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The intermittent effects of renewable energy on ecological footprint: evidence from developing countries

May 2021
Environmental Science and Pollution Research 28(26)

May 2021
28(26)

Authors:

National Institute of Technology Rourkela

This paper examines the relationship between renewable, non-renewable energy, natural resources, human capital, and globalization on ecological footprint from 1990 to 2016 for developing countries. We apply Westerlund co-integration technique to check the long-run relationship among the variables. The long-run elasticity of the model is analyzed through MG, AMG, and DCCE. For the robustness check of the long-run relationship among the variables, we use FMOLS and DOLS approach. The direction of causal relationship is determined through Dumitrescu and Hurlin causality test. Our findings revealed that economic growth, non-renewable energy, natural resource, and urbanization are inducing the ecological footprint of developing countries and reducing the environment's quality. To cope up with this situation, developing countries are bound to use more fossil fuel energy. The use of non-renewable energy consumption leads to increase the extraction of natural resources like coal and oil. However, renewable energy reduces the ecological footprint or improves environmental quality. Similarly, human capital and globalization have negative effects on ecological footprint. The results of causality test reveal that there are feedback effects between ecological footprint with economic growth, globalization, and natural resources. This study suggests that these developing countries should focus more on the investment in the renewable energy sector, improve quality education, and make stringent environmental policy for protecting the nations from ecological issues.

Conceptual framework. Source: Author’s construction

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Description of the variables

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Results of Pesaran-Yamagata homogeneity test

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Results of CIPS and CADF panel unit root tests

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Results of MG, AMG, and DCCE tests

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Figures - uploaded by Narayan Sethi

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Content uploaded by Narayan Sethi

Content may be subject to copyright.

RESEARCH ARTICLE

The intermittent effects of renewable energy on ecological footprint:

evidence from developing countries

Malayaranjan Sahoo

&Narayan Sethi

Received: 4 January 2021 /Accepted: 24 May 2021

#The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021

Abstract

This paper examines the relationship between renewable, non-renewable energy, natural resources, human capital, and global-

ization on ecological footprint from 1990 to 2016 for developing countries. We apply Westerlund co-integration technique to

check the long-run relationship among the variables. The long-run elasticity of the model is analyzed through MG, AMG, and

DCCE. For the robustness check of the long-run relationship among the variables, we use FMOLS and DOLS approach. The

direction of causal relationship is determined through Dumitrescu and Hurlin causality test. Our findings revealed that economic

growth, non-renewable energy, natural resource, and urbanization are inducing the ecological footprint of developing countries

and reducing the environment’s quality. To cope up with this situation, developing countries are bound to use more fossil fuel

energy. The use of non-renewable energy consumption leads to increase the extraction of natural resources like coal and oil.

However, renewable energy reduces the ecological footprint or improves environmental quality. Similarly, human capital and

globalization have negative effects on ecological footprint. The results of causality test reveal that there are feedback effects

between ecological footprint with economic growth, globalization, and natural resources. This study suggests that these devel-

oping countries should focus more on the investment in the renewable energy sector, improve quality education, and make

stringent environmental policy for protecting the nations from ecological issues.

Keywords Renewable energy .Natural resources .Globalization .Human capital .Ecological footprint

Introduction

The country follows the path of development process relying

on its resource base, including natural and human capital. In

earlier phases of the economic development, it was easier to

use and the pool of natural resources for a country. The rapid

extraction of natural resources boosts the development pro-

cess of developing countries, while the environmental quality

of these countries impaired by pattern and use of natural re-

sources (Bekun et al. 2019). Continuous use of natural re-

sources steadily increases the degree of environmental

degradation. This leads to explore more renewable energy

sources. Similarly, urbanization refers to the process of expan-

sion in the proportion of the population residing in urban

areas. Many cities are experiencing high growth of urbaniza-

tion (United Nations 2017). The sudden rise of urbanization

leads to environmental degradation, which is a global problem

for the environment but is more detrimental to developing

countries (Azam and Khan 2016). Similarly, globalization

has also some positive effects like reducing poverty, closing

the gap of income inequality in developing countries, but still

the environmental consequences are debated (Salahuddin

et al. 2019a,2019b;Destek2020;Khalidetal.2020).

Along with the path of development, ecological quality is

rapidly depreciating due to “deterioration of ambient air qual-

ity, reduction of forest cover, soil erosion, continuous varia-

tion in the pH value of water, and many other associated

reasons.”With income growth decelerating the rate of depre-

ciation, environmental sustainability continues to increase

above those income thresholds. The theoretical relation of

income-pollution represents an inverse U-shaped, known as

the theory of the environmental Kuznets curve (EKC) by

Responsible editor: Eyup Dogan

*Malayaranjan Sahoo

sahoomalayaranjan4@gmail.com

Narayan Sethi

nsethinarayan@gmail.com

Department of Humanities and Social Sciences, National Institute of

Technology Rourkela, Rourkela, Odisha 769008, India

Environmental Science and Pollution Research

https://doi.org/10.1007/s11356-021-14600-3

Kuznets (1955) that assumed an inverted U-shaped linkage

among environment and economic growth. In a seminal paper

by Grossman and Krueger (1995), studying the impact of

North American Free Trade Agreement (NAFTA) on the en-

vironment, it was found that an inverted U-shaped curve be-

tween growth and environmental quality. Today, when

assessing the environmental effects of economic develop-

ment, the ecological metric has chosen specifically as contam-

inants. In this analysis, however, we have selected the envi-

ronmental sustainability indicator for the ecological footprint

(EF). The ecological footprint describes the Earth’scarrying

capacity and the earth as an intrinsic futurist feature; it is also a

measure of sustainability, as characterized by Rees (1992)and

Wackernagel et al. (1999). They state that a common ecolog-

ical region can provide the goods that the parties engaged in a

plan to develop the use and at the same time of taking up the

waste formed by those groups. Wackernagel and Monfreda

(2004) later disregarded this concept. The ecological footprint,

according to them a calculation of the biological potential

required to manage an economic system which means that

the natural capital of the bio-capacity should have an essential

pool and also the waste produced by the economic system

should be capable of absorbing.

However, in reaction to global climate change, environ-

mentalists have pre-occupied themselves with studies on en-

ergy consumption (renewable and non-renewable), trade and

urbanization, globalization, human capital, and their environ-

mental impacts (Shahbaz et al. 2017a,2017b; Usman and

Hammar 2020;Sehrawat2020; Yao et al. 2020; Khan 2020;

Bakhsh et al. 2017; Xu and Lin 2017). Economic activity

raises the energy demand and stimulates industrialization, en-

couraging trade in turn. Though some of the developingcoun-

tries are rich in natural resources, yet weak technical and eco-

nomic structures are catalyzing unsustainable energy usage

and increasing greenhouse gas emissions. Since 2007, signif-

icant and unforeseen reductions in the cost of producing re-

newable energy have occurred. In conjunction with policy

initiatives aimed at reducing greenhouse gas emissions, ener-

gy systems, particularly solar and wind, have produced a par-

adigm shift in energy systems (Arndt et al. 2019).

Developing countries are mainly concerned with high

growth rate rather than the detrimental environmental impli-

cations of such growth. Energy Information Administration

(EIA 2013) states that energy consumption of developing

countries projected at 65% of world energy by 2040. It has

regarded that developing countries are consuming high

amount of non-renewable energy consumption than renew-

able energy, because of poor investment in renewable energy

sector forcing them to use fossils fuels, which has high carbon

contents (Hu et al. 2018). Changing the economic structure of

the developing countries is one of the most important factors

of high-energy consumption. Many developing countries de-

pend on high-energy intensive sectors (Miketa and Mulder

2005). Hence, it leads to increase environmental pollution,

adding harm to human health and sustainable environment.

It is also projected that developing countries expected to ex-

perience the high population growth through by 2040 (Keho

2016). Due to population explosion in developing counties,

resource scarcity can happen easily, triggering environmental

problems like global climate change, erosion, and habitat de-

struction. It has also claimed that developing countries have

resource endowments, unexplored renewable energy, and

frangibleness climate. Among the developing countries,

China and India are the 1

and 3

position in terms of world’s

emitters. Similarly, Indonesia, Mexico, and Brazil are

,12

,and14

carbon emitter in the world. These coun-

tries are highest emitter of CO

emissions because of high use

of non-renewable energy consumption like coal and natural

gas (Awodumi and Adewuyi 2020). As per the Global

Footprint Network (Global Footprint Network, 2018), the

world average ecological footprint per person was 2.75 global

hectares, and bio-capacity of world average is 1.63 global

hectares. Therefore, there is an ecological deficit of 1.1 in

the world. A footprint smaller than the planet’sbio-capacity

is a critical requirement for humanity’s long-term survival. If a

country fails to absorb its own population footprint, the coun-

try is therefore called as an ecological debtor because it has an

ecological deficit. Almost all developing countries are facing

the ecological deficit like Bangladesh, Pakistan, India,

Afghanistan, Haiti, Nigeria, Cameroon, and Sri Lanka.

Hence, in this paper, we are considering that higher ecological

footprint leads to reduced environmental quality. According

to Global Footprint Network (GFN 2018), about 80% of the

world’s population lives in a country with a significant envi-

ronmental crisis. The increased use of non-renewable energy,

in tandem with negative trade and a rise in urbanization,

would end environmental sustainability. Hence, this motivates

us to attempt this study in the developing countries.

In this paper, we examine the relationship between renew-

able energy, non-renewable energy, globalization, and natural

resources on the ecological footprint in 36 developing coun-

tries. The contributions of the paper to previous studies are as

follows: (i) there are many researchers who have discussed the

environment-growth-energy nexus in a panel as well as indi-

vidual studies. This study is the first empirical analysis in the

case of developing countries taking as ecological footprint (a

positive indicator), human capital, natural resources, and glob-

alization in a single lens. However, most of the previous stud-

ies have taken CO

emission as an indicator of environmental

quality (Ito 2017; Akram et al. 2020;Alietal.2019;

Salahuddin et al. 2019a,2019b). EF includes six types of

bio-productive land-use categories (grazing land, farm space,

carbon footprint, cropland, land for building, and to the

ocean). The EF is the only comparative metric against what

the biosphere will renew the resource demand of government,

corporations, and citizens. Due to the relatively stable

Environ Sci Pollut Res

production growth in developing countries in recent years,

through use of EF is optimal as it analyzes the ecological

effects of producing goods, to encourage a lifestyle needed

(Wachernagel and Rees 1996). (ii) This analysis is distinctive

of analysis techniques developed in the embraced panel data

that yield more accurate and robust forecasts. For the long-run

analysis, we apply the augmented mean group (AMG), which

permits to use of cross-sectional dependency presence in the

series. Morever, the Mean Group (MG) technique, and

Common Correlated Effects Mean Group (CCEMG) also

apply to check the edogeneity problem in the model.

The rest of the paper are organized as follows: the “Review

of related studies”section presents the review of related stud-

ies; in the “Theoretical underpinnings”section, we present the

theoretical underpinnings; the Data and methodology”section

discusses the data sources and methodology; results and dis-

cussion of the empirical analysis is presented in the “Results

and discussion”section, and conclusion and policy implica-

tions are presented in the “Conclusion and policy implica-

tions”section.

Review of related studies

There are extensive studies done on examining the relation-

ship between renewable, non-renewable energy consumption,

trade, urbanization, economic growth, and environmental deg-

radation. Researchers also used different indicators to measure

environmental quality like CO

emissions (negative indicator)

and ecological footprint and material footprint (positive indi-

cators). The main objective of our study is to measure the

relationship between renewable energy, non-renewable ener-

gy consumption, urbanization, and globalization on the eco-

logical footprint (EF) in developing countries. Hence, in this

section, we will discuss the previous studies on energy con-

sumption, urbanization, and globalization on ecological foot-

print, and this substantiates our study’sparameterization.

Energy consumption, economic growth, and

ecological footprint

The relationship between energy consumption and ecological

footprint depends on the economic growth or economic struc-

ture of a nation. On the very first level of economic develop-

ment, the energy demand of the country is primarily focus on

the use of non-renewable energy. This has the greatest effect

on environmental degradation because of the gigantic con-

sumption of fossil fuels. After a certain point, there will be a

reverse relationship between energy consumption and envi-

ronmental pollution; this is called environmental Kuznets

curve (EKC). Studies which found similar results in the panel

as well as a individual countries case such as Ahmed and Long

(2013). Gokmenoglu and Taspinar (2018), Seppälä et al.

(2001), Işıketal.(2019), Rauf et al. (2018), Al-Mulali et al.

(2015), Apergis and Payne (2014), Dogan and Turkekul

(2016), Dogan and Seker (2016), Dogan and Aslan (2017),

Dogan (2014), Udemba (2021), and Alharthi et al. (2021). In

other set of literature estimated the existence of EKC hypoth-

esis by using ecological footprint, a study by Uddin et al.

(2016) investigated the relationship between income level

and environmental quality in 22 countries during 1961–

2011. They found that only 10 countries validate the existence

of EKC hypothesis. They suggest that clean technology and

efficient energy resources should be used in the production

process to reduce the ecological footprint in the sample

countries. Similarly, Dogan et al. (2019) examined the deter-

minants of ecological footprint in case of MINT countries for

the period of 1971–2013. They have applied ARDL bound

testing approach for investigating the long-run and short-run

relationship. They found that all four countries show the exis-

tence of EKC hypothesis in the sample period. A recent study

by Haldar and Sethi (2020) investigated the relationship be-

tween renewable energy consumption, economic growth, and

emissions in developing countries. They also found the

existence of EKC hypothesis in the sample countries.

A recent study on the linkage between energy consumption

and the ecological footprint was led by Charfeddine (2017)

using the Markov switching model in Qatar. He found that the

ecological footprint and the ecological carbon footprint and

CO2 emissions are positively related to energy use and

financial growth. For a newly industrialized economy,

Destek (2020) found that economic growth and ecological

footprint are positively related to each other. Shahzad et al.

(2020) examined the “relationship between energy consump-

tion and ecological footprint in the USA.”They reported that

economic sophistication and the use of fossil fuel oil substan-

tially increase the ecological footprint of the USA. In compar-

ison, the quantitative causality empirics indicated the presence

of causal connections with the ecological footprint between

economic complexity and energy use. There also many stud-

ies which used other indicators for measuring the environmen-

tal quality like CO

emissions (Zhang and Lin 2012;Isenberg

et al. 2002; Salahuddin and Gow 2014; Mert and Bölük 2016;

Ali et al. 2016; Ehigiamusoe and Lean 2019; Apergis and

Ozturk 2015; Apergis and Danuletiu 2014; Altinoz and

Dogan 2021; Bhujabal et al. 2021; Omoke et al. 2020;Saint

Akadiri et al. 2020;Yangetal.2021).

Similarly, the study by Khan and Hou (2021) examined

interlink between energy consumption and ecological foot-

print in 37 International Energy Agency (IEA) during 1995–

2015. They used advanced panel technique for analyzing the

results and found that energy consumption in these countries

is reducing environmental quality in the long run. However, a

study by Sharma et al. (2021) investigates the role of renew-

able energy consumption on environmental quality (proxy of

ecological footprint) in eight developing countries of Asia.

Environ Sci Pollut Res

They have applied cross-sectional autoregressive distributed

lag model (CS-ARDL) for the long-run estimations and found

that renewable energy enhances environmental quality in

these countries. However, the effect of life expectancy is

positive but insignificant. Majeed et al. (2021) studied in

Pakistan on aggregate and disaggregate energy consumption

and environmental quality. They apply non-linear

autoregressive distributed lag (NARDL) model for the analy-

sis, and the empirical results for marginal intake show that

only harmful shocks have a substantial effect on ecological

footprint. Similarly, different forms of energy consumption

have asymmetric impacts on the environment.

Urbanization, globalization, and ecological footprint

A recent study by Rashid et al. (2018) investigated the relation-

ship between urbanization and ecological footprint in two

towns of Pakistan. They found that both the towns have used

ecological footprint than bio-capacity. They suggest that utili-

zation of green energy resources and energy-saving technology

would be the best way to reduce ecological footprint. In con-

trast, Nathaniel et al. (2019) claimed that urbanization and en-

ergy consumption are enhancing environmental quality in the

long run in south Asia during 1965–2014, while Nathaniel et al.

(2020a,2020b) investigated the relationship between renew-

able energy, financial development, and urbanization on eco-

logical footprint in MENA countries and found that urbaniza-

tion and financial development are deteriorating environmental

quality. Urbanization has become a paradigm change from rural

to urban environments with social and economic capacities.

Developing countries have undergone rising urbanization

speeds, while their industrialized counterparts appear to have

high urbanization rates (Sadorsky 2014). Many other studies

empirically analyzed the link between urbanization and

environmental quality. A study by Liang et al. (2019)using

advanced panel technique in the case of China found that due

to urban agglomeration, it has a positive impact on environ-

mental pollution in China. Similarly, a study by Shahbaz

et al. (2019) examined the connection between urbanization

and environmental quality in UAE for a period 1975–2011.

They have found that urbanization worsens environmental

quality. For a developing country, a study by Martínez-

Zarzoso and Maruotti (2011) examined the nexus between ur-

banization and environmental degradation, and the outcome

shows that urbanization and carbon pollution have an inverted

U-shaped relationship. Combining the nations based on thresh-

old analyses implies that pollution-urbanization has been neg-

ative at a given point, and emissions remain constant as urban-

ization goes beyond that point. Other groups’findings suggest

that urbanization would not encourage greenhouse gas emis-

sions, but rather income and population. Sharma (2011)studied

for a panel of 69 nations and found that economic growth,

financial development, and energy use enhance CO

emissions,

while urbanization reduces it. Natural systems in mountains

have been severely impact by rapid urbanization. Ecological

footprint is a vital basis for determining whether a country or

region’s growth is beyond its bio-capacity as a metric of fair

utilization of natural resources (Ding and Peng 2018). Long

et al. (2017) stated that ecosystems are subject to a variety of

complex and heterogeneous effects because of urbanization.

They have used ecological footprint to show the

environmental sustainability in the sample period. A recent

studybyGodiletal.(2021) investigated the relationship be-

tween transport service, financial development, and urbaniza-

tion on ecological footprint in Pakistan during 1980–2018.

They have used quantile autoregressive distributed lag

(QARDL) model for the long-run and short-run analysis.

They found that transport service and financial development

are enhancing environmental quality while urbanization

deteriorating it. A recent study by Nathaniel et al. (2021) found

that urbanization and natural resources are negatively related

with ecological footprint.

According to Shahbaz et al. (2017a,2017b), globalization

means the transfer of technologies via industrialized to emerg-

ing markets by foreign direct investment (FDI) and imports,

assistance in dividing labor, and increase in the value creation

of the respective economies. Globalization has a strong

influence on global prosperity with an increase in economic

growth. Absolute productivity, FDI, and commerce thus

implicitly rise energy use and depletion of the ecosystem. In

comparison, Lv and Xu (2018) found that globalization re-

duces environmental quality. Similarly, for an 83 nations stud-

ied, You and Lv (2018) found an “undesirable association

between globalization and CO

emissions.”In contrast,

Kwabena Twerefou et al. (2017) explored the correlation be-

tween globalization and environmental impacts for a panel of

36 African countries using GMM methodology. Similarly,

with a panel of 25 developing countries, Shahbaz et al.

(2018) investigated the impact of globalization on CO

emis-

sions. It is important to note that the results show a favorable

effect of globalization on pollution in developing economies.

Using panel-estimating methods, Salahuddin et al. (2018)

found that globalization improves environmental quality in

SSA regions, while urbanization worsens it. Martens and

Raza (2010) used the Maastricht globalization index (MGI)

to analyze globalization’s competitiveness against numerous

sustainability parameters. They could not find any relation

between them. A study by Ahmed et al. (2019) found that

there was not any significant relation with globalization and

ecological carbon footprint in Malaysia. However, it shows

positive relation with ecological footprint. From the above

literature, we found that there is a diverse finding between

globalization and ecological footprint. There are fixed find-

ings about the globalization and ecological footprint from the

literatures (Dogan 2016; Sabir and Gorus 2019; Usman and

Hammar 2021; Nathaniel et al. 2021)

Environ Sci Pollut Res

Trade openness, population, and ecological footprint

Through the several channels, trade openness can have a poten-

tial effect on the ecological footprint, and this influence can be

either beneficial or harmful. This trajectory of influence is deter-

mined by the degree of growth and industrialization in a country.

In the case of an advanced and developing world, the importation

of enhanced technological innovations and efficient manufactur-

ing practices is possible, and trade openness has an impact on the

climate through technology. Due to such consequences, mostly

during the manufacturing process, environmental quality is en-

hanced. On the opposite, the main priority of lawmakers of any

nation at the earlier stage of development is to achieve prosperity,

even at the expense of the environment (Destek 2020). However,

to improve efficiency, inexpensive and polluting technology is

introduced into certain countries, and in this instance, the tech-

nique impact of trade openness negatively affects the ecological

standard (Al-Mulali and Ozturk 2015). A recent study in the

emerging country by Aydin and Turan (2020) examines the

“relationship between financial openness and trade openness

on the ecological footprint in BRICS countries.”They found that

in China and India, trade transparency has lowered

environmental emissions, while in South Africa, it has

increased. Similarly, a study by Kongbuamai et al. (2020)inves-

tigate the linkages between trade openness, population density,

and economic growth on the ecological footprint in Thailand.

They revealed that trade openness has reduced environmental

quality, while population density increased during the study

period. Similarly, Uddin et al. (2017) apply DOLS for the

long-run estimations between economic growth, financial devel-

opment, and trade openness on ecological footprint in 27 high-

emitting countries and found that real income has positive effect

on ecological footprint, while trade openness has negative effect

but insignificant. However, a study by Ali et al. (2020)using

dynamic common correlated effect in OIC countries during

1991-2016 found that trade openness has positively affected

ecological footprint in these countries during the sample period.

They found that trade openness has positively affected ecological

footprint in these countries during the sample period. Mrabet and

Alsamara (2017) have used two models for the empirical inves-

tigation between trade openness and financial development on

emissions and ecological footprint. They found that trade

openness was positive link with CO

emissions whereas nega-

tively related with ecological footprint. It means in ecological

footprint case, trade openness enhances environmental quality

in the long run.

Population size affects the size of a person’s average eco-

logical footprint. A density of population implies that in that

country, there is a lot of land area per person. There could be

more land and services available for an individual to use in

his/her way of living. However, if the landscape is quite re-

source-limited, a low population density (Niccolucci et al.

2012) will not improve the ecological footprint size. There

could be more land and services available for an individual

to use in his/her way of living. The relationship between pop-

ulation and ecosystem has been recognized since Malthus

(1798). Toth and Szigeti (2016) claimed that environmental

degradation is not due to increase in population; it is because

of the consumption pattern of individuals. From the above

literature, we found that there are dearth of studies that have

been done on environmental quality in case of country-

specific or panel studies. However, very few studies have

carried out in developing countries case. Many studies have

not taken care the endogeneity issue, while in this paper, we

apply advance econometrics technique to solve the issue. Last

but not the least, we have taken ecological footprint for the

measurement of environmental quality in developing coun-

tries, by taking the variables like renewable and non-

renewable energy, urbanization, and natural resources. In this

way, this study adds contribution to the previous literatures.

Theoretical underpinnings

Ecological footprint monitors nature’s demand and supply. The

ecological footprint calculates on the demand side, the ecolog-

ical assets needed by a civilized group to generate the natural

resources it uses and to contain its waste, in particular carbon

emissions. The ecological footprint monitors six types of viable

surface areas: “cropland, livestock fields, fish stocks, built-up

land, woodland areas, and demand for carbon on land.”The

bio-capacity of a country on the supply side reflects the pro-

ductivity of its biological properties. It absorbs the waste par-

ticularly carbon emissions (Bilgili and Ulucak 2018). Initially,

Rees (1992), later it was developed by Wachernagel and Rees

(1996) and coined the term ecological footprint.

In pursuit of environmental analyses, ecological footprint

measure is commonly use around the world (Van den Bergh

and Verbruggen 1999). It encourages individuals in the econ-

omy to quantify and optimize the utilization of capital and

investigate the viability of consumer lifestyles (GFN, 2019).

Many studies also used the term carbon footprint, which is

expressed as the weight of CO

. Carbon footprint is a part of

ecological footprint. Hence, in this paper, we used ecological

footprint and its macro determinants in developing countries,

i.e., renewable and non-renewable energy consumption, ur-

banization, globalization, population, and trade.

In the twentieth century, along with the exponential in-

crease of the human population, there was extensive migration

to major cities. The subsequent losses in biodiversity and

shifts in habitats rival some of the mass extinctions suffered

by the planet in the past (Magurran and Dornelas 2010). Most

improvements in the landscape, including agricultural and in-

dustrial use, are the result of human land-use activities. In this

land-use practice, urbanization is exceptional in both the

speed as well as the degree of all its effect on natural

Environ Sci Pollut Res

ecosystems (Wear and Bolstad 1998). Globalization is char-

acterized as “a move to a more interconnected, interdependent

global economy from self-constrained and independent na-

tional economies with trade and investment barriers, regula-

tions, and cultural differences”(Ahmed et al. 2019). Through

trade and foreign direct investment channels, globalization

may influence the environment. For example, while starting

or expanding their business ventures, foreign investors can use

sophisticated technology to improve the quality of the envi-

ronment by reducing energy demand.

It is very important to know effects of human capital on

ecological footprint. Ecological learning is a way that encour-

ages people to discuss global hazards, participate in solving

problems, and take steps to change the ecosystem (Daniels

and Walker 1996). As a result, people have a better under-

standing of environmental challenges and have the skills to

build informed and rational choices. Human capital decreases

the use of fossil fuels, which ultimately increases the

efficiency of the atmosphere by controlling high carbon

emissions without affecting economic development.

Knowing the idea of ecological footprints shows that the

populace is such a key element of development. Rees (1992)

explicitly showed that mankind cannot afford to consume

more than the Earth can create, by developing a quantitative

instrument for understanding the availability and demand for

natural resources. There will be a mismatch between the de-

mand and supply of resources if population growth will be

accelerating. According to GFN (2018), when the demand

for natural resources is higher than supply, it is called an

ecological deficit or when the population growth rate is higher

than the area’sbio-capacity.Valinetal.(2013) pointed out

that one-third of environmental degradation is cause by global

food production. Each human introduced to the Earth needs to

feed, which increases the global production of food steadily

and, as a result, increases the number of greenhouse gases that

reach the atmosphere. By considering the above linkages of

ecological footprint, we have chosen explanatory variables

like urbanization, globalization, trade, renewable, population

density, and non-renewable energy consumption in develop-

ing cases. Figure 1describes the relationship between the

dependent variable and independent variables graphically.

Data and methodology

Data

We use the annual data of 36 developing countries (see

Table 10 Appendix) from 1990 to 2016. The period of this

study is based on the data availability of the main variable

“ecological footprint.”For determining environmental quali-

ty, we use data of ecological footprint per capita measured as

global hectares (gha/person). Many researchers also proposed

this indicator for calculating environmental quality (Isenberg

et al. 2002; Nathaniel et al. 2019; Shahzad et al. 2020). The

description of the variables is presented in Table 1as follows:

Model specification

The main objective of thispaper is to examine the relationship

between urbanization, globalization, and natural resources on

ecological footprint. To achieve this objective, we follow

studies such as Stöglehner (2003), Zhao et al. (2005), Miller

et al. (2013), Figge et al. (2017), and Sabir and Gorus (2019)

by including an inclusive measurement of urbanization and

globalization index on ecological footprint as follows:

EF ¼fGDP;RE;NRE;TR;GI ;URB;PO;NR;HCðÞð1Þ

where EF represents ecological footprint, GDP is economic

growth, RE and NRE denote renewable and non-renewable

energy consumption, TR istrade openness, GI measures glob-

alization index, and urbanizationsymbolized as URB. POand

NR denote population density and natural resources, respec-

tively. HC represents human capital index based on the aver-

age year of schooling (Barro and Lee 2013). To smoothness

the data, all variables are transformed into natural logarithmic,

and this may produce appropriate long-run results (Sahoo and

Sethi 2020a; Sahoo and Sethi 2020b). The above functional

form can be written as:

lnEFit ¼ϒ0þϒ1lnGDPit þϒ2lnREit þϒ3lnNREit

þϒ4lnTRit þϒ5lnGIit þϒ6lnURBit

þϒ7lnPOit þϒ8NRit þϒ9HCit þεit ð2Þ

Ecological

Footprint

Economic

growth (+)

Renewable

energy (-)

Non-renewable

energy (+)

Trade (+/-)

Globalizaon(-)Urbanizaon(+)

Populaon (+)

Natural

resources (+)

Human Capital

(-)

Fig. 1 Conceptual framework. Source: Author’s construction

Environ Sci Pollut Res

where t denotes the time period from 1990 to 2016, i repre-

sents the countries observe in this paper, and εis the error

term. ϒ

…ϒ8 discuss the log run coefficient of variables used

in this paper.

Econometrics methods

Cross-sectional dependency and slope homogeneity test

In the first case of panel studies, it is essential to examine the

CD test. Otherwise, it will give spurious results. Growing

associations trigger cross-sectional dependency (CD) across

socio-economic structures and typical unidentified shock,

which makes traditional panel estimation methods inaccurate.

Hence, ignoring cross-sectional dependence will also have

severe implications (Ertur and Musolesi 2017). It is best to

work with CD even though standard unit root testing implies

cross-section independence. Therefore, depending on

methods that presume cross-sectional independence, it can

yield inaccurate findings. We use CSD to fulfill this function,

which was introduced by Pesaran (2007). The CD equation is

given as:

CD ¼ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

NN−1ðÞ

s∑N−1

i¼1∑N

j¼iþ1b

ρij

 ð3Þ

Besides, we have applied a slope homogeneity test in our

model. It was developed by Pesaran and Yamagata (2008).

Thetestequationiscalculatedthrough“delta-tilde and adjust-

ed delta-tilde.”

Unit root test

The normal unit root tests in models presume that “indepen-

dence of cross-sections and slope homogeneity and can there-

fore generate misleading consequences.”In this paper, we use

a second-generation unit root test, which checks the problem

of CD in the equation. The second-generation tests used in this

paper are CIPS and CADF introduced by Pesaran (2007). The

equation for the CIPS can be written as:

Δzit ¼αiþβizi;t−1;þρiTþ∑n

j¼1δijΔzi;t−1þεit ð4Þ

swhere z

is respect variables, ρis deterministic compo-

nents, δis level of significance, and εis error term in the

model. We can get the cross-sectional augmented Dickey

Fuller (CADF) from the above equation:

CIPS ¼1

N∑N

i¼1CADFIð5Þ

where CADF

represents “cross-sectional augmented dick-

ey fuller test”and N is the number of observations.

Co-integration test

To examine the long-run elasticity among the variables, it is a

prerequisite to test the co-integration on the sated variables. In

this paper, we apply the Westerlund (2007) panel co-

integration technique to get the association among the vari-

ables. The main advantage of the technique is that it controls

slope heterogeneity and cross-sectional dependency in the

model. This test has four results: two results indicate group

statistics, and the other two denote panel measurement. The

Table 1 Description of the variables

Variables Symbol Measurement Sources

Dependent variable

Ecological Footprint EF Ecological footprint per capita as global hectares (gha/person) GFN (2018)

Independent variables

Gross domestic product GDP GDP (constant $2010) World Bank (2018)

Renewable energy RE Total final renewable energy consumption (kilo tons) IEA (2019)

Non-renewable energy NRE Non-renewable energy consumption to primary energy (kilo tons) IEA (2019)

Trade TR Total export minus import (% GDP) World Bank (2019)

Globalization index GI Economic, social, and political dimensions of globalization KOF Swiss Economic Institute, Dreher (2006).

Urbanization URB Urban population (% of total population) World Bank (2019)

Population density PO Population density (people per square land of area) World Bank (2019)

Natural resources NR Total natural resources rent (% of GDP) World Bank (2019)

Human capital HC Index of human capital per capita Penn World Tables (PWT 9.0.)

Notes: IEA represents the International Energy Agency; GFN is Global Footprint Network

Source: Authors’compilations

Environ Sci Pollut Res

co-integration test of the error correction base is presented in

Eq. (6).

Δyt¼γidtþρiyi;t−1−βixi;t−1



þ∏ηi

j¼1δijΔyi;t−j

þ∏ηi

j¼0δijΔxi;t−jþεit ð6Þ

where cross-sections are indicated by N (i = 1,………,N)

and T (t = 1, ……,T) denotes number of observations.

AMG, MG, and DCCE technique

Once the long-term co-integration of variables has been

established, the next procedure is to examine the magnitude

of the variables. Several studies have claimed that CD occurs

between nations because of economic shocks and unobserved

components like trade openness and globalization. In this

modernization period, global developments in some countries

because of trade openness, each nation has been greatly influ-

enced (Dogan et al. 2020). To know the main results of this

study, we have applied the “augmented mean group (AMG),

mean group (MG) introduced by Pesaran et al. (1999), and

dynamic common correlated effect (DCCE) technique.”

Chudik and Pesaran (2015) developed the DCCE technique.

Compared to other conventional approaches, this estimation

methodology is more appropriate even in the presence of CD

and common shock. The strategy of the DCCE can comfort-

ably deal with by considering heterogeneous slopes in which

the parameters differ through cross-sections, heterogeneity,

and CSD problem in results. For robustness, we have used

“full modified ordinary least square (FMOLS) and dynamic

OLS (DOLS).”The FMOLS and DOLS are also extremely

effective in overseeing the effective processing of the prob-

lems of heterogeneity and serial correlations (Danish et al.

2018).

Panel D-H causality test

The non-causality test for Dumitrescu and Hurlin 2012) (D-H)

coping the heterogeneous panels and CD. This test is applica-

ble when T<N and T>N (Dogan and Seker 2016). The D-H

technique can be written as:

Yit ¼θiþ∑J

j¼1γi

JXit−1ðÞ

þ∑J

j¼1ηIYit−jðÞþεit

ð7Þ

where γ

and η

represent the coefficient of estimator,

which fluctuate across all nation. Y and X measures the cau-

sality. The D-H causality test as follows:

:α

=0 for θ

H1:αi¼0foralli¼1;2;3;…………:Ni

αi¼0foralli¼Nþ1;2;3;…………Ni



ð8Þ

Here H

and H

represent the null and alternative hypoth-

esis, respectively.

Results and discussion

The descriptive and correlation matrices are presented in

Table 2. In descriptive statistics, we discuss the mean, stan-

dard deviation, minimum, and maximum of series used in this

study. It is observed that the average value of ecological foot-

print, GDP, renewable, and non-renewable energy is 1.25,

3.48, 6.62, 4.54, and 6.85, respectively. However, the average

values of natural resources, globalization, and urbanization are

2.41, 5.90, and 2.75, respectively. Standard deviation mea-

sures the dispersion or spread of value from the mean. In our

case, it has shown that the standard deviation of ecological

footprint is 2.52, which means different countries have a dif-

ferent footprint as per their consumption, population, and area

of bio-capacity. Similarly, std. deviation of urbanization, pop-

ulation, and natural resources varies across different develop-

ing countries. The minimum and maximum values in our ob-

servation are 0.10 and 96.24, respectively.

The correlation matrix is presented in Table 2.Itmeasures

the strength of the linear correlation between the variables.

Some variables are positive, and some are negatively related

to each other significantly. It is described that “economic

growth and ecological footprint are positively related to each

other.”The ecological footprint tracks not just emissions as a

direct result of economic activity, but also the loss of resources

needed to facilitate economic activity. Hence, available bio-

capacity may affect economic growth in developing countries.

Similarly, natural resources, population density, urbanization,

and non-renewable energy consumption have positively relat-

ed to EF. However, renewable energy, human capital, global-

ization, and trade are negative and significantly affect ecolog-

ical footprint.

In next step, we verify whether the cross-sections are inter-

dependent. This is important for the selection of the required

root unit and tests for co-integration. Table 3discusses the CD

tests. It implies a transmission of shock in one country against

the other. The null is that “there are independent among the

countries against the alternative hypothesis of a cross-

sectional dependency across the regions.”The results of the

CD test delineate the cross-sectional dependency across the

developing countries as it rejects the null hypothesis at a 1%

level of significance. We also apply the Pesaran and

Yamagata (2008) homogeneity test, which suggests a meth-

odology to try to know the homogeneity of the slope in this

study. The slope homogeneity test is presented in Table 4.As

per the results of both CD and slope homogeneity tests, we use

heterogeneous panel models as well as the panel techniques

which control the cross-sectional dependency.

Environ Sci Pollut Res

After knowing the CD and slope of homogeneity in the

sample, we apply the cross-section augmented Dickey-Fuller

(CADF) and cross-section Im-Pesaran (CIPS) unit root test as

per the recommendation of Pesaran (2007). Table 5presents

the panel unit root tests. To know the order of integration, it

shows that all variables in the series are stationary at level and

it became stationary after first differentiation. Hence, order of

integration of variables is I (1) at different levels of

significance.

The conventional panel co-integration test like Pedroni,

Kao, and Fisher techniques does not capture the cross-

sectional dependency. Hence, in this paper, we apply

Westerlund 2007co-integration, which can be used in the pres-

ence CD. In Westerlund, co-integration has four coefficient

parametersi.e., two are group coefficient and the other two are

panel coefficient, which are presents in table 6. The results

from co-integration reveal that we can reject the null hypoth-

esis of no co-integration and accept the alternative hypothesis

of co-integration among the variables in the sample period.

The result in Table 7discusses the effect of the long-run

elasticity of independent variables on the dependent variable

or ecological footprint. We apply three long-run tests, i.e.,

mean group (MG), augmented mean group (AMG), and

dynamic common correlated effects (DCCE) approach. The

results of MG reveal that economic growth increases ecolog-

ical footprint in developing countries, or it reduces environ-

mental quality. There is always a range of contributors to the

degree of economic growth and prosperity in each nation. To

promote high growth, developing countries adopted different

mechanism, and the available potential of natural resources.

Development may have detrimental environmental conse-

quences. There are many aspects, which include environmen-

tal (pollution), nature reserves overuse, wildlife destruction,

and depletion, and climate change. These are the main prob-

lems that may be confronting developing countries; the dete-

rioration in environmental sustainability is considered a major

problem in the present and long-lasting living conditions of

the population. This finding is stable with Hassan et al. (2019)

for BRICS economies, Aşıcıand Acar (2016) for developing

countries, and Ahmad et al. (2020) for emerging economies.

However, renewable energy consumption reduces the eco-

logical footprint in developing countries. One percent increase

in renewable energy consumption leads to a reduced ecolog-

ical footprint by 0.04%. In line with this, in their respective

studies, Sinha et al. (2017) have determined that an intensifi-

cation of the use of renewable energy technologies could

Table 2 Descriptive and

correlation matrix EF GDP RE NRE TR GI URB PO NR HC

Mean 1.25 3.48 6.62 4.54 6.85 5.90 2.75 0.78 2.41 0.58

Std. deviation 2.52 0.45 0.56 0.98 0.63 0.54 1.88 2.54 4.75 0.49

Min. 0.57 2.48 1.58 0.96 1.75 0.57 0.86 0.10 0.78 0.04

Max. 85.71 45.05 96.24 56.45 75.52 10.63 80.45 5.75 8.85 0.79

EF 1

GDP 0.05* 1

RE −0.45* 0.10* 1

NRE 0.36* 0.218 0.11* 1

TR −0.03 0.45* −0.42 0.26* 1

GI −0.08* 0.13* 0.05* 0.12* 0.45* 1

URB 0.31* 0.30* 0.03* 0.40* 0.43* 0.31* 1

PO 0.24* 0.09 0.43 0.35* 0.12* 0.41* 0.24* 1

NR 0.02* 0.20* 0.31* 0.25* 0.40* 0.36* 0.13* 0.06* 1

HC −0.30* 0.32* 0.01* −0.13* 0.30* 0.05* 0.01* 0.42* −0.02* 1

Notes: * represents 1% level of significance

Source: Authors’calculation

Table 3 Cross-sectional

dependence test EF GDP RE NRE TR GI URB PO NR HC

Pesaran CD 0.54* 1.41* 5.12* 9.11** 2.42* 4.72* 4.31** 2.04* 5.06** 1.04*

Pvalue 0.002 0.000 0.000 0.012 0.004 0.000 0.030 0.000 0.024 0.003

Notes: * and ** represent 1 and 5% level of significance

Source: Authors’calculation

Environ Sci Pollut Res

regulate ecological footprint. Hastik et al. (2016)haveindicat-

ed that renewable energy use alone could not solve environ-

mental challenges without improving energy-saving

manufacturing processes.

Further, the study revealed that non-renewable energy

consumption endorses an ecological footprint. To cope

up with the current momentum, developing countries

are highly using fossil fuel energy. The use of non-

renewable energy consumption leads to increase extrac-

tion of natural resources like coal and oil. Furthermore,

the growth of the service industry, which is the product

of economic development, will continue energy needs

and, thus, contribute to emissions, thereby reducing en-

vironmental quality. The continuous use of carbon-based

energy has a direct impact on our health and well-being,

all of which are directly related to the environmental

consequences of non-renewable energy. Evidently, the

use of non-renewable energy sources has many adverse

consequences on our environment, either because of how

they are produced and stored or because they are used

and ultimately disposed of. Accordingly, the continuing

use of fossil energy turned out to be dangerous for the

nature of the atmosphere, resulting in an improvement in

the ecological footprint in these countries. Therefore, to

ensure sustainable progress in these countries, it is im-

portant to substitute fossil fuel solutions with clean en-

ergy solutions to promote the growth pattern (Dincer

2000).

Similarly, trade openness shows a positive relationship

with the ecological footprint. As economic growth and

trade are closely connected, this may lead to an increased

ecological footprint in developing countries. Economic

growth arising from trade expansion may have an appar-

ent direct effect on the bio-capacity. The finding is sim-

ilarly in line with Al-Mulali et al. (2015). They have also

found that trade openness, urbanization, and energy use

increase the ecological footprint in 58 countries. In the

same way, urbanization is positively related to the eco-

logical footprint. As the pace of growth of urbanization

varies across regions, it is very important to remember

that rapidly due to migration of people from rural areas

to urban areas for better life, developing countries’pop-

ulation in urban areas are growing. As the population is

overcrowded in the urban areas, they demanded more

energy consumption, and it will create an imbalance in

the regeneration of resources and absorption of waste.

Martínez-Zarzoso and Maruotti (2011)foundsimilarre-

sults in the case of developing countries while using

-based indicator as environmental quality.

Unlike urbanization, globalization reduces the ecological

footprint in sample countries. It means globalization enhance

environmental quality in developing countries in the study

period. Through globalization, advanced or efficient technol-

ogy is a transfer from developed countries to developingcoun-

tries. There are many advantages for globalizations to the en-

vironment like it lowers the cost of production, developing

Table 4 Results of Pesaran-Yamagata homogeneity test

EF GDP RE NRE TR GI URB PO NR HC

Delta 45.21* 63.48* 23.75* 12.51** 29.45* 13.45* 28.40* 51.12** 14.19** 23.12*

Adjusted delta 51.89* 86.52* 42.84** 14.72* 46.24* 49.12* 32.49* 54.71** 34.56* 47.01*

Notes: * and ** represent 1 and 5% level of significance

Source: Authors’calculation

Table 5 Results of CIPS and CADF panel unit root tests

CADF CIPS

Level 1

difference Level 1

difference

EF −2.42 −2.96** −2.12 −4.52**

GDP −1.21 −3.45** −1.96 −3.45**

RE −1.86 −2.76** −1.36 −3.86**

NRE −2.78 −5.42* −2.42 −4.15*

TR −1.65 −4.12** −1.23 −3.72**

GI −1.52 −3.45** −1.85 −3.62**

URB −1.32 −3.75* −1.62 −5.23*

PO −2.91 −4.72** −2.63 −5.27*

NR −1.06 −3.51* −1.40 −3.81*

HC −1.54 −3.46** −2.66 −4.12**

Notes: * and ** represent 1 and 5% level of significance

Source: Authors’calculation

Table 6 Westerlund’s

panel co-integration test Statistics Coefficient P value

−2.39** 0.04

−3.58* 0.00

−2.78** 0.05

−4.61* 0.00

Notes: * and ** represent 1 and 5% level

of significance

Source: Authors’calculation

Environ Sci Pollut Res

better environmental regulations and standards. Via forums

for international practices such as fair trade and eco brands,

it has been at the forefront of building public awareness of

labor and environmental standards. This result is also hold up

by literatures like Ansari et al. (2020) and Zafar et al. (2019).

Similarly, the natural resource has a positive effect on the

ecological footprint. It means that 1% increase in natural re-

sources is leading to a growth of 0.75% in ecological foot-

print. The positive value of the natural resources’coefficient

means that countries with no natural resource will import en-

ergy from fossil fuels (e.g., oil or gas) (Balsalobre-Lorente

et al. 2018). These findings indicate that these developing

countries are not adequately exploiting their natural resources

and are using poor energy policies that are unable to reduce

the country’s dependency on traditional sources of energy.

The ecological footprint in developing countries, especially

in relation to their mining activities, can be attributed to the

influence of the availability of natural resources. Natural re-

sources play a beneficial role in environmental destruction as

sustainable and maintenance options are balanced with de-

mand and development. As a result, the rate of natural re-

source loss and environmental stress decreases, allowing nat-

ural resources to recover.

However, the result delineates that human capital reduces

the ecological footprint in developing countries. This means

that an increase of 1% in human resources improves environ-

mental sustainability by 0.20%. Standard living, education,

and life expectancy measure human capital. When education

and living conditions are improved, wages increase, and clean

energy options are used rather than risks that obstruct a health-

ier way of life. Education also increases awareness among the

people about the use of energy-saving or green technology in

their daily life. The studies which are similarly in line with our

findings are Ahmed and Wang (2019), Hassan et al. (2019),

Chen et al. (2019), and Nathaniel (2020). However, the

coefficient of population density is positive but not significant.

We also found that the results of AMG and DCCE confirm the

same sign and magnitude as the dependent variable.

Robustness check

We have applied FMOLS and DOLS for the robustness check.

The FMOLS and DOLS are superior to OLS estimator be-

cause they permit to apply in case of small sample and control

endogeneity problem. Table 8discusses the results of FMOLS

and DOLS.

The results of FMOLS and DOLS also support the

previous long-run results of MG, AMG, and DCCE.

The results reveal that economic growth, non-renewable

energy, trade openness, urbanization, and natural re-

sources abundance are increasing ecological footprint

significantly. However renewable energy consumption,

globalization index, and human capital reduce it, thereby

enhancing the environmental quality in the sampled

countries. Hence government of each nation should more

focus on the investment in the renewable energy sector

and make stringent environmental policies for protecting

the domestic nation from environmental issues.

The causal interaction among the stated variables is

describes in the D-H causality test in Table 9.Thistest

is more suitable in the case of heterogeneous panels and

the presence of cross-sectional dependency in the mod-

el. The D-H causality test reveals that there are feed-

back effects on economic growth to ecological footprint,

ecological footprint to globalization, and ecological

footprint to natural resources and trade to globalization

in these countries. This supports the idea that urbaniza-

tion in these economies is a significant concern.

However, urbanization, trade openness, renewable ener-

gy, and population density drive ecological footprint.

Table 7 Results of MG, AMG,

and DCCE tests Variables MG AMG DCCE

Coefficient t statistics Coefficient t statistics Coefficient t statistics

GDP 1.57* 6.08 2.16* 7.47 1.75* 6.40

RE −0.04** −3.64 −0.07** −2.38 −0.06** −3.89

NRE 0.68* 5.05 0.74* 6.29 0.14* 5.38

TR 0.09** 1.65 0.33** 3.05 0.03** 3.99

GI −0.17** −1.21 −0.58* −6.80 −0.72** −2.35

URB 0.27** 2.02 0.04** 2.76 0.02** 2.31

PO 0.05 0.03 0.66 0.02 0.45 0.08

NR 0.06** 4.09 0.26* 5.91 0.56** 3.95

HC −0.36* −5.98 −0.41* −6.36 −0.19** −2.21

Notes: *, **, and *** represent 1, 5, and 10% level of significance

Source: Authors’calculation

Environ Sci Pollut Res

These findings are similarly in line with Nathaniel et al.

(2020a,2020b), Chen and Fang (2018), and Nyasha

et al. (2018).

Conclusion and policy implications

This paper examines the relationship between renewable ener-

gy, non-renewable energy, globalization, natural resources, and

human capital on ecological footprint for a balanced data of 36

developing countries from 1990 to 2016. We consider other

explanatory variables like urbanization, economic growth,

trade openness, and population in this study. Cross-sectional

dependency and second-generation unit root tests are employed

to know the dependency among the cross-sections and order of

integration among the variables to use in this paper. After get-

ting the results of the order of integration, we apply Westerlund

(2007) panel co-integration, as it can be used in the presence of

cross-sectional dependency in the series. To know the main

results of our analysis or long-run elasticity among the inde-

pendent variables and dependent variables, MG, AMG, and

DCCE panel techniques are employed. Results of MG reveal

that economic growth increases ecological footprint in devel-

oping countries, or it reduces environmental quality. There is

always a range of contributors to the degree of economic

growth and prosperity in each nation. Development mecha-

nisms were established in various economies depending on

the unique features of each nation and the possible natural

resources required encouraging high growth. Development

may have detrimental environmental consequences. This find-

ing is like “Hassan et al. (2019) for BRICS economies, Aşıcı

and Acar (2016) for developing countries, and Ahmad et al.

(2020) for emerging economies.”

Consumption of renewable energy in developing countries

lowers ecological footprint. Our results supported the findings

of Sinha et al. (2017). They found that intensification of the use

of renewable energy technologies could regulate ecological

footprint in the long run. Hastik et al. (2016)indicatedthat

renewable energy use alone could not solve environmental

challenges without improving energy-saving manufacturing

processes. However, non-renewable energy consumption en-

dorses an ecological footprint. To cope up with this situation,

developing countries are highly using fossil fuel energy. The

use of non-renewable energy consumption leads to increase

extraction of natural resources like coal and oil. Furthermore,

the growth of the service industry, which is the product of

economic development, will continue energy needs and, thus,

contribute to emissions, thereby reducing environmental qual-

ity. Accordingly, the use of fossil energy turned out to be

dangerous for the nature of the atmosphere, resulting in an

improvement in the ecological footprint in these countries.

Therefore, “to ensure sustainable progress in these countries,

it is important to eventually substitute fossil fuel solutions with

clean energy to promote the growth pattern”(Dincer 2000).

However, the result delineates that human capital reduces

the ecological footprint in developing countries. It means a

1% increase in human capital reduces 0.20% of ecological

footprint or increases environmental quality. Standard living,

Table 8 Results of FMOLS and DOLS tests

Variables FMOLS DOLS

Coefficient t statistics Coefficient t statistics

GDP 2.18* 6.78 1.61* 5.09

RE −0.10** −3.24 −0.23** −4.38

NRE 0.98* 5.62 0.51** 2.27

TR 0.01** 3.91 0.09** 4.22

GI −0.46* −6.01 −0.69** −5.00

URB 0.07** 2.48 0.13** 3.49

PO 0.37 0.05 0.78 0.12

NR 0.75** 3.42 0.15** 4.21

HC −0.20** −2.26 −0.03*** −1.64

Notes: *, **, and *** represent 1, 5, and 10% level of significance

Source: Authors’calculation

Table 9 Results of D-H Granger non-causality test

Null hypothesis W-bar Probability value Direction

EF≠GDP 8.35 0.008 EF↔GDP

GDP≠EF 10.54 0.000

RE≠EF 9.41 0.000 RE→EF

EF≠NRE 6.74 0.005 EF→NRE

URB≠EF 4.72 0.034 URB→EF

TR≠EF 5.36 0.002 TR→EF

EF≠GI 7.39 0.000 EF↔GI

GI≠EF 8.41 0.000

EF≠NR 5.02 0.026 EF↔NR

NR≠EF 6.64 0.013

PO≠EF 5.86 0.038 PO→EF

GDP≠RE 4.30 0.064 GDP→RE

NRE≠GDP 6.45 0.004 NRE→GDP

TR≠GDP 4.96 0.060 TR→GDP

GI≠GDP 7.62 0.000 GI→GDP

NR≠GDP 9.45 0.000 NR→GDP

RE≠GI 8.53 0.000 RE→GI

NR≠RE 5.26 0.007 NR→RE

PO≠RE 4.25 0.047 PO→RE

PO≠NRE 6.79 0.003 PO→NRE

TR≠NRE 8.22 0.000 TR→NRE

GI≠NRE 7.54 0.001 GI→NRE

TR≠GI 5.12 0.009 TR↔GI

GI≠TR 8.55 0.000

Source: Authors’calculation

Notes: ≠represents “does not homogeneously cause”

Environ Sci Pollut Res

education, and life expectancy measure human capital. When

education and living conditions are improved, wages increase,

and clean energy options are used rather than risks that ob-

struct a healthier way of life. Hence, government of each na-

tion should more focus on the investment in the renewable

energy sector and improvement of quality education and make

stringent environmental policy for protecting the domestic

nation from environmental issues.

From the empirical results, we suggest some important pol-

icy implications of this paper. As the results indicate that non-

renewable energy, consumption increases ecological foot-

print. This suggests that appropriate environmental policies

are required in the developing countries to mitigate the eco-

logical footprint. This necessitates serious intentions to in-

crease energy efficiency and reduce the share of fossil fuels

in the energy mix while still considering renewable energy’s

considerable potential in this regard. As the elasticity of re-

newable energy, consumption is negative and significant.

More importantly, the importance of renewable energy use

indicates that the developing economies are on track to be-

come carbon-free and achieving long-term prosperity.

Government and policy makers should focus on the more

production of renewable energy, and respective government

of each country should allocate more funds for the innovation

of infrastructure development in the renewable energy sector.

To create a sustainable future, it is recommended that devel-

oping country should increase the share of renewable energy

in their energy mix, handle their natural resources effectively,

and monitor their urbanization trend in a similar way to their

current consequences. Government should provide subsidies

for using the clean energy in these countries, so that people

will prefer to use more renewable energy than the fossil ener-

gy consumptions. These renewable energy sources vary from

fossil fuels in that they are pure and emit few pollutants.

Increased investment in environmentally sustainable technol-

ogy would reduce pollution while also boosting productivity.

In the case of natural resources, illegal activities such as ex-

traction and logging are widespread, so enhanced public con-

sciousness and stringent legislation are needed to keep this

nefarious behavior in check. Government should make it easy

for the small-scale miners for getting the license simpler.

Furthermore, when dealing with national energy protection

challenges, lawmakers should pay attention to natural re-

source mining practices by requiring businesses that harvest

mineral resources to use energy-efficient equipment in their

operations. As urbanization, showing positive effect on eco-

logical footprint, hence smart infrastructure and electricity hy-

brid cars can be introduced to transform urban traffic to green

transportation. Eventually, environmental awareness and

emerging educational reform practices can continue to reap

the benefits of long-term sustainability.

The present study has some limitations as follows: (i) it is

only focused on developing countries, (ii) the study is limited

to the period up to 2016 due to unavailability of data of eco-

logical footprints, and (iii) the study also ignored some of the

important variables such ecological footprints, globalization,

and natural resources due to unavailability data for some

countries. It can be further carried out by including all the

developing countries. Moreover, we have used ecological

footprints as a proxy of environmental pollution in our study;

however, other environmental indicators such as nitrogen and

sulfur emissions and carbon and material footprint may also

be added for further analysis. In addition, as per the develop-

ment of new empirical methodology, advanced methodolo-

gies can be applied for the empirical analysis.

Acknowledgements The authors are grateful to the Editor-in-Chief and

Editor, Prof. Eyup Dogan, and anonymous referees of the journal for their

extremely useful suggestions for the improvement of this paper. The

usual disclaimers apply.

Availability of data and materials The datasets generated and/or ana-

lyzed during the current study are available in the World Development

Indicators (2020). Links: https://databank.worldbank.org/source/world-

development-indicators#.

Author contribution All the authors contributed to the study conception

and design. Malayaranjan Sahoo and Narayan Sethi performed material

preparation and data collection, and Malayaranjan Sahoo performed anal-

ysis. Both the authors wrote the firstdraft of the manuscript; and Narayan

Sethi commented on previous versions of the manuscript. All authors read

and approved the final manuscript.

Declarations

Ethics approval and consent to participate Not applicable

Consent for publication Not applicable

Competing interests The authors declare no competing interests.

Appendix

Table 10 Sample countries considered for the study

Argentina Congo Republic Kenya Peru

Bangladesh Colombia Sri Lanka Philippines

Bolivia Costa Rica Mexico Paraguay

Brazil Dominican Republic Mozambique El Salvador

Botswana Ghana Malaysia Togo

Chile Haiti Nigeria Thailand

China Indonesia Nicaragua Tunisia

Cote d’Ivoire India Pakistan Turkey

Cameroon Jordan Panama Tanzania

Environ Sci Pollut Res

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Trade openness, human capital, natural resource, and carbon emission nexus: a CS-ARDL assessment for Central Asian economies

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Apr 2024
ENVIRON SCI POLLUT R

There is a call for global efforts to preserve the ecological systems that can sustain economies and people’s lives. However, carbon emission (CEM) threatens the sustainability of humanity and ecological systems. This analysis looked into the influence of energy use (ERU), human capital (HCI), trade openness (TOP), natural resource (NRR), population, and economic growth (ENG) on CEM. The paper gathered panel data from the Central Asia region from 1990 to 2020. The CS-ARDL was applied to establish the long-term interaction among the indicators. The paper’s findings indicated the presence of the environmental Kuznets curve (EKC) in the Central Asia regions. Also, the empirical evidence highlighted that energy use, natural resources, and trade openness cause higher levels of CEM. However, the research verified that CEM can be improved through human capital and urban population growth. The study also found that HCI moderates the interaction between NRR and CEM. The causality assessment indicated a one-way interplay between ENG, ERU, NRR, and CEM. The study proposes that to support ecological stability in these regions, policy-makers should concentrate on developing human capital, investing in renewable energy sources, and utilizing contemporary technologies to harness natural resources in the economies of Central Asia.

Do circular economy, renewable energy, industrialization, and globalization influence environmental indicators in belt and road initiative countries?

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ENVIRON SCI POLLUT R

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Jun 2024

The present research focuses on the endogenous development theory and investigates the relationship between economic growth (dependent variable) and renewable energy consumption, technological innovation, and export diversification (independent variables) in seven emerging economies known as the E-7. Previous studies have examined these factors individually but have not explored their combined impact on the E-7 economies. Therefore, this study contributes to the existing literature on the effects of renewable energy consumption, technological advancement, and export diversification on economic development. The study analyses the dynamic connection among these variables in seven selected emerging countries: Brazil, China, Indonesia, India, Mexico, Russia, and Turkey. Panel data from 1990 to 2022 is utilized, and various methodologies, including panel cointegration, the PMG-ARDL estimator, and robustness tests such as FMOLS and DOLS, are employed. Empirical inferences are drawn using the Dumitrescu-Hurlin Panel Causality (DHC) test, and the long-run relationships among the variables are validated using the Westerlund residual cointegration tests. The results from the PMG-ARDL estimator show that renewable energy consumption, technological advancement, and export diversification have a significant and positive impact on economic expansion, confirming the validity of the endogenous growth model in the E-7 countries. The control variable of the financial sector has a positive but insignificant effect on economic growth, while trade openness has a negative and significant effect. The DHC test results indicate a neutral feedback effect of renewable energy consumption on economic growth. The findings also reveal a unidirectional causal relationship between technological innovation and economic growth. Overall, these findings provide valuable insights for economic policymakers in the E-7 countries. By removing barriers to renewable energy consumption, technological innovation, and export diversification, policymakers can promote sustainable economic development.

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The goal of simultaneously increasing economic development while mitigating ecological disbenefits is a significant challenge for many countries. There is a growing recognition that greater attention must be given to environmental concerns to maintain and improve biodiversity and generate positive ecological outcomes as part of measures to meet the United Nations' Sustainable Development Goals (SDGs). Many countries have increased their reliance on renewable energy to reduce carbon emissions and overall ecological footprint, but it is argued that any positive relationship between economic growth and pro‐environmental outcomes relies heavily on the quality of a country's institutions. This paper investigates this contention, focusing on the so‐called Next 11 (N‐11) countries, that is those developing countries exhibiting the most rapid economic development in the last three decades. Using the cross‐sectionally augmented autoregressive distributed lags (CS‐ARDL) technique to address cross‐sectional dependence in panel data, this paper examines the effects of renewable energy and institutional quality on the ecological footprint of the N‐11 countries from 1990 to 2022. It shows that while economic growth frequently generates environmental degradation, increasing consumption of renewable energy reduces the ecological footprint provided that institutional quality contributes positively to pro‐environmental outcomes. This analysis highlights the important role of institutional quality when designing policies to promote environmental sustainability. The results provide a benchmark for policymakers in the N‐11 countries to increase investment in renewable energy resources to help deliver environmental sustainability goals while also emphasising the need for governments to develop improved institutional quality.

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The escalating apprehension regarding climate change mitigation has intensified the quest for energy alternatives that are low in carbon emissions, economically viable, and consistently available. Within this context, renewable energy sources emerge as fitting candidates, being recognized for their eco-friendliness and cleanliness. Nonetheless, despite the allure of transitioning towards cleaner energy, there exists a notable dearth of literature addressing the pivotal role of solar energy innovations and economic globalization in advancing the agenda of climate change mitigation (SDG-13), thus complicating the prediction of factors influencing ecological quality. Consequently, this study undertakes the inaugural investigation into the impact of solar energy innovation on ecological footprint, while also considering the influences of digitalization, economic globalization, renewable energy, and natural resources in the USA. To this end, Quantile-on-Quantile Kernel-Based Regularized Least Squares (QQKRLS) and wavelet quantile regressions (WQR) methodologies are employed, utilizing data spanning from 2000 to 2020. The analysis reveals that solar energy innovation, along with renewable energy, digitalization, and economic globalization, exerts a negative impact on ecological footprint, whereas natural resources exhibit a positive influence. Drawing from these insights, it becomes apparent that a concerted effort from stakeholders and policymakers is imperative in realizing the objectives of SDG-13 and SDG-7, necessitating a paradigm shifts in the USA’s energy portfolio away from fossil fuels towards renewables. Graphical abstract

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Apr 2024

Environmental degradation and economic growth are two intricately related issues whose impact is in constant increase within a global context marked by climate risks and corruption, notably in certain African countries. This research work examines the impacts of economic growth, corruption, renewable energy, and foreign direct investment on carbon dioxide emissions for a set of West African economies between 1990 and 2020. The current paper uses the PMG-ARDL panel method in order to assess the relationships between the various variables invested. The results are indicative of the long-term effects of variables. These findings demonstrate that GDP per capita has a positive and significant effect on CO2 emissions, and that the Kuznet curve is not validated in this case. Moreover, FDI confirms the pollution heaven hypothesis as it reduces environmental quality in the long run. In contrast, renewable energy consumption and control corruption in West African countries constitute significant factors in the fight for environmental quality. The causality outcomes reveal that there exist one way of unidirectional link between CO2 to both income and corruption, and a one direction causality from FDI to CO2 emissions. Meanwhile, the link between renewable energy and CO2 emissions is neutral. In this respect, this research offers outstanding findings to help maintain influential procedures for environmental sustainability within the West African framework.

ICT, foreign direct investment and environmental pollution in major Asia Pacific countries

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Aug 2021
ENVIRON SCI POLLUT R

This paper examines the effect of ICT and FDI on environmental pollution in major Asia Pacific countries during the year 1990-2018. We use Pooled Mean Group (PMG) and Dumitrescu-Hurlin Panel Causality for the estimation of the results. Our results suggest that ICT and FDI affect the carbon emissions or environmental pollution negatively. This implies that with the rise in ICT infrastructure and FDI inflows, environmental pollution decreases significantly in the long run. The Dumitrescu-Hurlin causality results suggest the existence of bidirectional causality among ICT and FDI which implies that increase in foreign investment leads to increase in ICT infrastructure and also, with increasing ICT infrastructure, the foreign investment increases in the Asia Pacific countries. On the policy forefront, the main focus should be targeted towards promoting FDI and ICT infrastructure in order to facilitate sustainable economic development in the Asia Pacific countries.

Analysis of CO2 emissions and energy consumption by sources in MENA countries: evidence from quantile regressions

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Mar 2021
ENVIRON SCI POLLUT R

The development of economies and energy usage can significantly impact the carbon dioxide (CO2) emissions in the Middle East and North Africa (MENA) countries. Therefore, this study aims to analyze the factors that determine CO2 emissions in MENA under the environmental Kuznets curve (EKC) framework by applying novel quantile techniques on data for CO2 emissions, real income, renewable and non-renewable energy consumption, and urbanization over the period from 1990 to 2015. The results from the estimations suggest that renewable energy consumption significantly reduces the level of emissions; furthermore, its impact increases with higher quantiles. In addition, non-renewable energy consumption increases CO2 emissions, while its magnitude decreases with higher quantiles. The empirical results also confirm the validity of EKC hypothesis for the panel of MENA economies. Policymakers in the region should implement policies and regulations to promote the adoption and use of renewable energy to mitigate carbon emissions.

What drives ecological footprint in top ten tourist destinations? Evidence from advanced panel techniques

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Full-text available

Jul 2021
ENVIRON SCI POLLUT R

The persistent dwindling of the biocapacity has caused the ecological footprint (EF) to keep increasing in the top ten tourist destinations over the last three decades. Moreso, economic growth, natural resource rent, and urbanization substantially increased for the same period. Tourism contributes to economic prosperity. However, growth in tourism promotes an increase in energy consumption, natural resource exploration, transportation, and consequential ecological distortions. It is important to understand the predictors of EF to suggest suitable policies for environmental sustainability. Hence, this study examines the predictors of EF by considering factors that could influence EF. More precisely, the study analyzes the linkage among economic growth, natural resource rent, urbanization, energy intensity, and tourism using some of the most advanced and reliable econometric techniques, such as Westerlund’s (J Appl Econ 23(2):193-233, 2008) cointegration technique, and the continuously updated full modified (CUP-FM) and continuously updated bias-corrected (CUP-BC), long-run estimators. The outcomes of the analysis indicate that EF has a negative association with urbanization and natural resources, which implies that an abundance of natural resources and increasing urban population can help to reduce environmental degradation. Likewise, energy intensity helps in decreasing EF, while economic growth adds to EF. Tourist arrival and tourist receipt also add to EF and, therefore, negatively affect environmental quality. Finally, policy directions are discussed to reduce environmental degradation without reducing economic growth.

Nexus of ecological footprint and foreign direct investment pattern in carbon neutrality: new insight for United Arab Emirates (UAE)

Article

Full-text available

Jul 2021
ENVIRON SCI POLLUT R

Edmund Ntom Udemba

Carbon neutral is among the possible ways of solving the problem of climate change. Many scholars have utilized different single indicators such as CO2 and methane with different variables to mitigate the possible ways of solving the problem of global warming. The present study employs a specific country (UAE) approach to investigate the possible way of solving climate change. The author utilized 1980–2018 annual data of the UAE to investigate the possibility of carbon neutral in the UAE as to suggest ways of limiting climate change. Both linear and non-linear (squared) foreign direct investment (FDI) and GDP per capita were added to the study and considered as the variables of interest and other control variables (energy use and population). Among the findings of this study are inverted U-shaped relationship between economic growth (GDP per capita) and ecological footprint which confirms EKC for the UAE, positive relationship between energy use and ecological footprint, negative relationship between FDI and ecological footprint in all stages which established a flat pattern of relationship, and a positive relationship between the population and ecological footprint. Findings from causal analyses exposed a two-way direction or feedback (bidirectional) between economic growth (GDP) and urban population, and between energy use and urban population both in the short and long run. Also, a one-way transmission (unidirectional) is found transmitting from ecological footprint and FDI to population both in the short run and long run; from the ecological footprint, energy use, and FDI to economic growth; from the ecological footprint and FDI to energy use in the long run; and from ecological footprint to FDI and energy use in the short run. The policy focus should be towards the improvement on the urban population. FDI pattern suggests the pollution halo hypothesis which is a pointer towards its (FDI) crucial contribution to environmental performance. Hence, FDI should be encouraged through relaxing of some laws that are preventive in nature towards FDI so as to maintain this positive trend towards sustainable development. Graphical abstract The analytical abstract demonstrates the interactions that exist among the employed variables in Granger causality analysis. The big arrows denote two-way transmission while the small arrows show one-way causal transmissions passing to the variables where they are pointing at.

Asymmetric effects of energy consumption and economic growth on ecological footprint: new evidence from Pakistan

Article

Full-text available

Feb 2021
ENVIRON SCI POLLUT R

This study explores the asymmetric effects of both aggregate and disaggregate forms of energy consumption along with economic growth on environmental quality for Pakistan covering the period from 1971 to 2014. We have employed unit root test with breaks for stationary checks, BDS test for nonlinearity check and nonlinear autoregressive distributed lag (NARDL) approach for assessing the asymmetric co-integrating relationships among the variables by decomposing them into positive and negative shocks. The empirical findings for aggregate consumption reveal that only negative shocks have a significant impact on ecological footprint. Similarly, different sources of energy consumption have diverse asymmetric effects on ecological footprint. The positive (negative) shocks to oil and gas consumption increase (decrease) ecological footprint. Thus, an increase in oil consumption has a deteriorating impact on environmental quality while a decrease in gas consumption has a favorable impact on environmental quality. The asymmetric relationships also hold between coal consumption, electricity consumption, and ecological footprint. The positive shocks to coal and electricity consumption are negatively related with environmental quality while negative shocks are positively related with environmental quality.

Remittance inflows affect the ecological footprint in BICS countries: do technological innovation and financial development matter?

Article

Full-text available

May 2021
ENVIRON SCI POLLUT R

This study examines the impact of remittance inflows, technological innovations, and financial development on environmental quality in Brazil, India, China, and South Africa (BICS) economies over 1990–2016. This study employed a comprehensive environment proxy, i.e., ecological footprint for environmental quality, and also considers more advanced and robust econometric (second-generation) techniques. The outcomes of the current study reveal that remittance inflows and financial development significantly deteriorate the environmental quality, while technological innovations are an essential factor for the reduction of ecological footprint level. Furthermore, the results of the interaction terms show a significantly adverse effect on the ecological footprint. Additionally, the findings of country-wise analysis reveal that remittance inflows and financial development worsen the environmental quality in each sample country, while the technological innovations promote the environmental sustainability that is steady with panel results. Besides, the environmental Kuznets curve (EKC) hypothesis was verified across the BICS economies. Consistent with the key findings, an inverted U-shaped relationship exists between economic growth and ecological footprint in the case of Brazil and South Africa. In contrast, the U-shaped EKC hypothesis exists in the case of China and India. For robust policy implication, the findings of this study highlighted the dire need for “green policy tools” that should be linked with the BICS economy policies and driver for sustained growth.

The step towards environmental mitigation in Pakistan: Do transportation services, urbanization and financial development matter?

Article

Full-text available

May 2021
ENVIRON SCI POLLUT R

This study is a scholarly effort to broaden the existing literature on the impact of 3 transportation services, urbanization, and financial development on ecological footprints in 4 Pakistan. Data used in this study covers the period of 39 years from 1980 to 2018. This study 5 adopted the QARDL model to tackle the non-linear association of variables and test their long-6 run stability across the different quantiles. The findings of this study indicated a significant 7 negative association of transportation services and financial development with ecological 8 footprints in Pakistan at almost all quantiles whereas, the urban population was found to be 9 positively associated with the ecological footprint in Pakistan. Results also justify the existence 10 of the EKC hypothesis in the scenario of Pakistan. Policymakers are advised to frame strategies 11 for investors to invest more in eco-friendly projects to curtail the ecological footprints in 12 Pakistan. Minimizing the dependency of the transportation sector on fossil fuel, and increased 13 use of energy-efficient appliances in the urban population would be beneficial to control the 14 negative influence on ecological footprints in Pakistan. 15 16

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

Article

Full-text available

Mar 2021
ENVIRON SCI POLLUT R

This article seeks to analyze the impact of technological innovations, financial development, renewable energy consumption, economic growth, and population on the ecological footprint in Asia Pacific Economic Cooperation (APEC) countries by utilizing the balanced longitudinal data set during the period from 1990 to 2017. This study creates a new technological innovation index through principle component analysis including three important indicators that represent the technology and employs a consistent environmental framework identified as Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model. The second generation panel estimation technique is employed to calculate robust and reliable outcomes. After confirming the cross-sectional dependency among series, panel unit root tests confirm that all variables are stationary at their first integrated order. Furthermore, Westerlund cointegration test confirms the presence of long-run association among variables. The outcomes explore that financial development and renewable energy utilization significantly accelerate the environmental quality by 0.0927% and 0.4274%, respectively. While, the increase in technological innovation activities, economic growth, and population size has a detrimental effect on environmental quality in the long run by 0.099%, 0.517%, and 0.458%, respectively. Moreover, the results of panel Dumitrescu and Hurlin (D-H) non-causality test discovered the bidirectional causality relationship between financial development, technological innovations, renewable energy consumption, economic growth, and population size with the ecological footprint. These empirical findings provide some vital policy implications for central authority and policymakers to overcome the detrimental impact on environmental quality in the APEC region.

An Essay on the Principle of Population (1798)

Chapter

Dec 2017

Thomas Malthus

How renewable energy consumption and natural resource abundance impact environmental degradation? New findings and policy implications from quantile approach

Article

Feb 2021

The EKC literature has ignored the importance of natural resources on environmental degradation. Thus, this paper aims to investigate the impact of renewable energy consumption and the abundance of natural resources on CO2 emissions for a panel of 82 countries by using quantile regressions. Empirical results show that renewable energy consumption reduces CO2 emissions and its effect increases in higher quantiles. The impact on carbon emissions of natural resource abundance is negative at lower quantiles but positive at medium and higher quantiles. Also, the validity of the EKC hypothesis is confirmed for all quantiles, and an increase in trade openness and urbanization increases environmental degradation in lower and middle quantile levels; however, these determinants have negative impacts on carbon emissions at higher quantiles. Policy implications related to this outcome are further discussed in the study.

The intermittent effects of renewable energy on ecological footprint: evidence from developing countries

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