit
+
𝛽9lnTRDit
+
𝜀it
(6)
lnEFPit
=
𝛽0
+
𝛽1lnECIit
+
𝛽2lnRECit
+
𝛽3lnNRECit
+
𝛽4lnGDPit
+
𝛽5lnPOPit
+
𝛽6lnINFit
+
𝛽7lnFDIit
+
𝛽8lnINQit
+
𝛽9lnTRDit
+
𝛽10ECI2
+
𝜀it
The analysis of this study is based on panel data from
G-7 countries for 1996 to 2019, where the data have been
collected from international recognized databases such as
World Development Indicators (WDI), Global Footprint
Network (GFN), and ATLAS of Economic Complexity
by the Growth Lacb at Harvard University. In addition, for
the missing data, we used the techniques of linear imputa-
tions. The lists of all variables used in the models with their
descriptive statistics are illustrated in Table3. In this study,
we also stipulate the summary statistics of all variables used
in the model through box plots (see Fig.1).
However, the empirical analysis performed in few steps.
In the very first step, the unit root tests are applied to check
whether the panel series (variables) are stationary or not at
level I(0). This step would be applied after the descriptive
analysis of selected variables. If the empirical results of unit
root tests suggest that all selected variables are stationary at
first difference I(I), the second stage would be applied for
panel cointegration tests to check the long-run relationships.
Panel unit root
The current study is based on panel data, so multiple unit
root tests such as Fisher PP, Breitung, and Fisher-ADF
tests could be applied to ascertain the stationarity level of
the series used in the model. The structure of all the unit
root tests and their procedure can be found in the work
of Im etal. (2003); Levin etal. (2002); and Breitung and
Meyer (1994). For convenience, to avoid overlapping, we
ignore writing the same equations. Nevertheless, regard-
ing the null hypothesis (H0), it hypothesized the existence
of unit root in a series such as in all cases, H0: the vari-
able is non-stationary at the 0 (I)/level. In all cases, the
probability (P value) is used to accept or reject the H0. If
the numerical P value is greater than 5% (in some cases

Environmental Science and Pollution Research
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Table 1 Summary of related studies
Authors Country/region Time Methodology Empirical findings
Related studies on economic complexity and environmental quality
Boleti etal. (2021) 88 developing and developed economies 2002–2012 FE2SLS/IV model The findings confirmed that moving towards
higher economic complexity leads to
improve overall environmental quality. This
inferred that structure transformation does
not encourage ecological pollution
Can and Gozgor (2017) France 1964–2014 DOLS model The findings suggest that higher economic
complexity controls environmental degrada-
tion by suppresses CO2E in the long run
Chu (2021) 118 developed and developing countries 2002–2014 GMM model The empirical findings show the existence
of EKC relationship between CO2E and
economic complexity
Doğan etal. (2019) 55 countries fall into high income, higher
middle income, and lower-income groups
1971–2014 Panel quantile regression model The findings of the study suggest that eco-
nomic complexity has upsurge ecological
pollution in the higher middle and lower-
income countries, while it has controlled in
high-income countries
Doğan etal. (2021) OECD economies 1990–2014 AMG, FMOLS, DOLS, panel-ARDL, and
fixed effect models
The empirical findings of the study suggest
that economic complexity mitigate environ-
mental degradation
Neagu and Teodoru (2019) European Union countries FMOLS and DOLS models The empirical findings of the study found that
non-renewable energy consumption along
with economic complexity has a positive
impact on environmental degradation
Pata (2020)USA 1980–2016 VECM model The findings confirm that the environmental
Kuznets curve (EKC) hypothesis exists
between economic complexity and eco-
logical pollution. Also, renewable energy
consumptions and globalization contribute
to decreases environmental degradation
Romero and Gramkow
(2021)
67 countries from the developed and devel-
oping world
1976–2012 Fixed effect regression and S-GMM models The findings revealed that there is a negative
relationship between economic complexity
and CO2E in the investigated region
Shahzad etal. (2021) The USA 1965Q1–2017Q4 Quantile ARDL methodology The analysis confirmed that economic com-
plexity and non-renewable energy consump-
tion increases environmental degradation by
enhancing EFP in the USA

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Table 1 (continued)
Authors Country/region Time Methodology Empirical findings
Other related studies
Ahmad etal. (2021b) 28 Chinese provinces 2004–2017 CCEMG The empirical findings of the study revealed
that the inflow of FDI and level of income
suppresses environmental degradation. Fur-
thermore, the study confirms the presence
of pollution haven and EKC hypothesis as
whole
Ahmad etal. (2021e) 11 developing economies 1992–2014 FMOLS and Pooled MG methods The empirical findings suggests the exist-
ence of EKC hypothesis, indicating that an
increase in economic growth and renew-
able energy consumption leads to promote
environmental sustainability in developing
economies
Dagar etal. (2021) 38 OECD countries 1995–2019 S-GMM and D-GMM models The findings of the study confirm the indus-
trial production and financial development
degrading environmental quality, while REC
and natural resources promote environmental
sustainability
Khan etal. (2020b) G-7 countries 1995–2017 AMG The empirical findings of the study suggests
the inverse relationship of energy price with
total energy consumption and NREC in G-7
countries
Khan etal. (2020a) 36 belt and road initiative (BRI) countries 1995–2016 Panel vector
autoregressive (PVAR) based on GMM
model
The results showed that economic growth
and social well-being lead to environmental
pollution, while in the long-run, economic
growth enhance social well-being
Usman etal. (2020) 33 UMICs 1994–2017 AMG, FGLS, FMOLS, and DOLS The results of the study shows negative rela-
tionship of REC on EFP in Asian, American,
and European countries, while economic
growth in African and European countries
FE2SLS/IV fixed effect two-stages least square/instrumental variable model, DOLS dynamic ordinary least square model GMM generalized method of moments, AMG augmented mean group,
FMOLS Fully-Modify ordinary least square, ARDL autoregressive distributive lag, VECM vector error correction model, S-GMM system GMM, D-GMM dynamic GMM, CCEMG common
correlated effects mean group, pooled MG pooled mean group PVAR panel vector autoregressive model, FGLS feasible-generalized least squares model.

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10%), then the H0 could be accepted and vice versa. If the
null hypothesis is rejected, the series would not exhibit
the problem of unit root at I (I) (see the work of Abbasi
etal. 2020).
Cointegration tests
For the panel data analysis, panel cointegration tests are the
common practice when the series are non-stationary at I(I).
In past research studies on panel data, there are numerous
unit root test techniques for the analysis of panel cointegra-
tion. In this study, we have incorporated three panel coin-
tegration tests, namely, Kao, Pedroni test, and Fisher coin-
tegration tests. Among all cointegration tests, the Pedroni
cointegration technique describes total of seven statistics
in two dimensions. The very first group describes four test
statistics based on within dimension. Similarly, the second
group of test statistics provides three statistics between
dimensions. In all seven statistics along with two dimen-
sions, the H0 of no cointegration is tested. For example, for
all the test statistics of cointegration, the H0 is: H0: λ = 1 for
all i (no cointegration). However, the alternative hypoth-
esis (H1) is different for both within and between dimen-
sions. The cointegrations for both groups are as follows:
H1: λi = λ < 1 for all i (for within dimensions group) and H1:
λ < 1 for all i (for between dimensions group).
Table 2 Definitions, measures, sources and expected signs of variables
Variables Definition Measure Source Main/control
and expected
sign
Dependent variable
EFP Ecological footprint: per capita
consumptions
Global hectares Global Footprint Network (GFN)
(https:// www. footp rintn etwork. org/
our- work/ ecolo gical- footp rint/)
Main
Independent variables
ECI Economic-complexity index It measures productive capabili-
ties based on advanced skills and
knowledge
ATLAS of Economic Complexity
by the Growth Lacb at Harvard
University
(https:// atlas. cid. harva rd. edu/)
Main
+
REC Renewable energy consumption It measures in total final consumption
of energy
World Development Indicators
(WDI)
(https:// www. world bank. org/ en/
home)
Main
-
NREC Non-renewable energy consumption It measures in total final consumption
of energy
WDI Main
+
GDP GDP per-capita (constant 2010 US$) Measure of economic growth WDI Main
+
POP Rural population growth (annual %) Calculated as the difference between
urban population and total popula-
tion
WDI Control
±
lnINF Inflation (annual %) Consumer price index (CPI) WDI Control
±
FDI Foreign direct investment The inflow of foreign direct invest-
ment per capita
WDI Control
±
INQ Income inequality Gini coefficient/index estimate by
World Bank (WB)
WDI Control
±
TRA Trade Trade is the total imports and exports
measured as a share of GDP
WDI Control
±
Table 3 Descriptive statistics
Variables Mean Standard
Deviation
Minimum Maximum
lnEFP 1.320 0.273 0.934 1.776
lnECI 0.393 0.389 0.887 -0.616
lnREC 2.019 0.896 3.122 -0.159
lnFFEC 4.415 0.063 4.292 4.528
lnGDP 10.630 0.136 10.35 10.912
lnPOP -3.109 4.246 20.472 2.388
lnINF 0.498 0.674 -3.249 1.388
lnFDI 0.857 0.921 -3.864 2.868
lnINQ 3.540 0.104 3.325 3.725
lnTRA 3.939 0.383 3.098 4.484

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To test the H0 and H1, first, we estimate the residual
from the specified regression model of panel cointegra-
tion. The regression residuals in the current situation
may be computed from the above-mentioned (Eqs.1 to 6)
regression models. However, for the hypothesis testing, the
regression residuals in the above-estimated model would
be as follows:
Under this hypothesis testing, if the critical value exceeds
the test statistic value, the H1 will be accepted against H0,
which suggests that there is a cointegration (or long run)
association among the selected variables.
Additionally, we also employ the Kao test of cointe-
gration, in which we replicate the same techniques of the
Pedroni panel cointegration approach. However, in the
first stage, the Kao test of cointegration approach assumes
that the slope coefficient to be homogeneous in all cross-
sectional series, heterogeneous intercept in the regression
model, and all trend coefficients to be set at zero. In addi-
tion, the current study also incorporated the Fisher test of
𝜀it =𝜆𝜀it −1+𝜇it
cointegration, which proposes an alternative technique over
the Pedroni and Kao panel cointegration tests. This test has
an advantage over the previous two tests in the sense that
it relaxes the assumption of a unique cointegration vector
among the selected variables.
FM‑OLS andDynamic‑OLS estimates
FM-OLS and Dynamic-OLS models are incorporated after
confirming cointegration among selected study variables.
FMOLS and DOLS methods are applied to assess the long-
run elasticity between dependent and independent vari-
ables. In addition, these two methods are effective to solve
the problem of endogeneity among selected variables and
assess the serial correlation between error terms. The core
difference between the two methods is that previously, the
researchers uses the approach of non-parametric techniques
to handle the problem of autocorrelation and endogene-
ity, while in recent research, studies applied a parametric
method by including lead and lags values of the independ-
ent variables (Sun etal. 2018). In the case of a very small
sampled size, the results of DOLS are very efficient and
Fig. 1 Box plot summery statistics of the key variables

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improved, as suggested by Danish etal. (2020). The prob-
lem of cross-sectional dependency could be also handled by
using the DOLS approach since it obtains cross-sectional-
wise coefficients and after that those estimate generates,
consistent, efficient, and balance results. The FMOLS and
DOLS techniques could tackle the problem of heterogeneity
in the long-run cointegration panel.
Empirical results
Results ofpanel unit root tests
In this part of the empirical analysis, we represent the results
of panel unit root tests. First, we used few panel root tests
to identify the stationarity level of the variables used in the
model. In Table4, we present the results of different panel
unit root tests namely Lin, Levin and Chu, Fisher ADF,
and Lm, Pesaran, and Shin W-stat tests. The first row of
the Table4 shows the names of different unit root tests and
first column reports variable names used in the models.
The empirical output of the all unit root tests are given at
I (0) (level) as well as at the I (1) (first difference) against
each series. Overall, the empirical outputs indicate that all
variables are stationary at I (1). Therefore, the empirical
analysis of panel cointegration can be performed. Nemours
past research work suggest that if all variables of the model
are stationary at I (1), then the analysis of panel cointegra-
tion is good fit for estimation (e.g., see Pesaran etal. 2001).
Results ofpanel cointegration tests
In this study, we have applied most commonly used panel
cointegration tests such as Pedroni cointegration test, Kao,
and Fisher cointegration tests to check the long-run rela-
tionship among the dependent and independent variables.
The analysis of Pedroni panel cointegration test reports
two dimensions test statistics, i.e., within dimension and
between dimension test statistics, while, in all cases, the H0
of panel cointegration tested against the H1. Similarly, the
Kao panel cointegration test also performed which is based
on Augmented Dickey-Fuller (ADF) test statistic. Likewise,
the Fisher panel cointegration test is performed which is an
alternative test for cointegration analysis based on Trace and
Max-Eigen tests. In Table5, we present the results of all
cointegration tests, where the estimated output of cointegra-
tion analysis suggests the existence of long-run relationship
among all possible variables. The results of all three panel
Table 4 Panel unit root tests
results
Note: Asterisks (***) indicates 1% significance level.
Variables Lin, Levin, and Chu Fisher ADF Lm, Pesaran, and Shin
W-stat
Level 1st difference Level 1st difference Level 1st difference
lnEFP 0.06 -10.21*** 13.98 94.14*** 0.01 -9.89***
lnECI 0.72 -1.82*** 5.45 46.00*** 0.74 -5.00***
lnREC -0.48 -8.35*** 11.48 78.99*** 1.16 -8.24***
lnFFEC -0.62 -7.19*** 10.50 82.22*** 0.72 -8.39***
lnGDP -0.72 -4.90*** 8.52 43.70*** 0.46 -4.79***
lnPOP -0.37 -3.76*** 9.91 25.08*** 0.30 -3.35***
lnINF 5.43 -7.77*** 3.13 87.52*** 1.81 -8.83***
lnFDI -0.81 -9.40*** 29.03 100.54*** -2.84 -10.23***
lnINQ 0.48 -8.04*** 9.41 79.03*** -0.48 -8.03***
lnTRA -0.47 -8.52*** 9.18 77.10*** 0.68 -8.06***
Table 5 Panel cointegration
tests’ results
Note: Asterisks (*, **, and ***) indicates 10%, 5%, and 1% significance level respectively.
Pedroni cointegration test Kao cointegration test Fisher cointegration tests
Statistics Dimensions ADF No. CE Trace Max Eigen
Within Between t-statistic -1.579** At most 1 227.8*** 104.7***
Panel PP -1.506* Prob 0.050 At most 2 138.*** 68.77***
Panel ADF -2.339*** At most 3 79.4*** 48.31***
Group PP -1.455** At most 4 42.5*** 29.92***
Group ADF -2.632*** At most 5 24.2** 24.26**

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cointegration tests suggest that the H0 of no cointegration
can be reject either with 10%, 5%, and 1% level of signifi-
cance. The estimated output of Table5infer that there is
long-run relationship between economic complexity, renew-
able energy consumption, fossil fuel energy consumption,
economic growth, rural population growth, and ecological
footprint in G-7 countries.
Empirical results ofFMOLS
The econometric technique of FMOLS model is applied
to check the cointegration (long-run association) between
dependent and independent variables. In Models I to VI of
Table6, we reported results obtained from FMOLS tech-
nique. This suggests that all the predictors have considerable
long-run effect on EFP. In Models I to VI of Table6, the
variables lnECI, lnGDP, lnNREC, lnPOP, lnINF, lnFDI, and
lnTRA have statistically significant and lead to increases of
environmental pollution in terms of EFP, while the variables
of lnREC and lnINQ have significantly negative impact on
EFP in the case of G-7 countries.
From Model I to Model VI, economic complexity (ECI)
is statistically significant and positive even at 1% level
in Model IV, at 5% in Model V, and10% in Models I, II,
and III respectively. This result infers that a 1% increase
in the level of ECI causes to increase ecological footprint
by 0.039% (Model I), 0.036% (Model II), 0.034% (Model
III), 0.053% (Model IV), 0.033% (Model V), and 0.028%
(Model VI). The positive impact of ECI on ecological foot-
print suggests that the complexity of industrial structure and
productive activities are degrading environmental quality in
G-7 countries. The results of current study strongly support
the empirical findings of Ahmad etal. (2021a, b, c, d, e,
f) for emerging economies, Neagu (2020) for 48 develop-
ing and developed economies, Shahzad etal. (2021) for the
USA, and Yilanci and Pata (2020) for China. However, our
empirical results contradict the empirical work of Doğan
etal (2021), i.e., he reported a negative impact of ECI on
environmental degradation for OECD countries. Moreover,
the effect of ECI2 (non-linear influence of ECI) on environ-
mental degradation in terms of EFP is significantly nega-
tive. This relationship confirms the existence of environmen-
tal Kuznets curve (EKC hypothesis) or indicates inverted
U-shaped association between economic complexity and
EFP. By supporting the EKC hypothesis, the empirical find-
ings reveal that economic complexity leads to environmental
degradation but after reaching to a specific threshold level,
the higher level of economic complexity reduces ecolog-
ical pollution as can be seen from Model VI of Table6.
Explicitly, the empirical findings from Model VI show that
few important steps need to take to increase the level of
economic complexity for the achievement of high resource
efficiency since complex products insert effective and high
cost along with advance and complex technologies. This
Table 6 FM-OLS results
Note: Asterisks (*, **, and ***) indicates 10%, 5%, and 1% significance level respectively.
Variables Model I Model II Model III Model IV Model V Model VI
lnECI 0.039*
(0.022)
0.036*
(0.019)
0.034*
(0.018)
0.052***
(0.017)
0.033**
(0.017)
0.028***
(0.008)
lnREC -0.087***
(0.007)
-0.090***
(0.007)
-0.084***
(0.006)
-0.092***
(0.006)
-0.119***
(0.007)
-0.137***
(0.004)
lnNREC 0.511***
(0.116)
0.410***
(0.115)
0.465***
(0.109)
0.003***
(0.002)
0.004***
(0.001)
0.189***
(0.053)
lnGDP 0.483***
(0.040)
0.480***
(0.036)
0.463***
(0.034)
0.530***
(0.034)
0.550***
(0.031)
0.586***
(0.016)
lnPOP 0.009***
(0.001)
0.009***
(0.001)
0.009***
(0.001)
0.009***
(0.001)
0.009***
(0.001)
0.011***
(0.001)
lnINF 0.007***
(0.004)
0.006*
(0.004)
0.007**
(0.004)
0.001
(0.004)
-0.001
(0.001)
lnFDI 0.010***
(0.003)
0.010***
(0.003)
0.005*
(0.003)
0.004***
(0.001)
lnINQ -0.381***
(0.062)
-0.487***
(0.060)
-0.404***
(0.032)
lnTRA 0.149***
(0.023)
0.080***
(0.014)
ECI2-0.132***
(0.015)
Model statistics
R-squared 0.9780 0.9783 0.9789 0.9803 0.9819 0.9828
Adjusted R-Sq 0.9761 0.9761 0.9765 0.9779 0.9795 0.9803

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empirical findings are in line to the work of Ahmad etal.
(2021a, b, c, d, e, f) for emerging economies and Pata (2020)
for the USA but contrary to the empirical findings of Yilanci
and Pata (2020) for Chinese economy.
Regarding energies, in all 6 models, the coefficient of
renewable energy consumption (REC) is statistically signifi-
cant and negative. This infers that 0.087% (Model I), 0.019%
(Model II), 0.084% (Model III), 0.092% (Model IV), 0.119%
(Model V), and 0.137% (Model VI) decrease EFP because
of renewable energy consumption in G-7 economies. In con-
trast, the coefficient of non-renewable energy consumption
(NREC) is positive and statistically significant, suggest-
ing that a 1% increase in NREC increases EFP by 0.511%
(Model I), 0.410% (Model II), 0.465% (Model III), 0.003%
(Model IV), 0.004% (Model V), and 0.189% (Model VI) on
average in long run. However, concerning the actual effect
of disaggregated energy consumptions (REC and NREC),
our empirical results are consistent with our hypothesized/
expected signs in Table2 that renewable energy is encour-
aging environmental quality, while non-renewable energy is
unfavorable to environment. The results of Models 1 to VI
reveal that the consumption of renewable energy is possible
way to reduce the negative effect of economic activities.
Contrariwise, the consumptions of non-renewable energy
consumption adversely affect environmental sustainability,
while this result is not unexpected for us because most of the
economic activities rely on non-renewable energy such as
gas, oil, and coal (BP 2020). Around 80% of energy demand
for economic activities is fulfill by non-renewable energy
which is that main reason of environmental degradation. Our
empirical analysis is consistent with the work of Ahmad
etal. (2021a, b, c, d, e, f) for emerging economies and Ulu-
cak etal. (2020) for OECD countries.
As can be seen from Table6, the coefficients of GDP
per capita (economic growth) are statistically significant
and positive with EFP in all five models (Models I to VI),
which inferred that economic growth increases environmen-
tal degradation in G-7 countries. Specifically, a 1% increase
in economic growth will increase environmental degra-
dation by 0.483% (Model I), 0.480% (Model II), 0.463%
(Model III), 0.530% (Model IV), 0.550% (Model V), and
0.586% (Model VI). The empirical results obtained from all
five models reveal that economic growth and development
are deteriorating environmental quality as it is demanding
energy consumption and therefore creates more toxic waste
and pollution. The positive influence of economic growth
with EFP is expected because many developed economies,
particularly G-7 economies, are leading the world’s largest
share of economic growth and development. Therefore, in
order to accelerate economic activities and achieve higher
economic growth, the environmental quality has degraded
globally. The outcomes of our empirical work are in line
with empirical work of Ahamd etal. (2021) for emerging
economies, Baloch etal. (2020a) for sub-Saharan African
countries, Khan etal. (2021) for G-7 economies, Khan and
Yahong (2021) and Malik etal. (2020) for Pakistan, Fang
etal. (2021) for OECD countries, and Yilanci and Pata
(2020) for China.
Turning to the population growth, as can be seen from
Table6, the impact of population growth on environmental
degradation is statistically significant and positive. Particu-
larly, 1% increase in population growth will upsurge ecologi-
cal pollution by 0.009% (Models I to V). From this outcome,
we can inferred that the current population growth in G-7
countries effect environmental quality. The positive effect
of population growth on ecological footprint is not surpris-
ing and seems logical because many developed as well as
developing economies often ignore the adverse effect of
population growth on environment (Ahmad etal. 2021a, b,
c, d, e, f). Our findings are in line with the work of Abbasi
etal. (2020) for OECD countries, Khan etal. (2019) and
Khan and Yahong (2021) for Pakistan, Yahong and Khan
(2021) for China,and Rafique etal. (2021) for developed
economies.
The results obtained for the rest of the control variables
on environmental pollution are as follows: (i) The relation-
ship between inflation rate and EFP is positive and statisti-
cally significant even at 1% (Model II), 10% (Model III),
and 5% (Model V). (ii) The relationship between Foreign
Direct Investment (FDI) and EFP is positive and statisti-
cally significant at 1% (Models III and IV), 10% (Model V),
and 1% (VI). (iii) Income inequality has a negative effect
on EFP, while the coefficients are statistically significant
even at 1% (Models IV and VI). (iv) The relations between
trade and EFP are positive and statistically significant even
at 1% level (Models V and VI). It is important to mention
that we have also used panel DOLS econometric approach to
check the reliability and validity of our main model (Table6,
Model I). The empirical results obtained from DOLS model
in Table7 are in line with Model I of Table6, which endorse
the robustness of the study study’s results. In addition, the
Table 7 Dynamic-OLS results
Note: Asterisks (*, **, and ***) indicates 10%, 5%, and 1% signifi-
cance level respectively.
Variables Coefficient Std. Error t-Statistic Prob
lnECI 0.431*** 0.144536 2.987907 0.0087
lnREC -0.107*** 0.024518 -4.366604 0.0005
lnNREC 0.600 0.465502 1.290679 0.2152
lnGDP 1.033*** 0.211059 4.897911 0.0002
lnPOP 0.008** 0.002844 2.814852 0.0125
Model statistics
R-Squared 0.9980
Adjusted R-Sq 0.9877

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graphical representation of all the explanatory variables on
EFP is expressed in Fig.2.
Conclusion andpolicy implications
The current study analyzes the relationship between eco-
nomic complexity, renewable energy consumption, non-
renewable energy consumptions, economic growth, rural
population growth, and EFP in G-7 countries over the period
of 1996–2019. To this end, we employed different unit root
tests such as Lin, Levin, and Chu, Fisher ADF, and Lm,
Pesaran, and Shin unit root tests to check the stationarity
level. In additions, in this study, we adopted three cointegra-
tions techniques such as Pedroni, Kao, and Fisher to analyze
the long-run relationship among the study’s variables. The
empirical findings of all unit root tests reveal that all vari-
ables are stationary at first difference, while the findings of
cointegration tests confirms the existence of long-run asso-
ciation among the variables. The outcome obtained from all
models FMOLS and DOLS reveal that there is significantly
positive association between economic complexity and envi-
ronmental pollution by raising EFP. The results further sug-
gest that the higher demand of renewable energy mitigate
environmental degradation by decreasing EFP. Moreover,
the demand for non-renewable energy and economic growth
upsurge ecological pollution. However, based on our analy-
sis, few recommendations and policy implication can be
drawn as follows:
First, our analysis suggest that the prevailing economic
activities and existing structure transformation in G-7 coun-
tries are not environmental friendly, i.e., the long-run effect
of economic complexity on environmental quality is posi-
tive. But, on the other hand, the non-linear effect of eco-
nomic complexity on EFP is negative; therefore, the concern
authorities and government of G-7 countries must take into
account manufacture and complexity of products while mak-
ing the policies regarding environmental protection. Second,
G-7 economies should increase the demand for renewable
energy consumption by structuring advance energy units to
mitigate environmental pollution. Third, policymakers of
investigated economies should reconsider pricing policies
that can discourage the demand for non-renewable energy
consumption. To this end, the active institutions structure
are important which could enable the governments of G-7
countries to monitor policy implications more effectively so
that they can promote green economic growth in the future
(Ahmad etal. 2021a, b, c, d, e, f).
Although, the current study have vast importance for poli-
cymakers of G-7 countries, however, not without limitations
that should be considered and extended for future studies.
This study is limited to G-7 economies, and only few envi-
ronment influencing variables are incorporated while ana-
lyzing the effect of economic complexity and energies con-
sumptions on EFP. A good way to extend the future research
is to incorporate other environment influencing variables
such as urbanization, financial development, and human
capital and verify whether the empirical results are robust
to new variables. Best to our knowledge, this study leaves
the gap for future researchers to analyze the determinants of
environmental degradation for other developed and emerg-
ing economies. Also, micro-level analysis would be benefi-
cial for more effective policy implications. Finally, we could
not find loner dataset for the analysis of this study; therefore,
it would be more interesting to consider this limitation in the
future research studies, subject to the availability of larger
datasets.
Author contribution Salim Khan: Conceptualization, Methodology,
Data collection, Writing-original draft. Wang Yahong: Supervision,
Formal analysis, Writing-review and editing. Abbas Ali Chandio: Soft-
ware, Validation, Investigation, Validation.
Funding The research was funded by the National Social Science
Foundation of China (Grant No. 18BJY164),National Social Science
Foundation of China (Grant No. 20BGl168) Education Department
of Henan Province (Grant No. 19A790025), National natural Science
Foundation of China (Grant No. 41601566), and Humanities and Social
Science Project of Education Ministry (14YJCZH128).
Data availability The data that support the findings of this study are
openly available at the following URL https:// datac atalog. world bank.
org/ datas et/ world- devel opment- indic ators.
Fig. 2 Graphical representation of empirical findings

Environmental Science and Pollution Research
1 3
Declarations
Ethics approval Not applicable.
Consent to practice Not applicable.
Consent for publication Not applicable.
Competing interests The authors declare no competing interests.
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