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Energy Economics 121 (2023) 106661

Available online 11 April 2023

What derives renewable energy transition in G-7 and E-7 countries? The

role of nancial development and mineral markets

Muhammad Irfan

, Mubeen Abdur Rehman

, Asif Razzaq

, Yu Hao

School of Economics, Beijing Technology and Business University, Beijing 100048, China

Department of Business Administration, Ilma University, Karachi 75190, Pakistan

Lahore Business School, University of Lahore, Lahore, Pakistan

Research Division, CAREC Institute, Urumqi 830000, China

School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China

Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China

Beijing Key Lab of Energy Economics and Environmental Management, Beijing 100081, China

Sustainable Development Research Institute for Economy and Society of Beijing, Beijing 100081, China

Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314001, China

ARTICLE INFO

Keywords:

Energy transition

Mineral markets

Financial development

Clean energy

Sustainability

ABSTRACT

The enduring impact of the synergy between environmental sustainability and natural resources varies across

countries depending on the economic structure. Though the transboundary effect of mineral resources and

cleaner energy underlines the signicance of environmental performance, this research aims to scrutinize this

inclusive theme by investigating the dynamic effects of mineral markets, and nancialization on energy tran-

sition in selected developed and developing countries from 1990 to 2019. The cross-sectional autoregressive

distributive lags (CS-ARDL) model is applied to address slope heterogeneity and cross-sectional dependency. The

empirical ndings reveal that mineral markets signicantly contribute to the energy transition process in

accomplishing low-carbon power generation in G-7 countries. In contrast, nancial development negatively

inuences energy transition; however, it has an insignicant effect on the E-7 countries. The study outcomes are

veried by employing the augmented mean group (AMG) and common correlated effect mean group (CCEMG)

estimators. These ndings provide valuable policy recommendations for all stakeholders to drive the transition of

renewable energy toward low carbon and green growth.

1. Introduction

Currently, the challenge of reforming world’s energy system is a

prevailing subject for policymakers (Loorbach et al., 2017). Energy

transition

is a process that aims to produce sustainable energy, driven

by three overarching motives: reducing adverse environmental effects,

increasing energy independence, and growing the industrial and service

sectors to support sustainable economic development (Aldieri et al.,

2019; Lantz et al., 2021). Renewable energy and energy efciency are

commonly accepted as the principal aspects of this framework since they

can curtail greenhouse gas (GHG) emissions resulting from fossil fuel

consumption (Al Mamun et al., 2018). However, this subject must be

addressed with proper energy planning to proceed with the energy

transition process. As a result, rather than studying energy transition-

related concerns at a national level, a growing body of literature has

done so locally (Suitner and Ecker, 2020). This implies that effective

energy transition planning should include the specic aspects of coun-

tries (Lantz et al., 2021).

* Corresponding author at: School of Management and Economics, Beijing Institute of Technology, 5 Zhongguancun South Street, Haidian District, Beijing 100081,

China.

E-mail addresses: irfansahr@bit.edu.cn (M. Irfan), mubeenurehman@gmail.com (M.A. Rehman), asifrazzaq@yahoo.com (A. Razzaq), haoyuking@bit.edu.cn,

haoyuking@gmail.com (Y. Hao).

It is imperative to transfer the energy sectors from fossil fuels to clean energy; this process is referred to as the “energy transition” (Bond, 2018; De La Pena et al.,

2022).

Contents lists available at ScienceDirect

Energy Economics

journal homepage: www.elsevier.com/locate/eneeco

https://doi.org/10.1016/j.eneco.2023.106661

Received 14 September 2022; Received in revised form 7 March 2023; Accepted 2 April 2023

Energy Economics 121 (2023) 106661

The two most prevalent narratives in the literature deal with rapid

and gradual

energy transitions. Some scholars claim that it is irrational

to believe the energy transition has become boosted by the intermittent

nature and slow development of cleaner energy sources because of the

expensive infrastructure associated and historical policies of gradual

global transformations (Smil, 2016). Others argue that despite the

traditional evidence favoring conventional and gradual views, there is

sufcient evidence to suggest that they can happen promptly in specic

circumstances, particularly when climate change, scarcity, and techno-

logical innovations are becoming accelerating factors (De La Pena et al.,

2022: Sovacool, 2016).

The energy transition initiatives bring several challenges, for

instance, social, economic, and technical concerns (Papadis and Tsat-

saronis, 2020). The energy transition is experienced at varying rates in

different economies. When constraints like the Covid-19 epidemic

exacerbate the problem for countries already struggling to survive, both

in advanced and emerging,

economists tend to prefer economic and

security concerns above environmental preservation (Komendantova,

2021). Stakeholders can only develop solutions for a successful energy

transition by analyzing the distinct barriers and demands encountered in

each country separately (De La Pena et al., 2022; Relva et al., 2021).

The mining sector is a signicant contributor to the global economy

and is one of the most resource-intensive businesses.

Due to their vital

role in renewable energy transmission, metals, and minerals are

increasing in value, and the rising demand for clean energy technology

(Jiskani et al., 2022). A World Bank report claims that over 3 billion

tonnes of minerals

will be needed to install geothermal power, solar,

wind, and energy storage will be required to meet the target of the Paris

Agreement to limit global warming to <2 degrees Celsius. More mining

will be required to address the environmental challenges in a climate-

smart and sustainable manner due to the worldwide shift to low-

carbon technologies that primarily rely on minerals (Jiskani et al.,

2021).

It is well acknowledged that the increasing demand for mineral re-

sources increases the production volume and project complexity

(Dubinski, 2013), resulting in decision-making difculties (Chinbat and

Takakuwa, 2009). An intense mining process increases resource con-

sumption and operational hazards. Hence, it leads to the danger of

interaction between elements of sustainable mining and the mining

system, such as the community, environment, economy, safety, and

productivity. The impact of risks

impeding project objectives exerts an

incredible strain on the project’s overall performance. In the rst place,

mining operations promote a greener procedure and contribute to

cleaner energy generation. To accomplish cleaner energy targets based

on activities of minerals mining, it is indispensable to conduct a risk

assessment to mitigate, avoid and control the risk management strate-

gies (Amoatey et al., 2017).

Fig. 1. Energy transition situation between 1990 and 2019.

Table 1

Variables’ description.

Variable name Symbol Source Measurement unit

Energy Transition ET WDI

Clean energy concerning fossil fuel-

based energy consumption.

Mineral markets MM WITS Share of export products in percentage

Financial

Development

FD IMF

The access, depth, and efciency of

nancial institutions and markets

Foreign Direct

Investment

FDI WDI

FDI is the net inow of investment from

foreign economies

Urbanization URB WDI

People are living in urban areas: a

proportion of the total population

Renewable Energy RE WDI

The proportion of RE in total energy

consumption

Non-renewable

Energy

NRE WDI

The proportion of NRE in total energy

consumption

Gross Domestic

Product

GDP WDI

Per capita economic growth (constant

US$ 2015)

Notes: WDI, WITS, and IMF stand for World Development Indicators, World

Integrated Trade Solution, and International Monetary Fund, respectively.

A rapid narrative (or approach) means that advanced technologies are

meeting the growing need for energy and new policies are reshaping markets,

business models, and consumer habits to provide social benets and promote a

low-carbon economy.

The gradual approach promotes carbon-intensive initiatives by extending

the traditional strategies and policy practices.

Developing nations confront infrastructure and economic challenges in

decarbonizing their energy systems.

Following the World Bank report, the production of various metals and

minerals including cobalt, lithium, graphite, vanadium, nickel, and indium is

projected to increase up to 585%, 964%, 383%, 173%, 108%, and 241% by

2050, respectively (Hund et al., 2020).

Minerals for sustainable energy technology will acquire from nations with

abundant mineral deposits that have yet to be explored.

It is because the risk associated with mining failures may also reduce the

chance of reaching the operation’s planned sustainable objectives.

M. Irfan et al.

Energy Economics 121 (2023) 106661

Fig. 2. Distributions of critical variables in G-7 and E-7 economies. No data reports the countries outside the sample.

M. Irfan et al.

Energy Economics 121 (2023) 106661

For the development and stability

of an economy, the nancial

sector is crucial. It is indispensable to consider the nancial sector’s role

in environmental degradation. Three key channels may explain the

relationship between energy and nancial development. Firstly, more

nancial development increases foreign direct investment (FDI) and

boosts economic growth (GDP), resulting in an energy consumption

escalation. Secondly, the growth of the nancial sector and the process of

effective nancial intermediation lead to a rise in the consumption of

energy products. Thirdly, capital and nancial market development

expand the investment level that raises energy consumption (Shahbaz

et al., 2018; Zhang, 2011).

Financial support is considered one of the most critical measures for

sustainable development. According to the “nancial structure theory”

and “nancial promotion theory” proposed by Goldsmith and Schum-

peter, respectively, nancial institutions may assist economic develop-

ment (Yang and Ni, 2022). The nexus between green economic

transactions and nancial development further demonstrates the dual

role of the nancial sector. Scale, structural and technological effects

themselves can broaden the economic scale. Financial development may

enhance the economic structure and minimize ecological pollution by

applying manufacturing processes and modernizing equipment with

environmentally friendly production techniques (Sadorsky, 2010).

Contrarily, the expansion of nancing channels with high-energy use,

GHG emissions, and pollution levels has decreased the effectiveness of

green economic development (Boutabba, 2014; Yang and Ni, 2022).

To improve the environmental performance in both developed and

developing countries, this research aims to probe how mineral markets,

nancial development, and FDI play their role in achieving the goals of

sustainable energy consumption. The group of seven (G-7)

comprises

exceptionally advanced economies, and these countries account for 58%

of the worldwide wealth (IMF, 2018). Among the advanced nations, this

is a platform to address nancial and economic concerns since this

forum is not free from challenges. Following ratifying the Paris Climate

Agreement (PCA), most G-7 nations have taken necessary ecological

preservation actions to achieve sustainable economic development.

However, the G-7 economies are responsible for almost 24.58% of GHG

emissions worldwide (Wang et al., 2020; WDI, 2021). The emerging

seven

or E-7 countries comprise the other sample panel for our

research. These economies have the potential to expand and may even

outperform several important developed markets. Therefore, it is crucial

to research E-7 economies to provide obligatory policies and

recommendations.

This study explores the tendency of energy transition from 1990 to

2019 for G-7 and E-7 countries as represented in Fig. 1. Since 1990, the

Table 2

Summary statistics.

Variables N Mean SD Min 1st Quartile Median 3rd Quartile Max

Model 1

ET 210 12.763 9.794 0.674 5.128 8.577 20.047 33.181

MM 210 0.946 0.983 0.208 0.407 0.602 0.892 5.846

FD 210 0.742 0.134 0.345 0.691 0.759 0.858 0.946

FDI 210 10.442 0.693 7.543 10.120 10.526 10.834 11.709

URB 210 77.835 5.526 66.706 75.417 78.202 80.606 91.698

RE 210 9.266 6.575 0.608 4.430 7.355 13.200 22.690

NRE 210 79.009 12.474 46.226 75.197 82.694 86.438 94.633

GDP 210 4.565 0.080 4.439 4.501 4.544 4.624 4.783

Model 2

ET 210 39.953 31.864 3.495 12.976 29.703 71.400 109.670

MM 210 1.258 1.358 0.266 0.576 0.808 1.254 7.363

FD 210 0.406 0.108 0.191 0.327 0.395 0.474 0.657

FDI 210 10.076 0.708 7.867 9.554 10.239 10.563 11.464

URB 210 59.376 19.599 25.547 39.776 67.533 74.587 86.824

RE 210 26.177 17.183 3.180 11.510 24.022 43.620 58.653

NRE 210 76.279 13.919 51.216 62.549 81.404 88.898 93.396

GDP 210 3.631 0.370 2.722 3.313 3.798 3.933 4.079

Model 1 indicates G-7, whereas Model 2 denotes E-7 economies. N and SD stand for no. of observations and standard deviation, respectively.

Table 3

Findings of the CSD test.

Variables Model 1 Model 2

Breusch-Pagan

Pesaran CD Breusch-Pagan

Pesaran CD

ET 152.368*** − 0.757 184.882*** − 0.700

(0.000) (0.449) (0.000) (0.484)

MM 121.773*** 7.294*** 56.984*** − 0.517

(0.000) (0.000) (0.000) (0.605)

FD 127.350*** 13.556*** 60.859*** 3.123***

(0.000) (0.000) (0.000) (0.002)

FDI 26.294 2.412** 41.958*** 2.328**

(0.195) (0.016) (0.004) (0.020)

URB 106.623*** 6.276*** 132.127*** − 1.717*

(0.000) (0.000) (0.000) (0.086)

RE 269.642*** − 1.418 101.609*** 0.285

(0.000) (0.156) (0.000) (0.775)

NRE 228.038*** 1.931* 163.357*** − 2.583***

(0.000) (0.054) (0.000) (0.010)

GDP 270.652*** 3.899*** 95.602*** − 1.372

(0.000) (0.000) (0.000) (0.170)

Notes: Model 1 indicates G-7, whereas Model 2 denotes E-7 economies. *** and

** denote the signicant levels at 1% and 5%, respectively, whilst * claims at the

10% level. P-values are reported in the parenthesis.

Table 4

Findings of SH test.

Statistics Model 1 Model 2

Test value Prob. Test value Prob.

Tilde (Delta) 5.816*** <0.001 9.146*** <0.001

Adjusted tilde (Delta) 7.185*** <0.001 11.299*** <0.001

Notes: *** and ** denote the signicant levels at 1% and 5%, respectively, while

* claims at a 10% level.

Economic and nancial stabilities are two sides of the same coin (Nasir

et al., 2015).

G-7 consists of Canada, France, Germany, Italy, Japan, the United Kingdom

and the United States of America.

Brazil, China, India, Indonesia, Mexico, Russia, and Turkey.

M. Irfan et al.

Energy Economics 121 (2023) 106661

Fig. 3. Binary-relation scatter plots.

M. Irfan et al.

Energy Economics 121 (2023) 106661

energy transition in G-7 countries (orange bars) is gradually increasing

and has almost doubled in the given three decades asserting that the

increased dependence of developed nations on clean and renewable

energy sources. In the case of emerging economies (blue bars), the en-

ergy transition share is steadily decreasing and has become half of its

starting point in 1990. This is because non-renewable energy practices

are fullling more demand for energy in emerging economies.

The current study aims to probe the dynamic determinants of the

energy transition, making several possible contributions to the eld from

numerous angles. First and foremost, energy transition, a somewhat

idiosyncratic element, focuses on cleaner energy input to curb GHG

emissions by concerning low-carbon power generation (Nam and Jin,

2021) following the energy trade perspective (Zhang et al., 2021). The

primary theoretical input of this research states that how much clean

Table 5

Findings of 1st and 2nd generation unit root tests.

Variables IPS ADF CIPS CADF

I(0) I(I) I(0) I(I) I(0) I(I) I(0) I(I)

Model 1

ET 6.769 − 4.854*** 4.782 − 5.292*** − 1.129 − 5.314*** − 0.578 − 3.392***

(1.000) (0.000) (1.000) (0.000) (0.999) (0.000)

MM − 3.094*** − 7.912*** − 3.398*** − 8.616*** − 2.395** − 4.997*** − 2.217 − 4.677***

(0.001) (0.000) (0.000) (0.000) (0.106) (0.000)

FD − 3.370*** − 6.104*** − 3.711*** − 6.657*** − 2.487** − 5.484*** − 2.064 − 3.955***

(0.000) (0.000) (0.000) (0.000) (0.204) (0.000)

FDI − 1.951** − 8.684*** − 2.126** − 9.319*** − 3.359*** − 6.018*** − 2.600** − 4.258***

(0.026) (0.000) (0.017) (0.000) (0.011) (0.000)

URB 1.800 0.376 1.525 0.544 1.113 − 1.849 − 0.305 − 1.135

(0.964) (0.646) (0.936) (0.707) (1.000) (0.956)

RE 6.925 − 5.235*** 4.710 − 5.669*** − 1.369 − 5.149*** − 0.892 − 3.517***

(1.000) (0.000) (1.000) (0.000) (0.991) (0.000)

NRE 1.780 − 6.850*** 1.966 − 7.515*** − 1.421 − 5.172*** − 1.601 − 3.269***

(0.963) (0.000) (0.975) (0.000) (0.668) (0.000)

GDP 1.025 − 6.761*** 1.162 − 7.298*** − 1.425 − 3.686*** − 1.938 − 2.742***

(0.847) (0.000) (0.877) (0.000) (0.314) (0.004)

Model 2

ET − 0.676 − 6.291*** − 0.644 − 6.733*** − 1.707 − 4.930*** − 1.450 − 3.157***

(0.250) (0.000) (0.260) (0.000) (0.801) (0.000)

MM − 3.186*** − 8.053*** − 3.394*** − 8.747*** − 3.170*** − 5.575*** − 2.531** − 4.001***

(0.000) (0.000) (0.000) (0.000) (0.018) (0.000)

FD − 0.774 − 8.091*** − 0.791 − 8.765*** − 2.234* − 5.061*** − 2.335* − 3.971***

(0.220) (0.000) (0.214) (0.000) (0.058) (0.000)

FDI − 2.487*** − 8.114*** − 2.685*** − 8.686*** − 3.114*** − 5.524*** − 2.958*** − 4.075***

(0.007) (0.000) (0.004) (0.000) (0.001) (0.000)

URB 2.940 1.296 2.525 1.278 − 1.513 − 2.348** − 4.234*** − 1.758

(0.998) (0.902) (0.994) (0.899) (0.000) (0.502)

RE 0.656 − 6.744*** 0.768 − 7.246*** − 1.946 − 4.917*** − 1.719 − 3.172***

(0.744) (0.000) (0.779) (0.000) (0.545) (0.000)

NRE − 1.999** − 6.605*** − 2.186** − 7.067*** − 2.109 − 4.874*** − 1.834 − 3.508***

(0.023) (0.000) (0.014) (0.000) (0.420) (0.000)

GDP 2.620 − 4.938*** 2.671 − 5.471*** − 1.895 − 3.381*** − 2.647*** − 2.545**

(0.996) (0.000) (0.996) (0.000) (0.008) (0.016)

Model 1 indicates G-7, whereas Model 2 denotes E-7 nations. *** and ** represent the signicant levels at 1% and 5%, respectively, while * claims at the 10% level. P-

values are shown in the parenthesis.

Table 6

Findings of unit root test with a structural break.

Variables Model 1 Model 2

I(0) NB I(1) NB I(0) NB I(1) NB

ET 2.123 1 − 16.150*** 1 1.188 1 − 17.619*** 1

(1.000) (0.000) (1.000) (0.000)

MM − 0.297 1 − 3.782*** 1 − 0.794 1 − 9.889*** 1

(0.280) (0.000) (0.400) (0.000)

FD − 0.005 1 − 0.103*** 1 0.000 1 − 0.126*** 1

(0.110) (0.000) (0.870) (0.000)

FDI − 4.696** 1 − 24.616*** 1 − 0.314 1 − 8.261*** 1

(0.030) (0.000) (0.220) (0.030)

URB 0.153 1 − 8.563*** 1 0.190 1 − 6.153*** 1

(0.900) (0.000) (0.960) (0.000)

RE 1.742 1 − 13.699*** 1 1.792 1 − 18.745*** 1

(1.000) (0.000) (1.000) (0.000)

NRE − 2.001 1 − 18.110*** 1 0.318 1 − 22.429*** 1

(0.230) (0.000) (0.960) (0.000)

GDP 0.000 1 − 0.004*** 1 0.001 1 − 0.014* 1

(1.000) (0.000) (1.000) (0.090)

Model 1 indicates G-7, whereas Model 2 denotes E-7 nations. NB stands for the number of breaks. *** and ** represent the signicant levels at 1% and 5%, respectively,

while * claims at the 10% level. P-values are shown in the parenthesis.

M. Irfan et al.

Energy Economics 121 (2023) 106661

energy can substitute for dirty energy is worthy of attention. Second, the

dynamic impact of the mineral markets, nancial development, and FDI

on energy transition is investigated using the cross-sectional autore-

gressive distributive lag (CS-ARDL) statistical technique (Chudik and

Pesaran, 2015) to estimate the long and short-run impact of explanatory

variables for the period of 1990–2019. This advanced approach is ef-

cient enough to handle cross-section dependence (CSD), heterogeneous

slope coefcients, endogeneity, and unit root in the series (Khan et al.,

2020). In addition to the dynamic model, augmented mean group (AMG)

and common correlated effect mean group (CCEMG) estimator are

applied to obtain robust results. Third, to contrast developed and devel-

oping economies, this research used the sample of G-7 and E-7 countries

separately to compare the features of both groups and recommend

necessary policy implications. Last, the energy transition becomes

enriched using a pool of control variables: urbanization, clean energy,

non-renewable energy, and economic growth to obtain reliable

outcomes.

The rest of the article is structured as follows. Section 2 reviews and

presents the relevant literature. Section 3 denes the data and in-

troduces the methodology used in this study. Section 4 portrays the

empirical analysis of the effects of minerals and nancial development

on energy transition in developed and developing nations. In the end,

the conclusion and necessary policy implications are shown in Section 5.

2. Literature review and theoretical framework

2.1. Literature review

Energy and GHG emissions have a strong relationship; hence, several

research studies have inspected the inuence of energy transition and

efciency on CO

emissions (Nam and Jin, 2021). Clean energy, which

may be used as a proxy for energy transition, has been included in most

articles that have linked substitution and electrication with energy

transition. This nexus provided empirical evidence and theoretical

background for green development to validate the socio-economic in-

dicators and improve environmental performance (Acheampong et al.,

2023; Wang et al., 2022).

Clean

and dirty

energy are two categories of energy consump-

tion. This classication depends on how much energy usage inuences

the environment’s ecology. While dirty energy hinders green develop-

ment, clean energy encourages it (Ulucak, 2020). The primary contri-

bution to global warming is humans’ usage of dirty energy in their daily

activities of production and habitation (Sarkar et al., 2022). Moreover,

the effects of global warming on social progress and economic growth

result in a 25% decline in global GDP, even though limiting GHG

emissions only costs 1% of the economic growth (Stern, 2007). The ef-

fect of global warming on the economy is greater than the expense of

mitigating it; hence global warming is becoming a cause of slowing

down economic growth (Dogan et al., 2022; Tzeremes et al., 2023).

Therefore, it makes perfect sense to depend less on dirty energy and

increase the use of clean energy that helps to mitigate environmental

pollution (Rahman and Alam, 2021). The application and promotion of

green energy have evolved into the contemporary era’s development

trend in response to the rising demand for sustainable development.

A scarce body of studies estimates the role of renewable and con-

ventional energy and nancial channels on energy transition when

technical circumstances stay the same (Chen et al., 2022; Liu et al.,

2022). As a result, the second novelty of this article is to calculate the

amount of clean energy that can replace dirty energy in both emerging

and developed economies. This study signicantly lowers environ-

mental deterioration since it encourages implementing clean energy to

promote sustainable development and green growth. Panel data analysis

has been used to estimate how much carbon is mitigated by the global

energy transition. The impact of renewable energy on GHG emissions

was disclosed for BRICS and sub-Saharan nations (Danish, 2020). In

contrast, Vo et al. (2020) independently revealed the inuence of nu-

clear, alternative, and renewable energy on carbon reduction. Both

studies concluded that using cleaner energy helps to reduce CO

emis-

sions. Consuming clean energy contributes to sustainable economic

development (Taskın et al., 2020). The necessity of encouraging the use

of clean energy is conrmed by literature demonstrating the link be-

tween renewable energy and green growth. Pao and Fu (2013) discov-

ered how using clean and dirty energy might affect economic growth in

Mexico, Indonesia, South Korea, and Turkey (MIST).

Extended literature argued for the signicance of the phases and

components of mining operations that contribute to the energy transi-

tion. For example, growing consumer demand raises concerns about

metal supply and scarcity. Thus, mining projects must demonstrate their

risk assessment, mitigation, and management capacity. Otherwise, the

energy transition-based minerals supply would be delayed, making the

transition to a low-carbon proposal much more challenging (Islam et al.,

2022; Lebre et al., 2020; Zhu et al., 2022). Research on the mining

projects’ risk management analyzed that mining at large-scale opera-

tions has been ineffective due to underestimating or ignoring the haz-

ards (Irfan et al., 2022a; Xie et al., 2022). For the success of mining

operations, technologies should be applied effectively to increase the

dependability of choices (Irfan et al., 2022b).

Other risk-based quantitative studies undertaken in the mining

sector have focused on various risk factors, including operational, safety,

and water inrush. For instance, Gul et al. (2019) developed an advanced

technique for an underground zinc and copper mines case study. Iphar

and Cukurluoz (2020) presented a fuzzy safety evaluation approach to

improve the risk assessment practice in mechanized coal mines. This was

done to compensate for the deciencies of the traditional decision ma-

trix method’s precise risk score. Financial risks at a gold mine were

evaluated using an expanded TOPSIS technique (Jiskani et al., 2022).

Economic growth and nancial development have signicantly

affected the relationship between the environment and energy (Khan

et al., 2022). The rst category ensures empirical and theoretical sup-

port for nancial development to demonstrate how crucial it is to

encourage economic growth and preserve environmental performance

(Chincarini and Moneta, 2021; Lee and Wang, 2022).

Various studies have observed the effects of nancial development

on environmental deterioration since the Environmental Kuznets Curve

(EKC) hypothesis was proposed (Grossman and Krueger, 1995).

Table 7

Findings of cointegration test.

Estimates Model 1 Model 2

Stat. Prob. Stat. Prob.

Pedroni Co-integration Test

Phillips-Perron t − 3.021*** 0.001 − 1.787** 0.037

Phillips-Perron t (Modied) 2.151** 0.016 3.059*** 0.001

Dickey-Fuller t (Augmented) − 2.754*** 0.003 − 0.660 0.255

Kao Co-integration Test

Dickey-Fuller t 3.443*** <0.001 − 2.718*** 0.003

Dickey-Fuller t (Modied) 2.418*** 0.008 − 2.261** 0.012

Dickey-Fuller t (Augmented) 3.562*** <0.001 − 2.081** 0.019

Unadjusted Dickey-Fuller t 3.595*** <0.001 − 2.541*** 0.006

Unadjusted Dickey-Fuller t

(Modied)

2.469*** 0.007 − 1.753** 0.040

Notes: *** and ** denote the signicant levels at 1% and 5%, respectively, while

* claims at a 10% level.

Clean energy is primarily dened as energy that does not produce waste,

pollution or GHG emissions and thus is not considered bad for the environment

(Garai and Sarkar, 2022).

On the other hand, dirty energy, such as fossil fuel-based, is the energy that

emits signicant amounts of GHG gases, solid and liquid wastes that are

damaging to the atmosphere throughout the consumption process.

M. Irfan et al.

Energy Economics 121 (2023) 106661

According to one school of thought, nancial development has a nega-

tive impact on ecological performance (Ouyang and Li, 2018). Due to

nancial development, nancial institutions provide households and

investors with low-cost borrowing options with fewer constraints,

increasing their need for energy and hence, contributing to GHG

emissions (Charfeddine and Kahia, 2019). Similarly, Khan et al. (2017)

examined how nancial development affects environmental degrada-

tion in 34 upper-middle-income nations. The authors’ empirical analysis

discovered that nancial development adversely affects ecological per-

formance. In contrast, the other school contends that the nancial sector

Fig. 4. Circulars plots.

Table 8

Findings of Westerlund Bootstrap cointegration test.

Estimates Model 1 Model 2

Value Z-value Prob. Robust Prob. Value Z-value Prob. Robust Prob.

− 3.995 − 4.293 <0.001 <0.001 − 2.653 − 0.020 0.492 0.148

− 9.310 1.259 0.896 <0.001 − 0.611 4.610 1.000 1.000

− 8.780 − 2.858 0.002 0.015 − 15.835 − 8.841 0.000 0.000

− 4.130 1.806 0.965 0.405 − 1.164 3.223 0.999 0.850

Notes: *** and ** denote the signicant levels at 1% and 5%, respectively, while * claims at a 10% level.

M. Irfan et al.

Energy Economics 121 (2023) 106661

raises environmental standards. For instance, efcient research and

technology and restrictions on carbon emissions projects lead to green

nance.

Similarly, Shahbaz et al. (2013) claimed that trade openness and

nancial sector growth had decreased environmental damage in Indo-

nesia’s case. The nexus between nancial development and GHG emis-

sions were examined for G-20 nations and found that the nancial sector

reduces GHG emissions. In addition, it is also noted that nancial

development does not have any relationship with environmental quality

(Ozturk and Acaravci (2013). Global economies are adopting green in-

vestment strategies and transferring investments from high to low-

polluting projects to combat climate change (Wang and Zhi, 2016;

Zerbib, 2019). By providing the funding needed for projects with low

emissions levels, nancial instruments like blue and green bonds

may play a signicant role in addressing climate-related challenges

(Mumtaz and Yoshino, 2021; Xu et al., 2020).

Most economies place concerns on economic development, and the

urgent issues related to climate change have sparked researchers’ in-

terest in nding ways to reduce its damaging impacts. Even so, emerging

countries seek to escalate their economic development through various

methods and processes. One of the most appealing techniques is FDI,

which is a signicant source of outside investment since it may boost

economic growth by expanding production. Moreover, it might result in

the transfer of modern technology and assistance as well as the creation

of employment. More in-depth analyses of the FDI phenomenon and its

effects on the environment have recently been conducted in academic

studies.

2.2. Theoretical framework

The process of energy transition covers three facets: energy inde-

pendence, reducing adverse environmental effects, growing the indus-

trial and service sectors to accomplish sustainable environmental

objectives (Lantz et al., 2021), and transferring the energy sector to a

clean and sustainable one (De La Pena et al., 2022). The energy transi-

tion indicator is adjusted for FDI and nancial development. The

Pollution Halo

and the Pollution Haven

hypotheses capture the lit-

erature’s attention by considering the environmental effects of FDI. Most

research (see Huang et al., 2019; Singhania and Saini, 2021; Zafar et al.,

2020) afrms the Pollution Haven in developing nations and claims that

FDI raises GHG emissions by transferring the contaminating activities to

the host countries. FDI can curtail GHG emissions via energy-efcient

technologies and boost economic development (Caetano et al., 2022).

Based on the theoretical notion of the energy trade model, nanci-

alization and FDI further lead to trade activities affecting cleaner energy

consumption through three distinct aspects: scale, technical and

composition effects. The scale effect pertains to escalating the produc-

tion level in an economy. For instance, a rise in the production process

demands more raw materials and energy, further leading to nanciali-

zation and increasing environmental contamination by shifting the

Table 9

Findings of CS-ARDL test.

Variables Model 1 Model 2 Variables Model 1 Model 2

Coef. St. Errors Coef. St. Errors Coef. St. Errors Coef. St. Errors

Long Run Estimates Short Run Estimates

MM 0.356*** 0.072 0.020 0.129 ΔMM 0.352*** 0.071 0.022 0.132

(0.000) (0.879) (0.000) (0.869)

FD − 0.491** 0.244 − 1.472 1.862 ΔFD − 0.516** 0.249 − 1.503 2.012

(0.045) (0.429) (0.038) (0.455)

FDI 0.005 0.043 − 0.200 0.193 ΔFDI 0.002 0.042 − 0.208 0.199

(0.911) (0.300) (0.968) (0.296)

RE 1.297*** 0.109 1.281*** 0.103 ΔRE 1.290*** 0.103 1.324*** 0.105

(0.000) (0.000) (0.000) (0.000)

NRE − 0.185*** 0.051 − 0.636*** 0.183 ΔNRE − 0.180*** 0.049 − 0.670*** 0.194

(0.000) (0.001) (0.000) (0.001)

URB 0.426* 0.219 0.521* 0.288 ΔURB 0.403** 0.203 0.554* 0.302

(0.052) (0.071) (0.047) (0.066)

GDP 0.026 1.472 − 0.143 3.556 ΔGDP − 0.138 1.447 − 0.346 3.716

(0.986) (0.968) (0.924) (0.926)

F-Stat 30.280 47.340

p-value 0.005 0.071

N 196 196

Model 1 indicates G-7, whereas Model 2 denotes E-7 economies. *** and ** denote the signicant levels at 1% and 5%, respectively, while * claims at the 10% level. P-

values are reported in parentheses.

Table 10

Findings of robustness tests.

Variables Model 1 Model 2

AMG CCEMG AMG CCEMG

MM 0.154** 0.662** 0.082* 0.078*

(0.061) (0.286) (0.048) (0.041)

FD − 0.499* − 0.491 0.928** 3.202**

(0.293) (0.397) (0.474) (1.315)

FDI 0.002 0.038 − 0.027 0.457*

(0.016) (0.034) (0.058) (0.240)

REC 1.201*** 1.270*** 1.230*** 1.266***

(0.045) (0.046) (0.120) (0.034)

NREC − 0.163*** − 0.116*** − 0.675*** − 0.444***

(0.052) (0.032) (0.260) (0.043)

URB 0.027 − 0.103 − 0.016 1.107***

(0.080) (0.213) (0.023) (0.348)

GDP − 0.254* 3.290 − 1.161 − 9.550**

(0.139) (2.275) (1.138) (4.694)

Const. 10.101 41.567** 39.515* − 256.535***

(9.390) (18.458) (22.514) (74.976)

Wald-Test 727.760 7418.140 120.280 43.650

RMSE 0.052 0.027 0.256 0.107

p-vale <0.001 <0.001 <0.001 <0.001

N 210 210 210 210

Model 1 indicates G-7, whereas Model 2 denotes E-7 economies. *** and **

denote the signicant levels at 1% and 5%, respectively, while * claims at the

10% level. P-values are reported in parentheses.

According to the Pollution Halo theory, FDI transfers effective and envi-

ronmentally friendly technologies that lower degradation primarily by

consuming less energy (Aust et al., 2020).

In the Pollution Haven theory, on the other hand, economies with strict

environmental policies shift their polluting industries to nations with less

stringent environmental regulations.

M. Irfan et al.

Energy Economics 121 (2023) 106661

country to the industrial level (Zhang et al., 2021). The technical effect

helps to adopt innovation and advanced technologies to improve ef-

cient production and energy themes. In the end, the composition effect

presents a change in the economic structure mix: Shifting to the services

section (less polluting sector).

While the continuing growth in nancialization and economic ac-

tivities, it has become progressively challenging to hold the proportions

of these inuencing three effects constant. In the case of economic

growth, GDP is a measure of economic health and comprises several

economic components, including investment, production, consumption,

government spending, and FDI. Since a signicant part of economic

growth involves energy consumption and this rising consumption is

directly associated with energy transition. Energy transition fosters the

inclusive inputs to mitigate GHG emissions that have been validated

(Nam and Jin, 2021) following the energy trade perspective (Zhang

et al., 2021), Pollution Halo and Haven hypothesis (Caetano et al.,

2022). Hence, this study covers the minerals and urbanization along

with nancialization, FDI and GDP effect on energy transition in

developed and emerging economies, which needs further research.

3. Materials and methods

3.1. Data

This research investigates the dynamic association between mineral

markets, nancial development, FDI, urbanization, renewable energy,

non-renewable energy, and economic growth with energy transition in

14 economies from 1990 to 2019. The time span depends upon the data

availability of study variables. The sample is further bifurcated into

models: Model 1 and 2, illustrating the G-7 and E-7 economies,

respectively, to compare the empirical outcomes. In this part, this

research elucidates the nature of the study variables and their potential

relationship with the dependent variable. Supplementary material is

provided in Appendix A.

Energy transition (the share of clean energy in the TPES: total pri-

mary energy supply) (Nam and Jin, 2021) is retrieved from World Bank

(WDI). The data on the mineral markets (minerals export trading share,

expected direction

∂

> 0) (Jiskani et al., 2021) is collected from World

Integrated Trade Solution (WITS), whilst nancial development data

(nancial access, depth, and efciency of nancial markets and in-

stitutions, expected direction

∂

< 0) (Baloch et al., 2021) is gathered

from International Monitoring Fund (IMF). The IMF’s nancial devel-

opment ranges from 0 to 1. This dataset provides a multi-dimensional

measure and broader coverage for nancial sector development using

eight different indicators. More so, the data of FDI (net inow of in-

vestments, expected direction

∂

FDI

> 0) (Caetano et al., 2022), urbani-

zation (population in urban areas, expected direction

∂

URB

> 0) (Yao and

Tang, 2021), renewable energy (share of total energy, expected direc-

tion

∂

> 0), non-renewable energy (share of fossil fuels-based energy,

expected direction

∂

NRE

< 0) and economic growth (per capita GDP,

expected direction

∂

GDP

> 0) is aggrandized from WDI, as reported in

Table 1. The table further claries the variable’s name, symbol, source,

and measurement unit. The FDI and GDP are transformed into loga-

rithmic forms to present better outcomes.

To assess the short and long-run outcomes, this study employs a

cross-sectional ARDL technique (Chudik and Pesaran, 2015). The model

of this study is presented below.

FDI

URB

NRE

GDP

(3.1)

shows the slope,

are coefcients of explanatory variables,

whilst

denotes the residuals. i indicates the time period (from 1990 to

2019), whereas t reports the cross-sections (14 developed and emerging

countries). This research illustrates the distributions of the energy

transition, mineral markets, nancial development, FDI, and renewable

energy for G-7 and E-7 economies in 1990 and 2019 in Fig. 2. Economies

are allocated into 07 levels, where bright color directs higher magnitude

whereas light color denotes a lower value of the relevant indicator. In

the given three decades, every developed or developing country has

been changed blatantly, as conspicuous in the gure.

3.2. Econometric modeling

3.2.1. CSD and slope heterogeneity tests

This study estimates the CSD

and SH coefcient tests for all the

variables. Traditional methods have ignored these basic preliminary

tests, and their absence may lead to inconsistent estimates (Li et al.,

2020; Ulucak and Khan, 2020). For instance, in the presence of het-

erogeneity, the SH test is efcient enough to handle the homogenous

coefcient’ assumptions (Baltagi and Pesaran, 2007). The general for-

mula of this test is shown below:

= (V)

(2h)

−

(

N − h

)

(3.2)

ASH

= (V)

(

2h(S − h − 1

S + 1

)

−

(

N − 2h

)

(3.3)

where Δ

and Δ

ASH

report slope coefcient’ homogeneity in delta SH

and delta SH (adjusted), respectively.

3.2.2. Unit root test

This research has employed both 1st generation: Im, Pesaran, and

Shin (IPS) (Im et al., 2003) and Augmented Dickey-Fuller (ADF) and 2nd

generation: cross-sectional ADF (Pesaran, 2003) and cross-sectional IPS

(Pesaran, 2007) unit root tests. The 2nd generation unit root tests are

robust to CSD and Slope Heterogeneity (SH) coefcients. The general

formula of the unit root test is indicated as follows:

ΔT

= δ

+ δ

it− 1

+ δ

t− 1

∑

h=0

ΔT

t− 1

∑

h=1

ΔT

it− h

(3.4)

The equation mentioned above shows the difference and lag values

as Δ

t− 1

and T

t− 1

, respectively. Hence, the statistics of the cross-

sectional IPS unit root test are reported below:

CIPS = 1

∑

i=1

CADF

(3.5)

In Eq. (3.5), CADF denotes cross-sectional ADF as shown in Eq. (3.4)

and H

state the non-stationarity of the data.

3.2.3. Cointegration test

To estimate the long-run cointegration among study variables, this

study employed both 1st generation: Kao (Kao, 1999) and Pedroni

(Pedroni, 2004) and 2nd generation: Westerlund bootstrap (Westerlund,

2007)

based on error correction (EC), cointegration tests. The general

form of cointegration test is as follows:

= δ/SE(δ) (3.6)

= t(δ) (3.7)

= 1

∑

i− 1

SE(δ

)

(3.8)

This test is capable of handling the shocks in developed and emerging

economies (Pesaran, 2004).

Compared with 1st generation cointegration tests such as Kao and Pedro-

ni’s, the Westerlund bootstrap test is robust with error coefcients of SH (Khan

et al., 2020).

M. Irfan et al.

Energy Economics 121 (2023) 106661

= 1

∑

i− 1

Sδ

(1)

(3.9)

Eqs. (3.6) and (3.7) of P

and P

explore panel while Eqs. (3.8) and

(3.9) of G

and G

illustrate group means statistics, where H

is of no

cointegration.

3.2.4. CS-ARDL modeling

This study applies cross-sectional ARDL for long and short-run esti-

mations (Chudik and Pesaran, 2015). This approach is more robust to

endogeneity, CSD, SH coefcients, unobserved common factors

, and

non-stationarity

(Danish, 2019; Khan et al., 2020). The universal form

of CS-ARDL is presented below:

∑

r=1

it− r

∑

r=0

t− r

∑

r=0

t− r

(3.10)

where V

= (ΔET

, P

’)’ and P

= (MM

, FM

, FDI

, URB

, RE

, NRE

GDP

)’, P is the pool of explanatory variables, for instance, minerals,

nancial development, FDI, urbanization, clean energy, non-renewable

energy, and GDP.

4. Results and discussion

Table 2 reports the summary statistics of the study variables for both

models. It is shown that the mean of energy transition and minerals is

higher in E-7 countries; however, the volatility of these variables is

higher in developing countries. Table 2 reports that nancial develop-

ment, FDI, and urbanization have more mean magnitude in Model 1,

indicating advanced nancial management. The statistics further indi-

cate that the mean of cleaner and traditional energy is more favorable in

developing nations indicating that they are focusing on sustainable

economic development. In the end, economic growth presents more

obvious in Model 1. These descriptive statistics assert an idea about the

characteristics of both models.

Moreover, to examine the cross-sections of the study, CSD is esti-

mated using Breusch-Pagan and Pesaran CD CSD tests. The outcomes of

Table 3 report that CSD exists among the study variables by rejecting the

of no CSD, which infers the interconnection among G-7 and E-7

economies illustrating global economic spillover effects, regional con-

nectivity, and globalization (Hasanov et al., 2021). The ndings of slope

homogeneity (SH) are interpreted in Table 4, which elaborates the null

hypothesis (H

= SH exists among the series). The results of the test

conrm that heterogeneity exists among cross-section slope coefcients.

Fig. 3 describes the correlational plots for both models of this study.

These two-way graphs indicate the binary relationship among all the

study variables from 1990 to 2019.

The authors employed 1st and 2nd generations unit root tests to

probe the integration properties of the data. The four-panel unit root

tests’ highlights, which are employed in this article and aim to inspect

the integration order of the series, are reported in Table 5. In the pres-

ence of heterogeneity and CSD, 2nd generation non-stationarity tests

such as CIPS and CADF are endorsed (Fareed et al., 2022). Indeed, the

recorded ndings conrm that most variables have a unit root at the

level (by accepting the H

of stationarity). Hence, the study variables are

stationary at the 1st difference I (1), which validates that the study

variables are integrated of order one. In the current situation, the

applicable onward move is to estimate the enduring association among

study variables. This study employed Karavias and Tzavalis (2014)

panel unit root test to observe the structural break. Table 6 reports that

variables of both Model 1 and 2 are stationary at the rst difference I (1)

with one structural break in the series. Therefore, this research applies

Kao and Pedroni cointegration tests as in Table 7. The results of these

tests validate the presence of long-term connections in the hypothesized

variables by rejecting the H

of no cointegration.

To overcome the issues of CSD and slope heterogeneity, this study

further applies the 2nd generation panel cointegration test, for instance,

bootstrap Westerlund as recorded in Table 8. Following the ndings of

the Westerlund test, both panel (P

and P

) and group (G

and G

) mean

statistics, cointegration is recommended by rejecting the H

of no

cointegration. Consequently, the panel data variables are signicantly

interlinked. With this robust cointegration, two conditions: connection

is not spurious, and coefcients are valuable for estimations are met.

Furthermore, to overview the study, variables including energy transi-

tion, minerals, nancial development, FDI, urbanization, and renewable

energy are explored by circular plots, as shown in Fig. 4. These unique

plots present a comprehensive picture of key variables as shown in the

legend of the graphs. The dark color of the variables represents more

magnitude.

The outcomes of Model 1 in Table 9 explain the results, which infer

that the minerals are positively signicant with the coefcients of 0.352

and 0.356; it represents that a 1% surge in minerals can cause a 0.35%

increase in the energy transition in both the short and long run. These

results reveal that mineral resources contribute to the sustainable

objective in developed nations. These outcomes are supported by the

extant studies (Jiskani et al., 2022; Lebre et al., 2020; Ulucak, 2020).

The ndings of Model 2 state that MM also has a direct connection with

energy transition but with a lower magnitude indicating inadequate

intentions of emerging nations toward mineral use to meet the demands

of clean energy. Traditional energy dependence seems to impede the

energy transition. Though this appears to be an undesirable outcome, it

is indispensable to mention that the energy transition concerns the

amount of energy produced. Fareed et al. (2022) argued that this in-

uence might be expounded by the impact of advantage toward clean

energy production. They suggests that renewable energy could benet

the economies to accomplish Agenda 2030, provided the energy tran-

sition is a sustainable opportunity. The ndings of the study testify to the

crucial role played by energy transition; indeed, given the statistic that

clean energy considerably refers to the addition in sustainable growth.

In the case of nancial development, short and long-run coefcients

are signicant and negative, i.e., − 0.516 and − 0.491. It concludes that

nancial development in advanced economies desperately depends on

energy utilization and is less concerned about environmental goals.

However, Table 9 inspects that the negative inuence of nancial

development is more dominant in emerging countries than in advanced

ones indicating that developing nations ignore sustained commitments

and are more anxious about energy consumption, even at the expense of

atmospheric pollution. These outcomes are aligned with the current

studies (Acheampong et al., 2020; Xu et al., 2022). Financial develop-

ment seems to be a barrier to the energy transition. Though this seems an

undesirable outcome, it is essential to consider that economic growth

leads to the energy transition. However, when only considering nancial

development to the energy sector, ceteris paribus, FD reduces environ-

mental pollution in the long run and becomes favorable to the advanced

nations.

Meanwhile, FDI has a positive and negative relationship with energy

transition for Models 1 and 2. Although the results of FDI are insigni-

cant: however, the positive magnitude denotes that FDI in developed

economies supports environmental quality following the Pollution Halo

effect, and these outcomes are supported by the current literature

(Caetano et al., 2022; Hao et al., 2020). Contrarily, the Pollution Haven

affects developing countries to bear a negative connection of FDI with

energy transition in the long and short run. Foreign direct investment

increases energy transition in developed nations elucidating that these

economies are more concerned about sustainable growth. Specically,

in the long run, the higher coefcient of FDI indicates that advanced

economies are following the objectives of Agenda 2030 by promoting

clean and efcient energy generation. Lastly, renewable energy

Ignoring unobserved common factors can lead to biased estimations.

Able to deal with stationarity with varied difference levels.

M. Irfan et al.

Energy Economics 121 (2023) 106661

consumption has a signicant and positive relation with energy transi-

tion. These outcomes are supported by the extant literature (Vo et al.,

2020). Opposite to RE, non-renewable energy’s coefcients are docu-

mented to be negatively signicant in the long and short run for both the

study models. Empirical outcomes of CS-ARDL describe that the

magnitude of NRE is more rigorous in emerging nations than in devel-

oped countries.

Moreover, urbanization positively inuences energy transition in

both the long and short run for developed and developing economies.

The coefcients of urbanization claim that in the case of the long run, 1

unit rise in URBs increases energy transition by 0.426 and 0.521 units in

Models 1 and 2, respectively. The current studies further support these

consequences (Lantz et al., 2021). Ultimately, economic growth explains

its positive and negative connection with Models 1 and 2. In developed

economies, GDP emphasizes green energy and technological innovation

to impart its role in improving ecological quality, whilst emerging na-

tions, in the same scenario, are not aware and responsive to sustainable

growth to curtail the pollution level to accomplish the carbon neutrality

targets. The study outcomes are also tested for robustness and re-

conrmation by employing AMG (Eberhardt, 2012) and CCEMG

(Pesaran, 2007) estimations, as reported in Table 10.

5. Conclusions and policy implications

The Conference of Parties’ (COP-26) objectives, considering the

cleaner energy and environmental challenges, provide signicant pres-

sure to expedite the shift to the energy transition. This research aims to

scrutinize the dynamic impact of mineral markets and nancial devel-

opment along with a pool of auxiliary variables: FDI, urbanization, clean

energy, non-renewable energy, and GDP on energy transition from 1990

to 2019. To forecast the characteristics of both developed and emerging

countries, the sample is further bi-furcated in Models 1 and 2,

respectively.

With the massive expansion of minerals mining worldwide, minerals-

intensive countries have faced a mess regarding minerals extraction,

supply, and commercial dealings. The authors of this study have

employed CS-ARDL to estimate the nancialization of minerals and

energy nexus. This advanced approach is efcient enough to deal with

CSD, endogeneity, non-stationarity, and heterogeneous slope co-

efcients (Khan et al., 2020). The empirical ndings of this study esti-

mate that minerals signicantly contribute to the energy transition

process for both models to accomplish low-carbon power generation. In

the case of nancial development, it encourages the negative direction

with energy transition in the long- and short-run. FDI, renewable energy,

and urbanization support sustainable goals, while non-renewable en-

ergy is negatively related to the energy transition. Developed economies

draw such economic and environmental policies that follow sustainable

development goals. Statistics discover that the supply and demand for

minerals have signicantly increased due to low carbon-intensive goals

and a rising share of renewable energy for sustainable energy genera-

tion. These outcomes are robust with the AMG and CCEMG statistical

techniques.

The following policy implications and recommendations are pro-

vided to empower the decision-making process for all the energy tran-

sition stakeholders, as they are sympathetic to escalating the

decarbonization of the energy sector in both developed and developing

countries. The energy mix must immediately reduce its carbon con-

centration to attain the carbon neutrality objective. According to the

analysis, focusing on traditional fuels such as coal and oil mitigates the

likelihood of attaining a climate-stabilized scenario on time. Establish-

ing a Carbon Neutral objective by 2050 and developing short- and long-

run policies must be the next step in the broader social perspective and

transforming energy sector priorities on decarbonization (De La Pena

et al., 2022). Eliminating coal-red power plants is another crucial move

in the right way in emerging economies. In the context of ongoing en-

ergy security concerns, natural gas would be the preferred energy

source. However, deployment and investment in cleaner energy must

continue in the future. The government should promote the use of

cleaner sources rather than restrict them. Investing in RE in developed

nations today results in a possible replacement of fossil fuel revenues.

Additionally, increased investment in energy storage might lower the

carbon intensity while addressing intermittency challenges. The mining

sector relies on and prioritizes risks for sustainable mining operations.

Policymakers and mining sustainability focus groups should consider

formulating climate-smart and green mining policies and their pro-

spective repercussions (Jiskani et al., 2022). This paper evaluates the

hazards that must be addressed in order of importance for socially

acceptable and sustainable mining methods to provide green and

cleaner energy. The environmental deterioration is jeopardy to mineral

resources, leading to the threat of lessening natural resources. The

resource conservation agenda can assist the cleaner production tech-

niques and the latest technology to thwart enormous losses. In addition,

it receives benets from nancial services and economic insurance to

reinstate the natural resource protection strategy. More so, the nancial

development of G-7 and E-7 economies restricts green development ef-

ciency. Therefore, three elements of nancial development should be

examined while evaluating nancial reform (nancial deepening,

nancial efciency, and nancial size) (Yang and Ni, 2022). In devel-

oping their nancial size, economies should optimize the nancial re-

sources’ allocation and guide the ow of funds to pollution-free and low-

emission rms. The industrial orientation of green nance development

should be taken into account, with policies of nancial assistance for the

expansion of low-carbon rms and risk sharing for the technical inno-

vation of clean energy.

Lastly, governments should invest in cleaner energy sources for the

national grid. Specialization of the energy sector is essential because the

ensuing economies of scale and efciency would cut marginal costs, and

consequently energy prices, and increase FDI. It is vital to avoid

importing dirty energy; thus, governments should promote backup from

clean energy sources, especially those with accumulation potential, such

as hydro. As this study focuses on the G-7 and E-7 economies and sug-

gests policy instruments for cleaner energy, minerals, and nancial

development, the policy agenda might appear inconclusive. Indeed, the

suggested policy framework may have been more multifaceted due to

the G7 nations’ other growth-related considerations. Although broad-

ening the topic’s scope may have included more growth drivers, the

parameters were selected within the theoretical limitations of the study

challenges. Though the policy framework can be organized by consid-

ering the other contexts of advanced and emerging countries, which may

demand a policy revamp to address the environmental deterioration

challenges, there lies the generalizability of the study’s recommended

policy outline. To offer policy recommendations from a precise

perspective, further research might benet from examining the

comparative situation by considering the micro or sectoral-level

research.

CRediT authorship contribution statement

Muhammad Irfan: Resources, Conceptualization, Data curation,

Validation, Formal analysis, Writing – original draft, Writing – review &

editing. Mubeen Abdur Rehman: Writing – original draft, Writing –

review & editing, Investigation, Visualization. Asif Razzaq: Conceptu-

alization, Methodology, Writing – original draft, Writing – review &

editing. Yu Hao: Conceptualization, Methodology, Funding acquisition,

Supervision, Writing – original draft, Writing – review & editing.

Acknowledgments

The authors acknowledge sponsorship from Science and Technology

Program of Zhejiang Province of China (2022C35060), The Technology

Innovation Program of Beijing Institute of Technology (2022CX01013),

and the Joint Development Program of the Beijing Municipal

M. Irfan et al.

Energy Economics 121 (2023) 106661

Commission of Education. The authors are also very grateful to the

anonymous reviewers and Editor-in-Chief Prof. Dr. Richard S.J. Tol for

their insightful comments that helped us sufciently improve the quality

of this paper. The usual disclaimer applies.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.

org/10.1016/j.eneco.2023.106661.

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Energy Economics 121 (2023) 106661
Contents lists available at ScienceDirect Energy Economics
journal homepage: www.elsevier.com/locate/eneeco
What derives renewable energy transition in G-7 and E-7 countries? The
role of financial development and mineral markets
Muhammad Irfan a,b, Mubeen Abdur Rehman c, Asif Razzaq d, Yu Hao e,f,g,h,i,*
a School of Economics, Beijing Technology and Business University, Beijing 100048, China
b Department of Business Administration, Ilma University, Karachi 75190, Pakistan
c Lahore Business School, University of Lahore, Lahore, Pakistan
d Research Division, CAREC Institute, Urumqi 830000, China
e School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
f Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China
g Beijing Key Lab of Energy Economics and Environmental Management, Beijing 100081, China
h Sustainable Development Research Institute for Economy and Society of Beijing, Beijing 100081, China
i Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314001, China A R T I C L E I N F O A B S T R A C T Keywords:
The enduring impact of the synergy between environmental sustainability and natural resources varies across Energy transition
countries depending on the economic structure. Though the transboundary effect of mineral resources and Mineral markets
cleaner energy underlines the significance of environmental performance, this research aims to scrutinize this Financial development
inclusive theme by investigating the dynamic effects of mineral markets, and financialization on energy tran- Clean energy Sustainability
sition in selected developed and developing countries from 1990 to 2019. The cross-sectional autoregressive
distributive lags (CS-ARDL) model is applied to address slope heterogeneity and cross-sectional dependency. The
empirical findings reveal that mineral markets significantly contribute to the energy transition process in
accomplishing low-carbon power generation in G-7 countries. In contrast, financial development negatively
influences energy transition; however, it has an insignificant effect on the E-7 countries. The study outcomes are
verified by employing the augmented mean group (AMG) and common correlated effect mean group (CCEMG)
estimators. These findings provide valuable policy recommendations for all stakeholders to drive the transition of
renewable energy toward low carbon and green growth. 1. Introduction
commonly accepted as the principal aspects of this framework since they
can curtail greenhouse gas (GHG) emissions resulting from fossil fuel
Currently, the challenge of reforming world’s energy system is a
consumption (Al Mamun et al., 2018). However, this subject must be
prevailing subject for policymakers (Loorbach et al., 2017). Energy
addressed with proper energy planning to proceed with the energy
transition1 is a process that aims to produce sustainable energy, driven
transition process. As a result, rather than studying energy transition-
by three overarching motives: reducing adverse environmental effects,
related concerns at a national level, a growing body of literature has
increasing energy independence, and growing the industrial and service
done so locally (Suitner and Ecker, 2020). This implies that effective
sectors to support sustainable economic development (Aldieri et al.,
energy transition planning should include the specific aspects of coun-
2019; Lantz et al., 2021). Renewable energy and energy efficiency are tries (Lantz et al., 2021).
* Corresponding author at: School of Management and Economics, Beijing Institute of Technology, 5 Zhongguancun South Street, Haidian District, Beijing 100081, China.
E-mail addresses: irfansahr@bit.edu.cn (M. Irfan), mubeenurehman@gmail.com (M.A. Rehman), asifrazzaq@yahoo.com (A. Razzaq), haoyuking@bit.edu.cn, haoyuking@gmail.com (Y. Hao).
1 It is imperative to transfer the energy sectors from fossil fuels to clean energy; this process is referred to as the “energy transition” (Bond, 2018; De La Pena et al., 2022).
https://doi.org/10.1016/j.eneco.2023.106661
Received 14 September 2022; Received in revised form 7 March 2023; Accepted 2 April 2023 Available online 11 April 2023
0140-9883/© 2023 Published by Elsevier B.V. M. Irfan et Energy Economics al. 121 (2023) 106661
Fig. 1. Energy transition situation between 1990 and 2019.
exacerbate the problem for countries already struggling to survive, both Table 1
in advanced and emerging,4 economists tend to prefer economic and Variables’ description.
security concerns above environmental preservation (Komendantova, Variable name Symbol Source Measurement unit
2021). Stakeholders can only develop solutions for a successful energy
transition by analyzing the distinct barriers and demands encountered in Energy Transition ET WDI
Clean energy concerning fossil fuel- based energy consumption.
each country separately (De La Pena et al., 2022; Relva et al., 2021). Mineral markets MM WITS
Share of export products in percentage
The mining sector is a significant contributor to the global economy Financial
and is one of the most resource-intensive businesses.5 Due to their vital Development FD IMF
The access, depth, and efficiency of
financial institutions and markets Foreign Direct
role in renewable energy transmission, metals, and minerals are Investment FDI WDI
FDI is the net inflow of investment from foreign economies
increasing in value, and the rising demand for clean energy technology Urbanization URB WDI
People are living in urban areas: a
(Jiskani et al., 2022). A World Bank report claims that over 3 billion
proportion of the total population
tonnes of minerals6 will be needed to install geothermal power, solar, Renewable Energy RE WDI
The proportion of RE in total energy consumption
wind, and energy storage will be required to meet the target of the Paris Non-renewable
Agreement to limit global warming to <2 degrees Celsius. More mining Energy NRE WDI
The proportion of NRE in total energy consumption
will be required to address the environmental challenges in a climate- Gross Domestic
smart and sustainable manner due to the worldwide shift to low- Product GDP WDI
Per capita economic growth (constant US$ 2015)
carbon technologies that primarily rely on minerals (Jiskani et al.,
Notes: WDI, WITS, and IMF stand for World Development Indicators, World 2021).
Integrated Trade Solution, and International Monetary Fund, respectively.
It is well acknowledged that the increasing demand for mineral re-
sources increases the production volume and project complexity
The two most prevalent narratives in the literature deal with rapid2
(Dubinski, 2013), resulting in decision-making difficulties (Chinbat and
and gradual3 energy transitions. Some scholars claim that it is irrational
Takakuwa, 2009). An intense mining process increases resource con-
to believe the energy transition has become boosted by the intermittent
sumption and operational hazards. Hence, it leads to the danger of
nature and slow development of cleaner energy sources because of the
interaction between elements of sustainable mining and the mining
expensive infrastructure associated and historical policies of gradual
system, such as the community, environment, economy, safety, and
global transformations (Smil, 2016). Others argue that despite the
productivity. The impact of risks7 impeding project objectives exerts an
traditional evidence favoring conventional and gradual views, there is
incredible strain on the project’s overall performance. In the first place,
sufficient evidence to suggest that they can happen promptly in specific
mining operations promote a greener procedure and contribute to
circumstances, particularly when climate change, scarcity, and techno-
cleaner energy generation. To accomplish cleaner energy targets based
logical innovations are becoming accelerating factors (De La Pena et al.,
on activities of minerals mining, it is indispensable to conduct a risk 2022: Sovacool, 2016).
assessment to mitigate, avoid and control the risk management strate-
The energy transition initiatives bring several challenges, for gies (Amoatey et al., 2017).
instance, social, economic, and technical concerns (Papadis and Tsat-
saronis, 2020). The energy transition is experienced at varying rates in
different economies. When constraints like the Covid-19 epidemic
4 Developing nations confront infrastructure and economic challenges in
decarbonizing their energy systems.
5 Following the World Bank report, the production of various metals and
minerals including cobalt, lithium, graphite, vanadium, nickel, and indium is
2 A rapid narrative (or approach) means that advanced technologies are
projected to increase up to 585%, 964%, 383%, 173%, 108%, and 241% by
meeting the growing need for energy and new policies are reshaping markets,
2050, respectively (Hund et al., 2020).
business models, and consumer habits to provide social benefits and promote a
6 Minerals for sustainable energy technology will acquire from nations with low-carbon economy.
abundant mineral deposits that have yet to be explored.
3 The gradual approach promotes carbon-intensive initiatives by extending
7 It is because the risk associated with mining failures may also reduce the
the traditional strategies and policy practices.
chance of reaching the operation’s planned sustainable objectives. 2 M. Irfan et Energy Economics al. 121 (2023) 106661
Fig. 2. Distributions of critical variables in G-7 and E-7 economies. No data reports the countries outside the sample. 3 M. Irfan et Energy Economics al. 121 (2023) 106661 Table 2 Summary statistics. Variables N Mean SD Min 1st Quartile Median 3rd Quartile Max Model 1 ET 210 12.763 9.794 0.674 5.128 8.577 20.047 33.181 MM 210 0.946 0.983 0.208 0.407 0.602 0.892 5.846 FD 210 0.742 0.134 0.345 0.691 0.759 0.858 0.946 FDI 210 10.442 0.693 7.543 10.120 10.526 10.834 11.709 URB 210 77.835 5.526 66.706 75.417 78.202 80.606 91.698 RE 210 9.266 6.575 0.608 4.430 7.355 13.200 22.690 NRE 210 79.009 12.474 46.226 75.197 82.694 86.438 94.633 GDP 210 4.565 0.080 4.439 4.501 4.544 4.624 4.783 Model 2 ET 210 39.953 31.864 3.495 12.976 29.703 71.400 109.670 MM 210 1.258 1.358 0.266 0.576 0.808 1.254 7.363 FD 210 0.406 0.108 0.191 0.327 0.395 0.474 0.657 FDI 210 10.076 0.708 7.867 9.554 10.239 10.563 11.464 URB 210 59.376 19.599 25.547 39.776 67.533 74.587 86.824 RE 210 26.177 17.183 3.180 11.510 24.022 43.620 58.653 NRE 210 76.279 13.919 51.216 62.549 81.404 88.898 93.396 GDP 210 3.631 0.370 2.722 3.313 3.798 3.933 4.079
Model 1 indicates G-7, whereas Model 2 denotes E-7 economies. N and SD stand for no. of observations and standard deviation, respectively.
boosts economic growth (GDP), resulting in an energy consumption Table 3
escalation. Secondly, the growth of the financial sector and the process of Findings of the CSD test.
effective financial intermediation lead to a rise in the consumption of Variables Model 1 Model 2
energy products. Thirdly, capital and financial market development Breusch-Pagan Pesaran CD Breusch-Pagan Pesaran CD
expand the investment level that raises energy consumption (Shahbaz LM LM et al., 2018; Zhang, 2011). ET 152.368*** − 0.757 184.882*** − 0.700
Financial support is considered one of the most critical measures for (0.000) (0.449) (0.000) (0.484)
sustainable development. According to the “financial structure theory” MM 121.773*** 7.294*** 56.984*** − 0.517
and “financial promotion theory” proposed by Goldsmith and Schum- (0.000) (0.000) (0.000) (0.605)
peter, respectively, financial institutions may assist economic develop- FD 127.350*** 13.556*** 60.859*** 3.123***
ment (Yang and Ni, 2022). The nexus between green economic (0.000) (0.000) (0.000) (0.002) FDI 26.294 2.412** 41.958*** 2.328**
transactions and financial development further demonstrates the dual (0.195) (0.016) (0.004) (0.020)
role of the financial sector. Scale, structural and technological effects URB 106.623*** 6.276*** 132.127*** − 1.717*
themselves can broaden the economic scale. Financial development may (0.000) (0.000) (0.000) (0.086)
enhance the economic structure and minimize ecological pollution by RE 269.642*** − 1.418 101.609*** 0.285 (0.000) (0.156) (0.000) (0.775)
applying manufacturing processes and modernizing equipment with NRE 228.038*** 1.931* 163.357*** − 2.583***
environmentally friendly production techniques (Sadorsky, 2010). (0.000) (0.054) (0.000) (0.010)
Contrarily, the expansion of financing channels with high-energy use, GDP 270.652*** 3.899*** 95.602*** − 1.372
GHG emissions, and pollution levels has decreased the effectiveness of (0.000) (0.000) (0.000) (0.170)
green economic development (Boutabba, 2014; Yang and Ni, 2022).
Notes: Model 1 indicates G-7, whereas Model 2 denotes E-7 economies. *** and
To improve the environmental performance in both developed and
** denote the significant levels at 1% and 5%, respectively, whilst * claims at the
developing countries, this research aims to probe how mineral markets,
10% level. P-values are reported in the parenthesis.
financial development, and FDI play their role in achieving the goals of
sustainable energy consumption. The group of seven (G-7)9 comprises
exceptionally advanced economies, and these countries account for 58% Table 4
of the worldwide wealth (IMF, 2018). Among the advanced nations, this Findings of SH test.
is a platform to address financial and economic concerns since this Statistics Model 1 Model 2
forum is not free from challenges. Following ratifying the Paris Climate Test value Prob. Test value Prob.
Agreement (PCA), most G-7 nations have taken necessary ecological Tilde (Delta) 5.816*** <0.001 9.146*** <0.001
preservation actions to achieve sustainable economic development. Adjusted tilde (Delta) 7.185*** <0.001 11.299*** <0.001
However, the G-7 economies are responsible for almost 24.58% of GHG
emissions worldwide (Wang et al., 2020; WDI, 2021). The emerging
Notes: *** and ** denote the significant levels at 1% and 5%, respectively, while
seven10 or E-7 countries comprise the other sample panel for our * claims at a 10% level.
research. These economies have the potential to expand and may even
For the development and stability8 of an economy, the financial
outperform several important developed markets. Therefore, it is crucial
sector is crucial. It is indispensable to consider the financial sector
to research E-7 economies to provide obligatory policies and ’s role
in environmental degradation. Three key channels may explain the recommendations.
relationship between energy and financial development. Firstly, more
This study explores the tendency of energy transition from 1990 to
financial development increases foreign direct investment (FDI) and
2019 for G-7 and E-7 countries as represented in Fig. 1. Since 1990, the
9 G-7 consists of Canada, France, Germany, Italy, Japan, the United Kingdom
8 Economic and financial stabilities are two sides of the same coin (Nasir
and the United States of America. et al., 2015).
10 Brazil, China, India, Indonesia, Mexico, Russia, and Turkey. 4 M. Irfan et al. 5
Fig. 3. Binary-relation scatter plots. Energy Economics 121 (2023) 106661 M. Irfan et Energy Economics al. 121 (2023) 106661 Table 5
Findings of 1st and 2nd generation unit root tests. Variables IPS ADF CIPS CADF I(0) I(I) I(0) I(I) I(0) I(I) I(0) I(I) Model 1 ET 6.769 − 4.854*** 4.782 − 5.292*** − 1.129 − 5.314*** − 0.578 − 3.392*** (1.000) (0.000) (1.000) (0.000) (0.999) (0.000) MM − 3.094*** − 7.912*** − 3.398*** − 8.616*** − 2.395** − 4.997*** − 2.217 − 4.677*** (0.001) (0.000) (0.000) (0.000) (0.106) (0.000) FD − 3.370*** − 6.104*** − 3.711*** − 6.657*** − 2.487** − 5.484*** − 2.064 − 3.955*** (0.000) (0.000) (0.000) (0.000) (0.204) (0.000) FDI − 1.951** − 8.684*** − 2.126** − 9.319*** − 3.359*** − 6.018*** − 2.600** − 4.258*** (0.026) (0.000) (0.017) (0.000) (0.011) (0.000) URB 1.800 0.376 1.525 0.544 1.113 − 1.849 − 0.305 − 1.135 (0.964) (0.646) (0.936) (0.707) (1.000) (0.956) RE 6.925 − 5.235*** 4.710 − 5.669*** − 1.369 − 5.149*** − 0.892 − 3.517*** (1.000) (0.000) (1.000) (0.000) (0.991) (0.000) NRE 1.780 − 6.850*** 1.966 − 7.515*** − 1.421 − 5.172*** − 1.601 − 3.269*** (0.963) (0.000) (0.975) (0.000) (0.668) (0.000) GDP 1.025 − 6.761*** 1.162 − 7.298*** − 1.425 − 3.686*** − 1.938 − 2.742*** (0.847) (0.000) (0.877) (0.000) (0.314) (0.004) Model 2 ET − 0.676 − 6.291*** − 0.644 − 6.733*** − 1.707 − 4.930*** − 1.450 − 3.157*** (0.250) (0.000) (0.260) (0.000) (0.801) (0.000) MM − 3.186*** − 8.053*** − 3.394*** − 8.747*** − 3.170*** − 5.575*** − 2.531** − 4.001*** (0.000) (0.000) (0.000) (0.000) (0.018) (0.000) FD − 0.774 − 8.091*** − 0.791 − 8.765*** − 2.234* − 5.061*** − 2.335* − 3.971*** (0.220) (0.000) (0.214) (0.000) (0.058) (0.000) FDI − 2.487*** − 8.114*** − 2.685*** − 8.686*** − 3.114*** − 5.524*** − 2.958*** − 4.075*** (0.007) (0.000) (0.004) (0.000) (0.001) (0.000) URB 2.940 1.296 2.525 1.278 − 1.513 − 2.348** − 4.234*** − 1.758 (0.998) (0.902) (0.994) (0.899) (0.000) (0.502) RE 0.656 − 6.744*** 0.768 − 7.246*** − 1.946 − 4.917*** − 1.719 − 3.172*** (0.744) (0.000) (0.779) (0.000) (0.545) (0.000) NRE − 1.999** − 6.605*** − 2.186** − 7.067*** − 2.109 − 4.874*** − 1.834 − 3.508*** (0.023) (0.000) (0.014) (0.000) (0.420) (0.000) GDP 2.620 − 4.938*** 2.671 − 5.471*** − 1.895 − 3.381*** − 2.647*** − 2.545** (0.996) (0.000) (0.996) (0.000) (0.008) (0.016)
Model 1 indicates G-7, whereas Model 2 denotes E-7 nations. *** and ** represent the significant levels at 1% and 5%, respectively, while * claims at the 10% level. P-
values are shown in the parenthesis. Table 6
Findings of unit root test with a structural break. Variables Model 1 Model 2 I(0) NB I(1) NB I(0) NB I(1) NB ET 2.123 1 − 16.150*** 1 1.188 1 − 17.619*** 1 (1.000) (0.000) (1.000) (0.000) MM − 0.297 1 − 3.782*** 1 − 0.794 1 − 9.889*** 1 (0.280) (0.000) (0.400) (0.000) FD − 0.005 1 − 0.103*** 1 0.000 1 − 0.126*** 1 (0.110) (0.000) (0.870) (0.000) FDI − 4.696** 1 − 24.616*** 1 − 0.314 1 − 8.261*** 1 (0.030) (0.000) (0.220) (0.030) URB 0.153 1 − 8.563*** 1 0.190 1 − 6.153*** 1 (0.900) (0.000) (0.960) (0.000) RE 1.742 1 − 13.699*** 1 1.792 1 − 18.745*** 1 (1.000) (0.000) (1.000) (0.000) NRE − 2.001 1 − 18.110*** 1 0.318 1 − 22.429*** 1 (0.230) (0.000) (0.960) (0.000) GDP 0.000 1 − 0.004*** 1 0.001 1 − 0.014* 1 (1.000) (0.000) (1.000) (0.090)
Model 1 indicates G-7, whereas Model 2 denotes E-7 nations. NB stands for the number of breaks. *** and ** represent the significant levels at 1% and 5%, respectively,
while * claims at the 10% level. P-values are shown in the parenthesis.
energy transition in G-7 countries (orange bars) is gradually increasing
The current study aims to probe the dynamic determinants of the
and has almost doubled in the given three decades asserting that the
energy transition, making several possible contributions to the field from
increased dependence of developed nations on clean and renewable
numerous angles. First and foremost, energy transition, a somewhat
energy sources. In the case of emerging economies (blue bars), the en-
idiosyncratic element, focuses on cleaner energy input to curb GHG
ergy transition share is steadily decreasing and has become half of its
emissions by concerning low-carbon power generation (Nam and Jin,
starting point in 1990. This is because non-renewable energy practices
2021) following the energy trade perspective (Zhang et al., 2021). The
are fulfilling more demand for energy in emerging economies.
primary theoretical input of this research states that how much clean 6 M. Irfan et Energy Economics al. 121 (2023) 106661 Table 7
the environment’s ecology. While dirty energy hinders green develop-
Findings of cointegration test.
ment, clean energy encourages it (Ulucak, 2020). The primary contri- Estimates Model 1 Model 2
bution to global warming is humans’ usage of dirty energy in their daily
activities of production and habitation (Sarkar et al., 2022). Moreover, Stat. Prob. Stat. Prob.
the effects of global warming on social progress and economic growth Pedroni Co-integration Test
result in a 25% decline in global GDP, even though limiting GHG Phillips-Perron t − 3.021*** 0.001 − 1.787** 0.037
emissions only costs 1% of the economic growth (Stern, 2007). The ef- Phillips-Perron t (Modified) 2.151** 0.016 3.059*** 0.001 Dickey-Fuller t (Augmented) − 2.754*** 0.003 − 0.660 0.255
fect of global warming on the economy is greater than the expense of
mitigating it; hence global warming is becoming a cause of slowing
down economic growth (Dogan et al., 2022; Tzeremes et al., 2023). Kao Co-integration Test Dickey-Fuller t 3.443*** <0.001 − 2.718*** 0.003
Therefore, it makes perfect sense to depend less on dirty energy and Dickey-Fuller t (Modified) 2.418*** 0.008 − 2.261** 0.012
increase the use of clean energy that helps to mitigate environmental Dickey-Fuller t (Augmented) 3.562*** <0.001 − 2.081** 0.019
pollution (Rahman and Alam, 2021). The application and promotion of Unadjusted Dickey-Fuller t 3.595*** <0.001 − 2.541*** 0.006
green energy have evolved into the contemporary era’s development Unadjusted Dickey-Fuller t (Modified) 2.469*** 0.007 − 1.753** 0.040
trend in response to the rising demand for sustainable development.
A scarce body of studies estimates the role of renewable and con-
Notes: *** and ** denote the significant levels at 1% and 5%, respectively, while
ventional energy and financial channels on energy transition when * claims at a 10% level.
technical circumstances stay the same (Chen et al., 2022; Liu et al.,
2022). As a result, the second novelty of this article is to calculate the
energy can substitute for dirty energy is worthy of attention. Second, the
amount of clean energy that can replace dirty energy in both emerging
dynamic impact of the mineral markets, financial development, and FDI
and developed economies. This study significantly lowers environ-
on energy transition is investigated using the cross-sectional autore-
mental deterioration since it encourages implementing clean energy to
gressive distributive lag (CS-ARDL) statistical technique (Chudik and
promote sustainable development and green growth. Panel data analysis
Pesaran, 2015) to estimate the long and short-run impact of explanatory
has been used to estimate how much carbon is mitigated by the global
variables for the period of 1990–2019. This advanced approach is effi-
energy transition. The impact of renewable energy on GHG emissions
cient enough to handle cross-section dependence (CSD), heterogeneous
was disclosed for BRICS and sub-Saharan nations (Danish, 2020). In
slope coefficients, endogeneity, and unit root in the series (Khan et al.,
contrast, Vo et al. (2020) independently revealed the influence of nu-
2020). In addition to the dynamic model, augmented mean group (AMG)
clear, alternative, and renewable energy on carbon reduction. Both
and common correlated effect mean group (CCEMG) estimator are
studies concluded that using cleaner energy helps to reduce CO
applied to obtain robust results. Third, to contrast developed and devel- 2 emis-
sions. Consuming clean energy contributes to sustainable economic
oping economies, this research used the sample of G-7 and E-7 countries
development (Taskın et al., 2020). The necessity of encouraging the use
separately to compare the features of both groups and recommend
of clean energy is confirmed by literature demonstrating the link be-
necessary policy implications. Last, the energy transition becomes
tween renewable energy and green growth. Pao and Fu (2013) discov-
enriched using a pool of control variables: urbanization, clean energy,
ered how using clean and dirty energy might affect economic growth in
non-renewable energy, and economic growth to obtain reliable
Mexico, Indonesia, South Korea, and Turkey (MIST). outcomes.
Extended literature argued for the significance of the phases and
The rest of the article is structured as follows. Section 2 reviews and
components of mining operations that contribute to the energy transi-
presents the relevant literature. Section 3 defines the data and in-
tion. For example, growing consumer demand raises concerns about
troduces the methodology used in this study. Section 4 portrays the
metal supply and scarcity. Thus, mining projects must demonstrate their
empirical analysis of the effects of minerals and financial development
risk assessment, mitigation, and management capacity. Otherwise, the
on energy transition in developed and developing nations. In the end,
energy transition-based minerals supply would be delayed, making the
the conclusion and necessary policy implications are shown in Section 5.
transition to a low-carbon proposal much more challenging (Islam et al.,
2022; Lebre et al., 2020; Zhu et al., 2022). Research on the mining
2. Literature review and theoretical framework
projects’ risk management analyzed that mining at large-scale opera-
tions has been ineffective due to underestimating or ignoring the haz- 2.1. Literature review
ards (Irfan et al., 2022a; Xie et al., 2022). For the success of mining
operations, technologies should be applied effectively to increase the
Energy and GHG emissions have a strong relationship; hence, several
dependability of choices (Irfan et al., 2022b).
research studies have inspected the influence of energy transition and
Other risk-based quantitative studies undertaken in the mining
efficiency on CO2 emissions (Nam and Jin, 2021). Clean energy, which
sector have focused on various risk factors, including operational, safety,
may be used as a proxy for energy transition, has been included in most
and water inrush. For instance, Gul et al. (2019) developed an advanced
articles that have linked substitution and electrification with energy
technique for an underground zinc and copper mines case study. Iphar
transition. This nexus provided empirical evidence and theoretical
and Cukurluoz (2020) presented a fuzzy safety evaluation approach to
background for green development to validate the socio-economic in-
improve the risk assessment practice in mechanized coal mines. This was
dicators and improve environmental performance (Acheampong et al.,
done to compensate for the deficiencies of the traditional decision ma- 2023; Wang et al., 2022).
trix method’s precise risk score. Financial risks at a gold mine were
Clean11 and dirty12 energy are two categories of energy consump-
evaluated using an expanded TOPSIS technique (Jiskani et al., 2022).
tion. This classification depends on how much energy usage influences
Economic growth and financial development have significantly
affected the relationship between the environment and energy (Khan
et al., 2022). The first category ensures empirical and theoretical sup- 11
port for financial development to demonstrate how crucial it is to
Clean energy is primarily defined as energy that does not produce waste,
encourage economic growth and preserve environmental performance
pollution or GHG emissions and thus is not considered bad for the environment
(Chincarini and Moneta, 2021; Lee and Wang, 2022). (Garai and Sarkar, 2022). 12
Various studies have observed the effects of financial development
On the other hand, dirty energy, such as fossil fuel-based, is the energy that
emits significant amounts of GHG gases, solid and liquid wastes that are
on environmental deterioration since the Environmental Kuznets Curve
damaging to the atmosphere throughout the consumption process.
(EKC) hypothesis was proposed (Grossman and Krueger, 1995). 7 M. Irfan et Energy Economics al. 121 (2023) 106661 Table 8
Findings of Westerlund Bootstrap cointegration test. Estimates Model 1 Model 2 Value Z-value Prob. Robust Prob. Value Z-value Prob. Robust Prob. Gt − 3.995 − 4.293 <0.001 <0.001 − 2.653 − 0.020 0.492 0.148 Ga − 9.310 1.259 0.896 <0.001 − 0.611 4.610 1.000 1.000 Pt − 8.780 − 2.858 0.002 0.015 − 15.835 − 8.841 0.000 0.000 Pa − 4.130 1.806 0.965 0.405 − 1.164 3.223 0.999 0.850
Notes: *** and ** denote the significant levels at 1% and 5%, respectively, while * claims at a 10% level.
Fig. 4. Circulars plots.
According to one school of thought, financial development has a nega-
emissions (Charfeddine and Kahia, 2019). Similarly, Khan et al. (2017)
tive impact on ecological performance (Ouyang and Li, 2018). Due to
examined how financial development affects environmental degrada-
financial development, financial institutions provide households and
tion in 34 upper-middle-income nations. The authors’ empirical analysis
investors with low-cost borrowing options with fewer constraints,
discovered that financial development adversely affects ecological per-
increasing their need for energy and hence, contributing to GHG
formance. In contrast, the other school contends that the financial sector 8 M. Irfan et Energy Economics al. 121 (2023) 106661 Table 9 Findings of CS-ARDL test. Variables Model 1 Model 2 Variables Model 1 Model 2 Coef. St. Errors Coef. St. Errors Coef. St. Errors Coef. St. Errors Long Run Estimates Short Run Estimates MM 0.356*** 0.072 0.020 0.129 ΔMM 0.352*** 0.071 0.022 0.132 (0.000) (0.879) (0.000) (0.869) FD − 0.491** 0.244 − 1.472 1.862 ΔFD − 0.516** 0.249 − 1.503 2.012 (0.045) (0.429) (0.038) (0.455) FDI 0.005 0.043 − 0.200 0.193 ΔFDI 0.002 0.042 − 0.208 0.199 (0.911) (0.300) (0.968) (0.296) RE 1.297*** 0.109 1.281*** 0.103 ΔRE 1.290*** 0.103 1.324*** 0.105 (0.000) (0.000) (0.000) (0.000) NRE − 0.185*** 0.051 − 0.636*** 0.183 ΔNRE − 0.180*** 0.049 − 0.670*** 0.194 (0.000) (0.001) (0.000) (0.001) URB 0.426* 0.219 0.521* 0.288 ΔURB 0.403** 0.203 0.554* 0.302 (0.052) (0.071) (0.047) (0.066) GDP 0.026 1.472 − 0.143 3.556 ΔGDP − 0.138 1.447 − 0.346 3.716 (0.986) (0.968) (0.924) (0.926) F-Stat 30.280 47.340 p-value 0.005 0.071 N 196 196
Model 1 indicates G-7, whereas Model 2 denotes E-7 economies. *** and ** denote the significant levels at 1% and 5%, respectively, while * claims at the 10% level. P-
values are reported in parentheses.
Most economies place concerns on economic development, and the Table 10
urgent issues related to climate change have sparked researchers’ in- Findings of robustness tests.
terest in finding ways to reduce its damaging impacts. Even so, emerging Variables Model 1 Model 2
countries seek to escalate their economic development through various
methods and processes. One of the most appealing techniques is FDI, AMG CCEMG AMG CCEMG
which is a significant source of outside investment since it may boost MM 0.154** 0.662** 0.082* 0.078*
economic growth by expanding production. Moreover, it might result in (0.061) (0.286) (0.048) (0.041) FD − 0.499* − 0.491 0.928** 3.202**
the transfer of modern technology and assistance as well as the creation (0.293) (0.397) (0.474) (1.315)
of employment. More in-depth analyses of the FDI phenomenon and its FDI 0.002 0.038 − 0.027 0.457*
effects on the environment have recently been conducted in academic (0.016) (0.034) (0.058) (0.240) studies. REC 1.201*** 1.270*** 1.230*** 1.266*** (0.045) (0.046) (0.120) (0.034) NREC − 0.163*** − 0.116*** − 0.675*** − 0.444*** (0.052) (0.032) (0.260) (0.043)
2.2. Theoretical framework URB 0.027 − 0.103 − 0.016 1.107*** (0.080) (0.213) (0.023) (0.348)
The process of energy transition covers three facets: energy inde- GDP − 0.254* 3.290 − 1.161 − 9.550** (0.139) (2.275) (1.138) (4.694)
pendence, reducing adverse environmental effects, growing the indus- Const. 10.101 41.567** 39.515* − 256.535***
trial and service sectors to accomplish sustainable environmental (9.390) (18.458) (22.514) (74.976)
objectives (Lantz et al., 2021), and transferring the energy sector to a Wald-Test 727.760 7418.140 120.280 43.650
clean and sustainable one (De La Pena et al., 2022). The energy transi- RMSE 0.052 0.027 0.256 0.107
tion indicator is adjusted for FDI and financial development. The p-vale <0.001 <0.001 <0.001 <0.001 N 210 210 210 210
Pollution Halo13 and the Pollution Haven14 hypotheses capture the lit-
erature’s attention by considering the environmental effects of FDI. Most
Model 1 indicates G-7, whereas Model 2 denotes E-7 economies. *** and **
research (see Huang et al., 2019; Singhania and Saini, 2021; Zafar et al.,
denote the significant levels at 1% and 5%, respectively, while * claims at the
2020) affirms the Pollution Haven in developing nations and claims that
10% level. P-values are reported in parentheses.
FDI raises GHG emissions by transferring the contaminating activities to
raises environmental standards. For instance, efficient research and
the host countries. FDI can curtail GHG emissions via energy-efficient
technology and restrictions on carbon emissions projects lead to green
technologies and boost economic development (Caetano et al., 2022). finance.
Based on the theoretical notion of the energy trade model, financi-
Similarly, Shahbaz et al. (2013) claimed that trade openness and
alization and FDI further lead to trade activities affecting cleaner energy
financial sector growth had decreased environmental damage in Indo-
consumption through three distinct aspects: scale, technical and nesia
composition effects. The scale effect pertains to escalating the produc-
’s case. The nexus between financial development and GHG emis-
sions were examined for G-20 nations and found that the financial sector
tion level in an economy. For instance, a rise in the production process
reduces GHG emissions. In addition, it is also noted that financial
demands more raw materials and energy, further leading to financiali-
development does not have any relationship with environmental quality
zation and increasing environmental contamination by shifting the
(Ozturk and Acaravci (2013). Global economies are adopting green in-
vestment strategies and transferring investments from high to low- 13
polluting projects to combat climate change (Wang and Zhi, 2016;
According to the Pollution Halo theory, FDI transfers effective and envi-
Zerbib, 2019). By providing the funding needed for projects with low
ronmentally friendly technologies that lower degradation primarily by CO
consuming less energy (Aust et al., 2020).
2 emissions levels, financial instruments like blue and green bonds
14 In the Pollution Haven theory, on the other hand, economies with strict
may play a significant role in addressing climate-related challenges
environmental policies shift their polluting industries to nations with less
(Mumtaz and Yoshino, 2021; Xu et al., 2020).
stringent environmental regulations. 9 M. Irfan et Energy Economics al. 121 (2023) 106661
country to the industrial level (Zhang et al., 2021). The technical effect
transition, mineral markets, financial development, FDI, and renewable
helps to adopt innovation and advanced technologies to improve effi-
energy for G-7 and E-7 economies in 1990 and 2019 in Fig. 2. Economies
cient production and energy themes. In the end, the composition effect
are allocated into 07 levels, where bright color directs higher magnitude
presents a change in the economic structure mix: Shifting to the services
whereas light color denotes a lower value of the relevant indicator. In
section (less polluting sector).
the given three decades, every developed or developing country has
While the continuing growth in financialization and economic ac-
been changed blatantly, as conspicuous in the figure.
tivities, it has become progressively challenging to hold the proportions
of these influencing three effects constant. In the case of economic
3.2. Econometric modeling
growth, GDP is a measure of economic health and comprises several
economic components, including investment, production, consumption,
3.2.1. CSD and slope heterogeneity tests
government spending, and FDI. Since a significant part of economic
This study estimates the CSD15 and SH coefficient tests for all the
growth involves energy consumption and this rising consumption is
variables. Traditional methods have ignored these basic preliminary
directly associated with energy transition. Energy transition fosters the
tests, and their absence may lead to inconsistent estimates (Li et al.,
inclusive inputs to mitigate GHG emissions that have been validated
2020; Ulucak and Khan, 2020). For instance, in the presence of het-
(Nam and Jin, 2021) following the energy trade perspective (Zhang
erogeneity, the SH test is efficient enough to handle the homogenous
et al., 2021), Pollution Halo and Haven hypothesis (Caetano et al.,
coefficient’ assumptions (Baltagi and Pesaran, 2007). The general for-
2022). Hence, this study covers the minerals and urbanization along
mula of this test is shown below:
with financialization, FDI and GDP effect on energy transition in ( )
developed and emerging economies, which needs further research. 1 1
ΔSH = (V)2(2h)− 12 + N − h (3.2) V
3. Materials and methods ( ) ( ) − 12 1
2h(S − h − 1 1 Δ 2 + N − 2h (3.3) 3.1. Data ASH = (V ) S + 1 V
This research investigates the dynamic association between mineral
where ΔSH and ΔASH report slope coefficient’ homogeneity in delta SH
markets, financial development, FDI, urbanization, renewable energy,
and delta SH (adjusted), respectively.
non-renewable energy, and economic growth with energy transition in
14 economies from 1990 to 2019. The time span depends upon the data 3.2.2. Unit root test
availability of study variables. The sample is further bifurcated into
This research has employed both 1st generation: Im, Pesaran, and
models: Model 1 and 2, illustrating the G-7 and E-7 economies,
Shin (IPS) (Im et al., 2003) and Augmented Dickey-Fuller (ADF) and 2nd
respectively, to compare the empirical outcomes. In this part, this
generation: cross-sectional ADF (Pesaran, 2003) and cross-sectional IPS
research elucidates the nature of the study variables and their potential
(Pesaran, 2007) unit root tests. The 2nd generation unit root tests are
relationship with the dependent variable. Supplementary material is
robust to CSD and Slope Heterogeneity (SH) coefficients. The general provided in Appendix A.
formula of the unit root test is indicated as follows:
Energy transition (the share of clean energy in the TPES: total pri- ∑ ∑ m m ΔT ¯ A δ δ
mary energy supply) (Nam and Jin, 2021) is retrieved from World Bank
it = δi + δiTit− 1 + δi t− 1 + ihΔT t− 1 +
ihΔTit− h + εit (3.4) h=0 h=1
(WDI). The data on the mineral markets (minerals export trading share,
The equation mentioned above shows the difference and lag values
expected direction ∂ET > 0) (Jiskani et al., 2021) is collected from World as ΔT ∂MM
t− 1 and Tt− 1, respectively. Hence, the statistics of the cross-
Integrated Trade Solution (WITS), whilst financial development data
sectional IPS unit root test are reported below:
(financial access, depth, and efficiency of financial markets and in- / ∑
stitutions, expected direction ∂ET < 0) (Baloch et al., 2021) is gathered m ∂FD CIPS = 1 V CADFi (3.5)
from International Monitoring Fund (IMF). The IMF i=1 ’s financial devel-
opment ranges from 0 to 1. This dataset provides a multi-dimensional
In Eq. (3.5), CADF denotes cross-sectional ADF as shown in Eq. (3.4)
measure and broader coverage for financial sector development using and H
eight different indicators. More so, the data of FDI (net inflow of in-
0 state the non-stationarity of the data.
vestments, expected direction ∂ET > 0) (Caetano et al., 2022), urbani- ∂FDI
3.2.3. Cointegration test
zation (population in urban areas, expected direction ∂ET > 0) (Yao and
To estimate the long-run cointegration among study variables, this ∂URB
Tang, 2021), renewable energy (share of total energy, expected direc-
study employed both 1st generation: Kao (Kao, 1999) and Pedroni
tion ∂ET > 0), non-renewable energy (share of fossil fuels-based energy,
(Pedroni, 2004) and 2nd generation: Westerlund bootstrap (Westerlund, ∂RE
expected direction ∂ET < 0) and economic growth (per capita GDP,
2007)16 based on error correction (EC), cointegration tests. The general ∂NRE expected direction
form of cointegration test is as follows:
∂ET > 0) is aggrandized from WDI, as reported in ∂GDP
Table 1. The table further clarifies the variable’s name, symbol, source,
Pt = δ/SE(δ) (3.6)
and measurement unit. The FDI and GDP are transformed into loga-
rithmic forms to present better outcomes.
Pa = t(δ) (3.7)
To assess the short and long-run outcomes, this study employs a /
cross-sectional ARDL technique (Chudik and Pesaran, 2015). The model ∑v δi
of this study is presented below. Gt = 1 V (3.8)
i− 1SE(δi)
ETit = ρ + ρ MM FD FDI URB RE O 1
it + ρ2
it + ρ3
it + ρ4
it + ρ5 it (3.1) + ρ NRE GDP 6
it + ρ7
it + εit
15 This test is capable of handling the shocks in developed and emerging
ρO shows the slope, ρ1 to ρ7 are coefficients of explanatory variables, economies (Pesaran, 2004).
whilst εit denotes the residuals. i indicates the time period (from 1990 to
16 Compared with 1st generation cointegration tests such as Kao and Pedro-
2019), whereas t reports the cross-sections (14 developed and emerging
ni’s, the Westerlund bootstrap test is robust with error coefficients of SH (Khan
countries). This research illustrates the distributions of the energy et al., 2020). 10 M. Irfan et Energy Economics al. 121 (2023) 106661 /
Kao and Pedroni cointegration tests as in Table 7. The results of these ∑v Sδ G i
tests validate the presence of long-term connections in the hypothesized a = 1 V (3.9) i− 1δi(1)
variables by rejecting the H0 of no cointegration. Eqs. (3.6) and (3.7) of P
To overcome the issues of CSD and slope heterogeneity, this study
t and Pa explore panel while Eqs. (3.8) and (3.9) of G
further applies the 2nd generation panel cointegration test, for instance,
t and Ga illustrate group means statistics, where H0 is of no cointegration.
bootstrap Westerlund as recorded in Table 8. Following the findings of
the Westerlund test, both panel (Pt and Pa) and group (Gt and Ga) mean
3.2.4. CS-ARDL modeling
statistics, cointegration is recommended by rejecting the H0 of no
This study applies cross-sectional ARDL for long and short-run esti-
cointegration. Consequently, the panel data variables are significantly
mations (Chudik and Pesaran, 2015). This approach is more robust to
interlinked. With this robust cointegration, two conditions: connection
endogeneity, CSD, SH coefficients, unobserved common factors17, and
is not spurious, and coefficients are valuable for estimations are met.
non-stationarity18 (Danish, 2019; Khan et al., 2020). The universal form
Furthermore, to overview the study, variables including energy transi-
of CS-ARDL is presented below:
tion, minerals, financial development, FDI, urbanization, and renewable
energy are explored by circular plots, as shown in Fig. 4. These unique ∑ ∑ ∑ m m 3 ET
plots present a comprehensive picture of key variables as shown in the it = α0 + λitETit− r + β Pt− r + Vt− r + εit (3.10) r=1 r=0 it r=0
legend of the graphs. The dark color of the variables represents more where V magnitude.
t = (ΔETit, Pt’)’ and Pit = (MMit, FMit, FDIit, URBit, REit, NREit, GDP
The outcomes of Model 1 in Table 9 explain the results, which infer
it)’, P is the pool of explanatory variables, for instance, minerals,
financial development, FDI, urbanization, clean energy, non-renewable
that the minerals are positively significant with the coefficients of 0.352 energy, and GDP.
and 0.356; it represents that a 1% surge in minerals can cause a 0.35%
increase in the energy transition in both the short and long run. These
4. Results and discussion
results reveal that mineral resources contribute to the sustainable
objective in developed nations. These outcomes are supported by the
Table 2 reports the summary statistics of the study variables for both
extant studies (Jiskani et al., 2022; Lebre et al., 2020; Ulucak, 2020).
models. It is shown that the mean of energy transition and minerals is
The findings of Model 2 state that MM also has a direct connection with
higher in E-7 countries; however, the volatility of these variables is
energy transition but with a lower magnitude indicating inadequate
higher in developing countries. Table 2 reports that financial develop-
intentions of emerging nations toward mineral use to meet the demands
ment, FDI, and urbanization have more mean magnitude in Model 1,
of clean energy. Traditional energy dependence seems to impede the
indicating advanced financial management. The statistics further indi-
energy transition. Though this appears to be an undesirable outcome, it
cate that the mean of cleaner and traditional energy is more favorable in
is indispensable to mention that the energy transition concerns the
developing nations indicating that they are focusing on sustainable
amount of energy produced. Fareed et al. (2022) argued that this in-
economic development. In the end, economic growth presents more
fluence might be expounded by the impact of advantage toward clean
obvious in Model 1. These descriptive statistics assert an idea about the
energy production. They suggests that renewable energy could benefit
characteristics of both models.
the economies to accomplish Agenda 2030, provided the energy tran-
Moreover, to examine the cross-sections of the study, CSD is esti-
sition is a sustainable opportunity. The findings of the study testify to the
mated using Breusch-Pagan and Pesaran CD CSD tests. The outcomes of
crucial role played by energy transition; indeed, given the statistic that
Table 3 report that CSD exists among the study variables by rejecting the
clean energy considerably refers to the addition in sustainable growth. H
In the case of financial development, short and long-run coefficients
0 of no CSD, which infers the interconnection among G-7 and E-7
economies illustrating global economic spillover effects, regional con-
are significant and negative, i.e., − 0.516 and − 0.491. It concludes that
nectivity, and globalization (Hasanov et al., 2021). The findings of slope
financial development in advanced economies desperately depends on
homogeneity (SH) are interpreted in Table 4, which elaborates the null
energy utilization and is less concerned about environmental goals. hypothesis (H
However, Table 9 inspects that the negative influence of financial
0 = SH exists among the series). The results of the test
confirm that heterogeneity exists among cross-section slope coefficients.
development is more dominant in emerging countries than in advanced
Fig. 3 describes the correlational plots for both models of this study.
ones indicating that developing nations ignore sustained commitments
These two-way graphs indicate the binary relationship among all the
and are more anxious about energy consumption, even at the expense of
study variables from 1990 to 2019.
atmospheric pollution. These outcomes are aligned with the current
The authors employed 1st and 2nd generations unit root tests to
studies (Acheampong et al., 2020; Xu et al., 2022). Financial develop-
probe the integration properties of the data. The four-panel unit root
ment seems to be a barrier to the energy transition. Though this seems an
tests’ highlights, which are employed in this article and aim to inspect
undesirable outcome, it is essential to consider that economic growth
the integration order of the series, are reported in Table 5. In the pres-
leads to the energy transition. However, when only considering financial
ence of heterogeneity and CSD, 2nd generation non-stationarity tests
development to the energy sector, ceteris paribus, FD reduces environ-
such as CIPS and CADF are endorsed (Fareed et al., 2022). Indeed, the
mental pollution in the long run and becomes favorable to the advanced
recorded findings confirm that most variables have a unit root at the nations. level (by accepting the H
Meanwhile, FDI has a positive and negative relationship with energy
0 of stationarity). Hence, the study variables are
stationary at the 1st difference I (1), which validates that the study
transition for Models 1 and 2. Although the results of FDI are insignifi-
variables are integrated of order one. In the current situation, the
cant: however, the positive magnitude denotes that FDI in developed
applicable onward move is to estimate the enduring association among
economies supports environmental quality following the Pollution Halo
study variables. This study employed Karavias and Tzavalis (2014)
effect, and these outcomes are supported by the current literature
panel unit root test to observe the structural break. Table 6 reports that
(Caetano et al., 2022; Hao et al., 2020). Contrarily, the Pollution Haven
variables of both Model 1 and 2 are stationary at the first difference I (1)
affects developing countries to bear a negative connection of FDI with
with one structural break in the series. Therefore, this research applies
energy transition in the long and short run. Foreign direct investment
increases energy transition in developed nations elucidating that these
economies are more concerned about sustainable growth. Specifically,
in the long run, the higher coefficient of FDI indicates that advanced
17 Ignoring unobserved common factors can lead to biased estimations.
economies are following the objectives of Agenda 2030 by promoting
18 Able to deal with stationarity with varied difference levels.
clean and efficient energy generation. Lastly, renewable energy 11 M. Irfan et Energy Economics al. 121 (2023) 106661
consumption has a significant and positive relation with energy transi-
source. However, deployment and investment in cleaner energy must
tion. These outcomes are supported by the extant literature (Vo et al.,
continue in the future. The government should promote the use of
2020). Opposite to RE, non-renewable energy’s coefficients are docu-
cleaner sources rather than restrict them. Investing in RE in developed
mented to be negatively significant in the long and short run for both the
nations today results in a possible replacement of fossil fuel revenues.
study models. Empirical outcomes of CS-ARDL describe that the
Additionally, increased investment in energy storage might lower the
magnitude of NRE is more rigorous in emerging nations than in devel-
carbon intensity while addressing intermittency challenges. The mining oped countries.
sector relies on and prioritizes risks for sustainable mining operations.
Moreover, urbanization positively influences energy transition in
Policymakers and mining sustainability focus groups should consider
both the long and short run for developed and developing economies.
formulating climate-smart and green mining policies and their pro-
The coefficients of urbanization claim that in the case of the long run, 1
spective repercussions (Jiskani et al., 2022). This paper evaluates the
unit rise in URBs increases energy transition by 0.426 and 0.521 units in
hazards that must be addressed in order of importance for socially
Models 1 and 2, respectively. The current studies further support these
acceptable and sustainable mining methods to provide green and
consequences (Lantz et al., 2021). Ultimately, economic growth explains
cleaner energy. The environmental deterioration is jeopardy to mineral
its positive and negative connection with Models 1 and 2. In developed
resources, leading to the threat of lessening natural resources. The
economies, GDP emphasizes green energy and technological innovation
resource conservation agenda can assist the cleaner production tech-
to impart its role in improving ecological quality, whilst emerging na-
niques and the latest technology to thwart enormous losses. In addition,
tions, in the same scenario, are not aware and responsive to sustainable
it receives benefits from financial services and economic insurance to
growth to curtail the pollution level to accomplish the carbon neutrality
reinstate the natural resource protection strategy. More so, the financial
targets. The study outcomes are also tested for robustness and re-
development of G-7 and E-7 economies restricts green development ef-
confirmation by employing AMG (Eberhardt, 2012) and CCEMG
ficiency. Therefore, three elements of financial development should be
(Pesaran, 2007) estimations, as reported in Table 10.
examined while evaluating financial reform (financial deepening,
financial efficiency, and financial size) (Yang and Ni, 2022). In devel-
5. Conclusions and policy implications
oping their financial size, economies should optimize the financial re-
sources’ allocation and guide the flow of funds to pollution-free and low-
The Conference of Parties’ (COP-26) objectives, considering the
emission firms. The industrial orientation of green finance development
cleaner energy and environmental challenges, provide significant pres-
should be taken into account, with policies of financial assistance for the
sure to expedite the shift to the energy transition. This research aims to
expansion of low-carbon firms and risk sharing for the technical inno-
scrutinize the dynamic impact of mineral markets and financial devel- vation of clean energy.
opment along with a pool of auxiliary variables: FDI, urbanization, clean
Lastly, governments should invest in cleaner energy sources for the
energy, non-renewable energy, and GDP on energy transition from 1990
national grid. Specialization of the energy sector is essential because the
to 2019. To forecast the characteristics of both developed and emerging
ensuing economies of scale and efficiency would cut marginal costs, and
countries, the sample is further bi-furcated in Models 1 and 2,
consequently energy prices, and increase FDI. It is vital to avoid respectively.
importing dirty energy; thus, governments should promote backup from
With the massive expansion of minerals mining worldwide, minerals-
clean energy sources, especially those with accumulation potential, such
intensive countries have faced a mess regarding minerals extraction,
as hydro. As this study focuses on the G-7 and E-7 economies and sug-
supply, and commercial dealings. The authors of this study have
gests policy instruments for cleaner energy, minerals, and financial
employed CS-ARDL to estimate the financialization of minerals and
development, the policy agenda might appear inconclusive. Indeed, the
energy nexus. This advanced approach is efficient enough to deal with
suggested policy framework may have been more multifaceted due to
CSD, endogeneity, non-stationarity, and heterogeneous slope co-
the G7 nations’ other growth-related considerations. Although broad-
efficients (Khan et al., 2020). The empirical findings of this study esti-
ening the topic’s scope may have included more growth drivers, the
mate that minerals significantly contribute to the energy transition
parameters were selected within the theoretical limitations of the study
process for both models to accomplish low-carbon power generation. In
challenges. Though the policy framework can be organized by consid-
the case of financial development, it encourages the negative direction
ering the other contexts of advanced and emerging countries, which may
with energy transition in the long- and short-run. FDI, renewable energy,
demand a policy revamp to address the environmental deterioration
and urbanization support sustainable goals, while non-renewable en-
challenges, there lies the generalizability of the study’s recommended
ergy is negatively related to the energy transition. Developed economies
policy outline. To offer policy recommendations from a precise
draw such economic and environmental policies that follow sustainable
perspective, further research might benefit from examining the
development goals. Statistics discover that the supply and demand for
comparative situation by considering the micro or sectoral-level
minerals have significantly increased due to low carbon-intensive goals research.
and a rising share of renewable energy for sustainable energy genera-
tion. These outcomes are robust with the AMG and CCEMG statistical
CRediT authorship contribution statement techniques.
The following policy implications and recommendations are pro-
Muhammad Irfan: Resources, Conceptualization, Data curation,
vided to empower the decision-making process for all the energy tran-
Validation, Formal analysis, Writing – original draft, Writing – review &
sition stakeholders, as they are sympathetic to escalating the
editing. Mubeen Abdur Rehman: Writing – original draft, Writing –
decarbonization of the energy sector in both developed and developing
review & editing, Investigation, Visualization. Asif Razzaq: Conceptu-
countries. The energy mix must immediately reduce its carbon con-
alization, Methodology, Writing – original draft, Writing – review &
centration to attain the carbon neutrality objective. According to the
editing. Yu Hao: Conceptualization, Methodology, Funding acquisition,
analysis, focusing on traditional fuels such as coal and oil mitigates the
Supervision, Writing – original draft, Writing – review & editing.
likelihood of attaining a climate-stabilized scenario on time. Establish-
ing a Carbon Neutral objective by 2050 and developing short- and long- Acknowledgments
run policies must be the next step in the broader social perspective and
transforming energy sector priorities on decarbonization (De La Pena
The authors acknowledge sponsorship from Science and Technology
et al., 2022). Eliminating coal-fired power plants is another crucial move
Program of Zhejiang Province of China (2022C35060), The Technology
in the right way in emerging economies. In the context of ongoing en-
Innovation Program of Beijing Institute of Technology (2022CX01013),
ergy security concerns, natural gas would be the preferred energy
and the Joint Development Program of the Beijing Municipal 12 M. Irfan et Energy Economics al. 121 (2023) 106661
Commission of Education. The authors are also very grateful to the
Fareed, Z., Rehman, M.A., Adebayo, T.S., Wang, Y., Ahmad, M., Shahzad, F., 2022.
anonymous reviewers and Editor-in-Chief Prof. Dr. Richard S.J. Tol for
Financial inclusion and the environmental deterioration in Eurozone: the
their insightful comments that helped us sufficiently improve the quality
moderating role of innovation activity. Technol. Soc. 69, 101961 https://doi.org/
10.1016/j.techsoc.2022.101961.
of this paper. The usual disclaimer applies.
Garai, A., Sarkar, B., 2022. Economically independent reverse logistics of customer-
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Document Outline

What derives renewable energy transition in G-7 and E-7 countries? The role of financial development and mineral markets
- 1 Introduction
- 2 Literature review and theoretical framework
  - 2.1 Literature review
  - 2.2 Theoretical framework
- 3 Materials and methods
  - 3.1 Data
  - 3.2 Econometric modeling
    - 3.2.1 CSD and slope heterogeneity tests
    - 3.2.2 Unit root test
    - 3.2.3 Cointegration test
    - 3.2.4 CS-ARDL modeling
- 4 Results and discussion
- 5 Conclusions and policy implications
- CRediT authorship contribution statement
- Acknowledgments
- Appendix A Supplementary data
- References

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