fdi and re.pdf

Renewable Energy 201 (2022) 135–149
Available online 17 November 2022
0960-1481/© 2022 Elsevier Ltd. All rights reserved.
The effect of foreign direct investment on renewable energy consumption
subject to the moderating effect of environmental regulation: Evidence
from the BRICS countries
Yan Tan a
, Utai Uprasen b,*
a
Yulin Normal University, 1303 East Jiaoyu Road, Yulin, Guangxi, 537000, China
b
Pukyong National University, 45 Yongso-ro, Nam-gu, Busan, 48513, South Korea
A R T I C L E I N F O
Keywords:
Foreign direct investment
Renewable energy consumption
Environmental regulation
Moderating effect
Panel threshold model
BRICS
A B S T R A C T
The environmental impact of foreign direct investment (FDI) is inconclusive, and FDI’s different effects are
generally explained through various factors. Meanwhile, this paper explores the effect of FDI on renewable
energy consumption. Environmental regulation is considered as a moderating variable and a threshold variable
simultaneously to capture the nonlinear association between FDI and renewable energy consumption. The panel
threshold method was employed for empirical estimations using the dataset of the BRICS countries from 1990 to
2015. In addition, the generalized method of moments (GMM) models—both system GMM and difference GMM
models—were applied to verify the robustness of this work. When formal environmental regulation was used as a
moderating variable, the empirical results indicate that FDI reduces renewable energy consumption when the
degree of regulatory stringency is lower than a threshold level. In addition, it turns to foster renewable energy
consumption once the stringency is higher than the threshold. A similar finding was obtained when an informal
environmental regulation was considered. Furthermore, the findings suggest that stricter environmental regu
lation can substantially promote renewable energy consumption emanating from FDI.
1. Introduction
Most countries around the world ratified the Paris Agreement in
2015. This agreement included a declaration to limit global warming to
below 2◦
Celsius (◦
C) and try to decrease to 1.5 ◦
C, compared to pre-
industrial levels, by 2050. To have net zero emissions by 2050, the
world requires a cut of 37 gigatonnes (Gt) of annual emissions. This
pathway to sustainability requires tremendous changes in patterns of
energy consumption. Renewable energy plays a vital role in the net zero
emission scenario. Hence, replacing fossil fuels with renewable energy is
critical to environmental sustainability. The share of renewable energy
in global energy would need to increase from 19% in 2019 to 79% by
2050 [1].
The possibility of achieving the 2050 goal depends on sufficient
progress by 2030 to accelerate the renewables-based transition. The
agreement at the 26th United Nations Climate Change Conference
(COP26) in Glasgow demanded that countries revise and strengthen the
Nationally Determined Contributions (NDCs) to meet the ambitious
2030 target. Based on the new and updated NDCs and the parties’
commitment to net zero pledges, emissions are expected to reduce by
approximately 20% by 2030 compared to business as usual before the
first NDCs. This may help limit the increase in temperature to 2.1 ◦
C,
which is still well above the 1.5 ◦
C goal. Despite the increased ambition
of the parties, current climate pledges are inadequate to achieve the
2050 goal. Substantial additional efforts are required from the parties to
achieve the 1.5 ◦
C target [2].
The required scale-up of renewable energy deployment requires
effective collaboration between private and public sectors. In addition,
the renewable targets in the NDCs must be translated into national laws,
and environmental policies need to become increasingly stringent [3].
The Paris Agreement requires both developed and developing countries
to manage greenhouse gas emissions. Governments need to scale up
renewable energy deployments and reduce energy-related greenhouse
gas emissions.
Regarding developing countries, the environment and economic
development are often critical national issues. Foreign direct investment
(FDI) inflow can respond to the economic imperative that developing
giants like the BRICS (Brazil, Russia, India, China, and South Africa) are
* Corresponding author.
E-mail addresses: tinatan1129@gmail.com (Y. Tan), utai_uprasen@pknu.ac.kr (U. Uprasen).
Contents lists available at ScienceDirect
Renewable Energy
journal homepage: www.elsevier.com/locate/renene
https://doi.org/10.1016/j.renene.2022.11.066
Received 12 May 2022; Received in revised form 28 September 2022; Accepted 14 November 2022

136
encountering. FDI brings an influx of numerous benefits to developing
countries, for example, GDP growth, technology, management skills,
capital, and an improvement in living standards. Therefore, FDI is
sought-after in developing countries. The inward stock of FDI of the
BRICS accounted for 9.58 and 27.61% of the whole world and devel
oping countries, respectively, whereby they held 48.58% of the GDP of
developing countries for the past decade.
However, the benefits of FDI do not always accrue automatically or
impartially across countries. The significant increase of FDI in devel
oping countries might be connected to environmental degradation
because of low environmental regulation stringency, while developed
economies enforce strict environmental regulations to prevent pollution.
For example, Kalamova and Johnstone [4] pointed out that the rela
tively lax environmental stringency in 70 developing host countries
positively affects FDI inflows from 27 developed source countries. Fig. 1
also proves that from 1990 to 2014, while inward FDI of the BRICS
increased significantly, the degree of environmental policy stringency
was notably low, and the renewable energy deployment was also min
imal compared to the OECD (Organization for Economic Co-operation
and Development) countries.
Moreover, the carbon dioxide emissions of the BRICS nations
constituted 41.92% of the entire world in the last decade; China alone
comprised 28.03%. The coal consumption of the BRICS countries was
66.28%, and renewable energy consumption was just 29.76% of the
world’s total. Pioneering research found that carbon emissions in
developing countries could result from the increasing inflow of FDI,
known as the pollution haven hypothesis. For example, the most recent
work of Borga et al. [5] found that FDI was a substantial source of
emissions in 59 countries, and it was more prominent in the BRICS
economies, between 2005 and 2016. While the net zero emission com
mitments in most developing countries are merely in the discussion
stage, the commitments from Brazil and China are in the state of policy
documentation; and they are in the declaration level for India, Russia,
and South Africa.
Based on the above discussion, we can infer that the effects of FDI on
renewable energy consumption could be heavily influenced by envi
ronmental regulation stringency. To comply with the Paris Agreement,
intensifying the local environmental regulation might be beneficial to
discipline and guide multinational enterprises (MNEs) in renewable
consumption when FDI inflows. Nevertheless, overaggressive policies
might cause a sharp increase in the environmental compliance cost of
the MNEs, since it is widely argued that renewable energy sources are
expensive. Developing countries tend to be reluctant to raise environ
mental regulatory stringency when they put tremendous efforts into
attracting FDI. As a result, developing countries face a dilemma: How
and to what extent can environmental regulation stringency moderate
the impacts of FDI on stimulating renewable energy consumption are
still open questions.
Our research examines these questions and contributes to the pre
vious studies in the following three aspects: First, we provide an inte
grated theoretical framework to elaborate on the impact mechanisms
from FDI to renewable energy consumption regarding environmental
regulation stringency. Second, from a methodological standpoint, the
existing theories discussed in section 3 lead us to hypothesize a non-
linear relationship between FDI and renewable energy consumption
when the moderating role of environmental regulation is considered.
Therefore, we implement the threshold model to scrutinize the moder
ating impact features of two forms of environmental regulation, namely
formal and informal environmental regulation, on renewable energy
consumption from FDI. When [6]-the only work which considered the
moderating factor of FDI on renewable energy consumption-explored
the linear interaction effect between FDI and effective governance
(proxied by the Worldwide Governance Indicators), our research con
tributes to it by examining both the moderating effect and the threshold
effect of environmental regulation simultaneously. It enables us to
capture the non-linear impact of FDI on renewable energy consumption,
adjusted by environmental regulation. To provide more comprehensive
results, our research is also the first attempt to adopt HDI as informal
environmental regulation. Third, from a policy recommendation
standpoint, our findings can provide a distinct and proper level of
environmental regulation stringency for BRICS when FDI inflows, which
aims to encourage a low carbon route for BRICS countries.
In summary, to capture the non-linear effect of FDI on renewable
energy consumption under different levels of environmental policy
stringency, the panel threshold regressions were adopted using the
BRICS economies’ data from 1990 to 2015. The environmental policy
Fig. 1. FDI, renewable energy consumption, environmental policy stringency index of BRICS countries (1990–2015).
Source: Author’s calculations using data from the United Nations Conference on Trade and Development (UNCTAD), the International Energy Agency (IEA), and the
Organization for Economic Cooperation and Development (OECD).
Y. Tan and U. Uprasen

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stringency (EPS) index, developed by the OECD, was employed to
represent formal environmental regulation as it can capture multiple
dimensions of environmental regulation; it is comparable across coun
tries. In addition, an informal environmental index was also observed,
proxied by the Human Development Indicator (HDI). Furthermore, we
increased the robustness of our work by conducting additional estima
tions using the system GMM and the different GMM methods.
The paper is structured as follows: Section 2 provides a review of
related literature. Section 3 elaborates on our proposed theoretical
framework, hypotheses, and empirical model. Section 4 illustrates the
variables and data. Section 5 presents the empirical results and findings
discussion. Section 6 provides the robustness test by GMM. Section 7
concludes our research with specific policy recommendations.
2. Literature review
2.1. FDI and renewable energy consumption
The effect of FDI on energy consumption is generally conceptualized
through two competing hypotheses. First, the pollution haven hypoth
esis postulates that polluting enterprises attempt to shift their business
activities to developing countries to avoid strict environmental regula
tions in their respective home countries. Therefore, FDI induces non-
renewable energy consumption in the host country, implying that FDI
aggravates pollution through carbon emissions [7–10]. In contrast, the
pollution halo hypothesis advocates that FDI transfers green technology,
such as renewable energy–using technology, to a host country [11–13].
In other words, FDI contributes to renewable energy consumption in the
host country. However, there is no general agreement on the effect of
FDI on renewable energy consumption based on theoretical points of
view.
Concerning FDI’s mechanisms, the FDI-energy consumption nexus
can be explained through certain mechanisms, including the scale effect,
technique effect, and composition effect [14,15]. With the scale effect,
FDI’s energy consumption increases since it generally raises the level of
production of a host country [16]. A higher production level boosts up
non-renewable energy consumption, provided that the enterprise min
imizes its production cost by utilizing a cheaper resource [15,17]. In
other words, FDI discourages renewable energy consumption in host
countries through the scale effect [18]. Hence, the pollution haven hy
pothesis, in regard to the scale effect, postulates that FDI aggravates the
pollution in a host country [19,20]. Meanwhile, the technique effect
captures the effect of technology transfer emanating from FDI to a host
country. The FDI brings not only energy-efficient technology but also
environmentally friendly technology from a home to a host country,
according to the pollution halo hypothesis [21]. Consequently, the en
ergy will be efficiently utilized; that is, FDI lowers energy intensity in the
production procedures. Several empirical works affirm that FDI reduces
energy intensity in various case studies, including Turkey [22] and
China at the country level [23], provincial level [24], and firm level
[25]. The same findings are also found in the case of 13 East African
countries [26] and 60 developing countries [27]. Furthermore, the FDI
also fosters renewable energy consumption in a host country. Conse
quently, the technique effect improves the environmental quality [28].
For the composition effect, the effect of FDI on energy consumption is
undetermined and subject to the sectoral distribution of FDI [23]. While
an increase in the share of FDI in secondary sectors may raise
non-renewable energy consumption, the FDI growth in the tertiary
sector may lower non-renewable energy consumption. The pollution
haven hypothesis suggests that lax environmental regulation in devel
oping countries may attract polluting FDI into secondary sectors,
discouraging renewable energy consumption and negatively affecting
the environment. Although there is an extensive variety of research on
the association between FDI and environmental issues, studies focusing
on the effect of FDI on renewable energy consumption is scarce [6]. The
existing literature investigates the association between FDI and
renewable energy consumption, which can be discussed in three
perspectives.
2.1.1. Linear effect of FDI on renewable energy consumption
Both the theoretical viewpoints and the empirical findings show the
inconclusive consequence of FDI on renewable energy consumption.
Nevertheless, various works suggest a positive relation between FDI and
renewable energy consumption. By employing the bootstrap autore
gressive distributed lag technique, Samour et al. [29] found that FDI
significantly increased renewable energy consumption in the United
Arab Emirates (UAE) from 1989 to 2019. Mert and Boluj [30] also
observed a positive effect in the case of 21 Kyoto annex countries by
employing unbalanced panel data from 1970 to 2010 and in Kazakhstan
and Uzbekistan from 1992 to 2018 [31]. Using panel quantile regression
with 16 Asian countries from 1990 to 2019, Tiwari et al. [32] found that
FDI promotes renewable energy consumption among a group of
low-income nations compared to high-income countries. This phenom
enon results in the scarcity of funds in low-income countries, enabling
FDI to raise renewable energy consumption through investment. Using
the dynamic ARDL (DARDL) technique, Islam et al. [33] observed the
same result for Bangladesh from 1990 to 2019. Using the general
method of moments (GMM) and the pooled common correlated effects
(PCCE) methods, the panel estimations of Apergis and Pinar [34] re
ported FDI’s positive impact in 25 European Union countries from 2003
to 2017. The positive effects of FDI based on the panel estimations were
also observed by Amri [35] and Amuakwa-Mensah and Näsström [36] in
the case of 75 countries (1990–2010) and 124 countries (1998–2012),
respectively. Besides the research on the effect of the total FDI, Doytch
and Narayan [37] studied the impact of inward FDI on renewable energy
consumption at the sectoral level. A Blundell-Bond dynamic panel esti
mator was employed with the dataset of 74 countries from 1985 to 2012.
They discovered that FDI stimulates the shift from non-renewable to
renewable energy consumption in the service industry in high-income
nations. This finding implies the technique effect of FDI emanating
from soft technology transfer in the service sector, which supports the
pollution halo hypothesis. However, FDI shows insignificant effect on
renewable energy consumption in other industries including mining and
manufacturing sectors. Meanwhile, the causality of FDI to renewable
energy consumption was found by Khandker [38] in Bangladesh from
1980 to 2015, utilizing the Johansen co-integration technique and the
Granger causality test. Nevertheless, when the case of 31 Chinese
provinces was examined from 2000 to 2015, the unilateral causal rela
tion of FDI to renewable energy consumption was found only in the long
term; the relationship did not exist in the short run [39].
However, certain works showed a negative association between FDI
and renewable energy consumption. Kang et al. [40] examined the case
of South Asian countries by employing the fully modified ordinary least
squares (FMOLS) regression and dynamic ordinary least squares (DOLS)
models from 1990 to 2019. The study revealed the negative impact of
FDI on renewable energy consumption: the one percent increase in FDI
reduces renewable energy consumption by 3.36%. Furthermore, the
findings of Sbia et al. [41] confirmed that FDI decreased the demand for
clean energy in the UAE, using the autoregressive distributed lag (ARDL)
method with the quarterly data from 1975Q1 to 2011Q4. The negative
effect of FDI on renewable energy consumption was also found among
the European Union countries using the dataset between 2001 and 2015
[42]. The similar negative impacts of FDI, using the panel models, were
also reported by Zhang et al. [43] and Alsagr and Van Hemmen [44] in
the case of 35 OECD countries (1999–2018) and 19 emerging countries
(1996–2015), respectively. Additionally, Paramati et al. [45] incorpo
rated the issues of heterogeneity, together with cross-sectional depen
dence, in their analysis and used the dataset of 20 emerging economies
between 1991 and 2012. The results from the panel estimation exhibited
that a one percent raise in FDI inflows promotes clean energy con
sumption by 0.07%. Noteworthily, the outcome of each country shows
different consequences. While FDI encourages clean energy

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consumption in Hungary, Mexico, Russia, and South Africa, it lowers
clean energy consumption in Chile, Greece, Malaysia, Peru, Poland, and
Turkey. FDI has no impact on clean energy consumption in the rest of the
countries, including China, India, and Korea. Generally, the negative
consequence of FDI on renewable energy consumption is commonly
explained by the fact that FDI improves technological innovation by
increasing production efficiency, which helps lower renewable energy
consumption [46]. Meanwhile, Grabara et al. [31] posited that the
negative impact of FDI renewable energy consumption in Kazakhstan
and Uzbekistan from 1992 to 2018 reflects the composition effect of FDI
because most inward FDIs go to highly polluting industries, such as the
mining sector.
Furthermore, the insignificant effect of FDI on renewable energy
consumption was also found. By employing the fixed effects models in
the case of 19 countries of the G20 group from 1971 to 2009, Lee [47]
postulated that the positive association between FDI and the clean en
ergy consumption is detected only in a bivariate setting regression. On
the other hand, the result from a multivariate setting regression showed
an insignificant impact on FDI. The study further argued that FDI does
not stimulate renewable energy utilization. The positive impact of FDI
on clean energy consumption in a bivariate setting regression is
explained through omitted variables bias. Moreover, using the dataset of
71 low and middle-income countries from 2000 to 2017, Murshed [48]
also reported an insignificant effect of FDI on renewable energy. The
work of Zeng and Yue [49] scrutinized the effect of FDI on the renewable
energy consumption of the BRICS countries. By employing the panel
ARDL-PMG method, the study found a negative effect on FDI from 1991
to 2019. Both Murshed [48] and Zeng and Yue [49] explained that this
effect resulted from most FDI going to non-renewable energy–intensive
industries in the studied countries.
2.1.2. Nonlinear effect of FDI on renewable energy consumption
Shahbaz et al. [50] adopted the cross-sectional autoregressive
distributional lag (CS-ARDL) model using the data of 39 countries from
2000 to 2019. The estimation between renewable energy, FDI, and the
squared term of FDI reveals a U-shaped association between FDI and
renewable energy consumption. The reason is that the scale effect is
dominant in the early stage, and the technique effect tends to play a
significant role in the later stage due to technological transformation. In
addition, Qin and Ozturk [18] found an asymmetric effect of FDI on
renewable energy consumption by utilizing a time series nonlinear
autoregressive distributed lag (NARDL) model for the case of the BRICS
countries from 1991 to 2019. The results indicated that while a positive
change in FDI has a positive impact on renewable energy consumption
in South Africa, Brazil, and India, the negative change in FDI shows a
negative effect on renewable energy consumption in India, China,
Brazil, and South Africa. Furthermore, the asymmetric effects were also
examined in BRICS economies by Yilanci et al. [51] using the Fourier
autoregressive distributive lag (FADL) model from 1985 to 2017. The
results from the negative components revealed that while FDI shows a
positive effect on clean energy consumption for Russia, with a coeffi
cient of 0.01, it has an insignificant effect on China and South Africa. On
the other hand, Zhang et al. [52] employed the NARDL model to
investigate the BRICS nations using data from 1997Q1-2018Q4. They
found that FDI positively affects renewable energy consumption.
Regarding the panel estimations, Qamruzzaman and Jianguo [53] used
the NARDL model to examine three groups of panel models, categorized
according to income level from 1990 to 2017. The empirical results
confirmed the long-term asymmetric link between FDI and renewable
energy consumption in all three studied groups.
2.1.3. The nonlinear and moderating effects of FDI on renewable energy
consumption
Research exploring the triadic relationship among FDI, renewable
energy consumption, and certain intervening variables is remarkably
insufficient. Few works investigated the effect of FDI on the level of
pollution. Using provincial panel data of China from 2000 to 2014,
Wang and Liu [54] found that a stricter environmental regulation de
creases pollution from FDI because the lower level of an environmental
protection rule makes polluting multinational enterprises (MNEs) enter
China, raising the pollution level. Meanwhile, stricter environmental
regulation will discourage polluting MNEs and stimulate them to
improve technology that reduces pollution to comply with the regula
tion. In their study, pollution was proxied by the index of environmental
pollution, calculated from six pollutants, such as wastewater discharge,
sulfur dioxide emissions, and others. In addition, Wang et al. [55] uti
lized the panel dataset of 276 cities in China from 2004 to 2018 with the
generalized spatial two-stage least squares technique. They postulated
that the higher level of fiscal decentralization lowers the effect of FDI on
haze pollution. A similar finding was claimed by Huang et al. [56]. By
employing the feasible generalized least squares with data of G20
countries between 1996 and 2018, Huang et al. claimed that higher level
of regulatory quality reduces carbon emissions emanating from FDI.
There is only one study investigating the effect of FDI on renewable
energy consumption and intervening variables. In particular, it exam
ined the linear effect of FDI on renewable energy consumption. Yahya
and Rafiq [6] employed the system generalized method of moment
technique with the dataset from 68 countries from 2013 to 2014. The
empirical results indicated that effective governance, proxied by the
Worldwide Governance Indicators [57], strengthens the positive impact
of greenfield FDI on the renewable energy consumption of low-risk
nations and weakens the negative effects of greenfield FDI on renew
able energy consumption in high-risk economies. This phenomenon
results from the fact that well-regulated and systematized governments
force the MNEs to employ eco-friendly technologies involving renew
able energy. Moreover, the government may implement supporting
policies to promote renewable energy utilization among MNEs, such as
tax credits and loans for renewable energy projects.
2.2. Environmental regulation and renewable energy consumption
The rise in global environmental awareness compelled several
countries to implement various environmental regulations, which are
essential factors in reducing fossil fuels and greenhouse gas emissions
[58]. The Paris Agreement requires both developed and developing
countries to take responsibility for reducing carbon dioxide emissions
and increasing renewable energy deployment. The renewable goals in
the NDCs must be converted into national laws and implemented
properly. An effective environmental regulation fosters clean energy
production [14]. In addition, the interconnections among FDI, pollution,
and environmental regulation suggest that the contribution of FDI to
production processes, either through productivity boosting [59] or
technology transfer [60], raises the income level in a host country,
prompting people to demand higher environmental quality. Thus,
environmental regulations accordingly become more stringent [9].
Generally, the effect of environmental regulation on environmental
quality and energy consumption can be explained through two major
hypotheses.
First, the cost of compliance hypothesis asserts that enforcing envi
ronmental regulations raises an enterprise’s compliance cost. The
compliance cost, such as the operating pollution control equipment, is a
burden leading to decreased energy consumption and lower
manufacturing output [61,62]. In addition, the rise in production costs
decreases a company’s profit [63,64]. As a result, the reduction in an
enterprise’s profit restricts the opportunity for technological innovation
of production [65–68]. Hence, the application of environmental regu
lation discourages the consumption of renewable energy.
Alternatively, the Porter hypothesis states that when the imple
mentation of environmental regulation intensifies to an appropriate
degree, it will stimulate an enterprise to increase research and devel
opment (R&D) activity, thereby achieving advanced production tech
nology and environmentally friendly technology to comply with the

139
regulation. This innovation will offset the compliance cost and acquire
cleaner production technology [69,70]. This mechanism enables enter
prises to switch from traditional fuel to clean energy utilization.
Therefore, the implementation of environmental regulations encourages
the consumption of renewable energy.
Several empirical works supported the prediction of the Porter hy
pothesis. By employing the OECD environmental policy stringency index
(EPS), Santis and Lasinio [71] and Martínez-Zarzoso et al. [72] claimed
that a stricter environmental policy encourages overall innovation.
Concerning the impact of environmental rules on environmental inno
vation, Kemenade and Teixeira [73] found that environmental policy
stringency shows an insignificant consequence on environmental inno
vation, while certain works [74–77] revealed a positive effect. In addi
tion, the other empirical works found that strict environmental
regulation compels firms to shift from dirty technology to clean tech
nological innovation [64,78–80].
Concerning the link between regulations and the environment, the
existing literature mostly studies the association between environmental
regulation and pollution [61,81–85], leaving scant research on the as
sociation between regulation and renewable energy consumption.
Nevertheless, empirical works investigating the consequence of envi
ronmental regulation on renewable energy consumption are found in
two perspectives—formal and informal environmental regulations.
2.2.1. Formal environmental regulation and renewable energy consumption
Generally, environmental regulations comprise formal and informal
regulations. Formal environmental regulations may refer to the
discharge permit system, emission taxes, and penalties implemented by
the government on business units to achieve sustainable development
[86]. The proxy variable of formal environmental regulation is used in
various works. While Brunneimer and Cohen [87] and Hamamoto [88]
employed pollution abatement and control expenditures (PACE), Yang
et al. [89] adopted PACE and pollution abatement fees (PAF) to repre
sent formal regulations. Additionally, environmental regulation can be
measured through a single index [90], policy shock [91], or compre
hensive index [85,92,93]. Wang and Shao [74] employed the OECD EPS,
as it contains multidimensional environmental stringency compared to
the previous variables, such as the PACE and PAF.
The existing works on the impact of environmental regulation on
renewable energy consumption are very limited. Zhao et al. [94]
investigated the case of China using the panel model of 286 cities from
2003 to 2018. The environmental regulation variable was calculated
based on the fuzzy integrated evaluation method. The study found that
environmental regulation promotes both the quantity and share of
renewable electricity consumption in China. The research concluded
that there is a linear relationship between regulation and renewable
energy consumption; stricter regulation raises renewable energy
consumption.
The study investigating the case of the BRICST (Brazil, Russia, India,
China, South Africa, and Turkey) nations also indicated a positive effect
of environmental regulation on renewable energy consumption. The
research was conducted by Li et al. [95] using the data from 1991 to
2019, adopting the fixed effects regression and panel quantile models.
The environmental regulation was proxied by the OECD EPS index.
While the result from the fixed effects model showed an insignificant
effect, the significant effect of the regulation on renewable energy
consumption was reported using the panel quantile model. The research
found that environmental regulatory stringency promotes renewable
energy consumption at lower quantiles and hinders the consumption of
renewable energy at higher quantiles. This phenomenon can be
explained by the fact that at lower quantiles, the level of renewable
energy consumption is low; environmental regulations are enforced
strictly relative to higher quantiles.
Furthermore, the negative association between environmental
regulation on renewable energy consumption was reported by Bashir
et al. [96]. They investigated the case of 29 OECD countries using the
dataset from 1996 to 2018 with three techniques: fully modified ordi
nary least squares (FMOLS), OLS fixed effects models, and panel quantile
regressions. The OECD EPS index was employed in the study. The results
showed a negative impact of environmental regulation on renewable
energy consumption in all three methods. The authors claimed that the
negative relationship implies the ineffectiveness of recent environ
mental regulations in the OECD countries.
2.2.2. Informal environmental regulation and renewable energy
consumption
Besides the formal environmental regulations, certain informal reg
ulatory measures can also influence the activities of polluters. Informal
regulations generally do not act on a firm directly and raise the cost of
production [81]. Pargal and Wheeler [97] postulated that the inade
quate implementation of formal environmental regulations by the gov
ernment triggers social groups to request enterprises to control
pollution. Informal environmental regulations are not enacted by the
authorities. Instead, it is public environmental consciousness and
awareness that put efforts to monitor enterprises’ business activities and
demand good environmental quality through complaints, petition let
ters, demonstrations, or boycotts of the products of polluting firms.
Since enterprises think about their social reputations, they will manage
to reduce pollution [98]. Further, various variables are used to represent
informal environmental regulations. Xie et al. [99] investigated the ef
fect of an informal environmental regulation on environmental total
productivity growth of 30 provinces of China using the education level
of employees and the number of public complaints about pollution. They
found that only education level shows a positive effect on green growth.
Peng and Ji [100] stipulated that informal regulation, proxied by the
information disclosure policy (EIDP), positively affects green innovation
in China. Moreover, the positive consequences of informal environ
mental controls on the green growth of the G20 nations were claimed by
Wang and Shao [74], using the proportion of tertiary education
enrollment and the ratio of environment-related technology patents.
Using employees’ average salary, the ratio of employees with college
degrees, and the number of environmental non-governmental organi
zations per 10,000 individuals, Xiong and Wang [101] posited that
informal environmental regulations could lower industrial solid waste in
China.
Furthermore, Langpap and Shimshack [102] adopted filing records
of the citizens regarding environmental issues to scrutinize the influence
of the private sector on the public enforcement of environmental regu
lations. Kathuria [103] used several articles on the environment from
public media to observe whether the press can be a pollution controller
in India. Moreover, Goldar and Banerjee [104] applied the poll per
centage in parliamentary elections to represent and evaluate the effect of
informal regulation on water quality in India. Li et al. [101] utilized the
environmental petition index in their study on industrial transfer.
Our research is motivated by what has not yet been studied con
cerning the interplay among FDI, formal environmental regulation,
informal environmental regulation, and renewable energy consumption.
Based on the literature, two important research gaps are found. First, the
relationship between FDI and consumption of renewable energy remains
inconclusive. Second, the role of the intervening factor on FDI and
renewable energy consumption requires more investigation. Only one
research [6] scrutinized the impact of FDI on renewable energy con
sumption by considering the role of effective governance as a moder
ating variable. The research gaps from the previous literature leave
unresolved questions for policy implementation in host countries. In
addition, attracting more FDI while protecting the environment and
complying with the Paris Agreement by strengthening environmental
regulations seem to be a dilemma for developing countries, such as
BRICS, despite disapproving of the “pollute first and clean up later”
policy. The question of how the environment can be effectively managed
in these emerging economies, which rely heavily on FDI, still needs to be
addressed. Further, there are insufficient existing empirical works that

140
provide guidelines regarding the degree of environmental regulatory
stringency that can be applied in developing countries, such as the
BRICS countries, striving to attract FDI and preserve the environment
simultaneously. Hence, our research attempted to provide guidelines on
environmental regulation implementation through empirical studies
using Hansen’s endogenous panel threshold model [105] based on Hy
potheses 1 and 2, discussed in the next section.
3. Theoretical framework and model
3.1. Formal environmental regulation, FDI, and renewable energy
consumption
Based on the aforementioned theories, we hypothesized that there is
a nonlinear impact of FDI on renewable energy consumption. The effect
is contingent on the degree of the implementation of formal environ
mental regulation. When the degree of environmental regulation strin
gency is low, the impact of FDI on renewable energy consumption is
mainly influenced by the scale effect and the composition effect. In
addition, the possibility of having green innovation is low, as postulated
by the cost of compliance hypothesis. Therefore, FDI tends to reduce
renewable energy consumption. However, when the degree of envi
ronmental regulatory stringency is high, the technique effect will be
stronger than the scale effect and the composition effect, as argued by
the Porter hypothesis, eventually leading to a rise in renewable energy
consumption. The interaction among FDI, formal environmental regu
lation, and renewable energy consumption are presented in Fig. 2.
Overall, FDI raises renewable energy consumption when the stringency
of environmental regulation is higher than a certain threshold level.
Accordingly, our hypothesis is expressed below.
Hypothesis 1. FDI enhances renewable energy consumption after a
certain threshold level of formal environmental regulatory stringency of
a host country.
We employed the threshold panel model to scrutinize the heteroge
neous influence of FDI on renewable energy consumption under
different levels of formal environmental policy stringency. In addition,
we conducted Hansen’s endogenous threshold model to yield more
flexible specifications. This model provides certain advantages, such as
avoiding a potential bias from an artificial threshold. In addition, it
allowed us to examine the existence of a threshold value and obtain the
exact value of the threshold. It is also believed that utilizing this model is
more efficient than simply implementing an ad hoc and arbitrary
method exogenously by dividing the samples or conducting a linear
interaction term between FDI and formal regulation. The EPS was used
as a threshold variable in our research. According to our proposed the
ory in Fig. 2, the single-threshold model, taking the EPS as the threshold
variable, is specified as Equation (1), where Ln represents the natural
logarithm:
LnREWit = λ0 + λ1LnGDPPCit + λ2LnTRADEit + λ3LnCREDITit + λ4LnURBANit
+λ5LnFDIitI(LnEPSit ≤ θ1) + λ6FDIitI(LnEPSit > θ1) + μi + εit
(1)
where REW refers to renewable energy consumption, and EPS (envi
ronmental policy stringency index) represents environmental regulatory
stringency. We also incorporated GDP per capita (GDPPC), trade open
ness (TRADE), urbanization rate (URBAN), and financial development
(CREDIT) as explanatory variables to observe the important determining
factors of renewable energy consumption of the BRICS nations.
EPS indicates the threshold variable, θ presents the threshold value
to be estimated, and I(.) is an indicator function. Two main steps are
applied when implementing the threshold model: examining whether
the threshold effect exists and calculating the confidence interval of the
threshold value.
First, the null hypothesis (H0 : λ5 = λ6) and the alternative hypoth
esis (H1 : λ5 ∕
= λ6) were proposed to test the existence of a threshold
effect in the model. A single threshold effect nonlinear regression can be
confirmed, if the null hypothesis is rejected. Then, the F-statistic is
written as below:
F1 =
S0 − S1
(
r!!
)
ρ!!2
where S1 indicates the residual sum of the squared errors with threshold
effects, while S0 indicates that without threshold effects. The term ρ!!2
=
S1(γ!!
)/n indicates the variance of the error term. According to the null
hypothesis (H0 : λ5 = λ6), the threshold value cannot be detected, and F1
shows a nonstandard asymptotic distribution. In this regard, Hansen
[105] exploited the repetitive bootstrap method to determine the critical
values of the F-statistic. Using the bootstrap method allows for the dis
tribution of the F-statistic asymptotic, and the threshold effect can be
detected by calculating the asymptotic p-values, if it is significant.
The second step involves determining the confidence interval of the
threshold values. The γ!!
represents a consistent estimator of γ, and the
null hypothesis (H0) is set to determine the confidence interval by
implementing the non-rejection interval approach with a likelihood
ratio statistic. The equation is as follows:
Fig. 2. FDI Under formal regulation.

141
LR1(γ) =
LR1(γ) − LR1
(
γ!!
)
ρ!!2
The multiple panel thresholds model can be estimated similarly to
the single threshold model. Specifically, the significance of the double
threshold effect can be examined according to a single threshold model.
If the null hypothesis of the single threshold is rejected, this model will
act as a double threshold model. This same procedure can also be
applied to the triple threshold effect. The test of a triple threshold model
will be implemented when the significance of the double threshold
model effect is rejected. To ensure the robustness of our empirical
model, we incorporated other control variables in the model (i.e., GDP
per capita, trade openness, financial development, and urbanization).
After examining the existence of the threshold effect and estimating the
threshold value of the EPS, a single threshold was detected. The results
are presented in Table 3.
3.2. Informal environmental regulation, FDI, and renewable energy
consumption
Compared with formal environmental rules, informal regulations
emanating from public environmental awareness affect enterprises by
considering their social reputation. A bad social reputation of an en
terprise may influence its performance and profit, such as a decrease in
sales volume due to social movement boycotts. Therefore, informal
regulation does not act on the firm directly and raises the cost of pro
duction. As a result, a host country with low public awareness of the
environment attracts foreign polluting enterprises. The composition and
scale effects become dominant effects, discouraging renewable energy
consumption. Nevertheless, a high degree of public awareness of the
environment reduces a host country’s attractiveness from the viewpoint
of foreign polluting firms. Thus, enterprises attempt to improve their
social reputations by developing green technologies. Consequently, the
technique effect becomes dominant, which fosters renewable energy
consumption. The effect of FDI on the consumption of renewable energy,
contingent on the moderating role of informal environmental regula
tion, is shown in Fig. 3. Accordingly, we hypothesized that FDI has a
nonlinear impact on renewable energy consumption, subject to the
moderating role of public environmental awareness, expressed as Hy
pothesis 2, as follows:
Hypothesis 2. FDI reinforces the consumption of renewable energy
after a certain threshold level of informal environmental regulatory
stringency of a host country.
The threshold model was also applied for the case of informal envi
ronmental regulation to test Hypothesis 2. As shown in Table 3, a single-
threshold effect is also observed when setting HDI as a threshold vari
able. Hence, the corresponding equation for testing Hypothesis 2 can be
written as follows:
LnREWit = λ0 + λ1LnGDPPCit + λ2LnTRADEit + λ3LnCREDITit + λ4LnURBANit
+λ5LnFDIitI(LnHDIit ≤ θ1) + λ6FDIitI(LnHDIit > θ1) + μi + εit
(2)
4. Variables and data
4.1. Dependent variable
Renewable energy consumption (REW), the dependent variable in
our research, refers to the percentage of total energy consumption. The
appropriate features of the selected independent variable were set by
certain pioneering studies, including Tugcu [106], Wu et al. [107], Iqbal
et al. [108], and Namahoro et al. [109]. The related independent vari
ables were adopted based on the related hypotheses, as discussed in the
literature review in Section 2. The main independent variable in our
study is FDI, defined as the net foreign direct investment inflows as the
percentage of GDP. Environmental regulation was adopted as a
moderating variable to test Hypothesis 1 and Hypothesis 2. Further
more, environmental regulation is categorized into formal and informal
regulations to scrutinize its roles in moderating the impact of FDI on
renewable energy consumption.
4.2. Threshold variables
The following variables were generally adopted in the previous
research as formal regulations: pollution abatement and control ex
penditures (PACE), pollutant discharge fees, and pollution abatement
fees (PAF) [87–89,99]. Meanwhile, our study used the environmental
policy stringency index (EPS) from the OECD database to represent the
level of formal environmental regulations. The index captures the level
of policy stringency on environmentally hazardous activities. Compared
to the above-mentioned formal environmental regulation approaches,
the index offers the following advantages. First, it is a composite index
that captures both market and non-market environmental policy mea
sures, including emission trading schemes, feed-in tariffs,
environment-related taxes, subsidies in R&D, and environmental stan
dards. Second, the index measures every aspect with specific indicators.
Fifteen environmental policy instruments are utilized to evaluate the
level of environmental policies’ stringency. Notably, the EPS takes
Fig. 3. FDI undre informal regulation.

142
renewable energy issues into account, such as public expenditures on
R&D in renewable energy and renewable energy certificates for trading
schemes. Therefore, with the implications offered by multidimensional
environmental policies, the EPS becomes the first tangible policy
mechanism to assess environmental policy stringency and provide
further policy implications and reforms [110–112].
Furthermore, pioneering researchers adopted different variables to
proxy informal environmental regulations. For instance, green con
sciousness, citizen suit records, the number of articles about contami
nation in public media, the number of public complaints related to
pollution activities, and the educational status of employees [99,
102–104]. However, those indexes focus on certain issues, such as ed
ucation or suit records. Considering the limitations of the previous in
dexes, we adopted the Human Development Index (HDI) [113] in our
work as a proxy for informal regulation. The HDI was invented by the
United Nations Development Programme (UNDP) in 1990 as a multi
dimensional tool. It captures a country’s development in both social and
economic aspects. Three key dimensions are considered when estab
lishing the index: life expectancy and health, education opportunities,
and a proper standard of living. Consequently, the life expectancy index,
education index, and gross national income (GNI) are taken into account
when estimating the HDI. Therefore, it provides a more ideal and
comprehensive measurement than the indexes mentioned in the previ
ous literature.
4.3. Control variables
4.3.1. Real income per capita
Numerous pioneering papers have examined the impact of income on
energy consumption. The literature can be divided into four groups
based on the arguments of certain hypotheses: the growth hypothesis
(GH), the conservation hypothesis (CH), the feedback hypothesis (FH),
and the neutrality hypothesis (NH). The existing works show different
findings of the predictions of certain hypotheses than the ones
mentioned above. Some empirical evidence supported that real income
had a positive impact on renewable energy consumption, which support
GH [34,106,114–119] [120,121]. A few works documented the negative
impact of GDP on renewable energy consumption, such as [122]. NH can
also be observed in some research, such as Bulut and Inglesi-Lotz [123]
and Hossain [124]; that is, there was no causal relationship between
income and energy consumption.
4.3.2. Trade openness
Trade openness has been affirmed as a major driver of renewable
energy consumption. Nevertheless, empirical evidence on the relation
ship between them varies. According to Grossman and Krueger [125],
the effect of trade openness on energy consumption is explained through
the scale effect, the composition effect, and the technology effect. The
scale effect indicates that energy consumption can increase when trade
induces economic growth and cross-border transportation service. The
composition effect is caused by specialization in an industry emanating
from competitive advantage. The technology effect comes from the
inflow of technology from developed to developing countries through
trade openness. These effects encourage the adoption of renewable en
ergy in developing countries. Notably, a considerable amount of
research on developing countries supported that trade openness accel
erated renewable energy consumption [6,114,117,126–129].
4.3.3. Financial development
The existing empirical works reveal financial development as one of
the major determinants of renewable energy consumption; however,
they show conflicting results. The positive effects of financial develop
ment on renewable energy consumption were found by various studies,
including Burakov [130] for Russia (1990–2018), Wang et al. [131] for
China (1992–2013), and Lahiani et al. [132] for USA (1975–2019).
Similar findings were also documented by research studies focusing on
certain country groups, such as Kutan et al. [133] for the BRICS coun
tries from 1990 to 2012, Anton and Nucu [46] for 28 European countries
from 1990 to 2021, Wu and Broadstock [134] for 22 emerging econo
mies from 1990 to 2010, and Kim and Park [135] for 30 selected
countries from 2000 to 2013. The general explanation for the positive
impact of financial development on renewable energy consumption is
expounded on by Song et al. [136]. They argued that the funds provided
by the banking sector to the market would facilitate and promote the
demand and consumption of renewable energy. In addition, Ekanayake
and Tharver [137] proposed that further financial development is
strongly connected with economic growth, economic efficiency, and
capital accumulation, resulting in increased renewable energy con
sumption. On the contrary, certain empirical works found that financial
development led to the increasing usage of non-renewable energy over
renewable energy. For example, the work of Islam et al. [138] reported
that financial development accelerated the demand for non-renewable
energy in Malaysia from 1971 to 2009. Çoban and Topcu [139] found
a similar outcome for European countries from 1990 to 2011. The same
evidence was claimed by Sadorsky [140], Omri and Kahouli [141],
Shahbaz et al. [142], and Chiu and Lee [143].
4.3.4. Urbanization
There are various studies on the relationship between urbanization
and energy consumption. Some pioneering works pointed out that ur
banization is a strong driving force of renewable energy. For example,
Shahbaz and Lean [144] examined the case of Tunisia and found that
urbanization strengthens the role of financial development on renew
able energy consumption. In addition, Chen [127] argued that urbani
zation had a significant positive impact on renewable energy
consumption in China, while Yang et al. [145] concluded that the in
crease in renewable energy consumption could be attributed to the rapid
urbanization in China. However, urbanization contributes to the con
sumption of fossil fuel energy rather than renewable energy. Sharma
et al. [146] also pointed out that urbanization is a significant driver of
renewable energy consumption in South and Southeast Asian countries.
In summary, while the GDPPC normally shows a positive impact on
renewable energy consumption, other factors may have uncertain
effects.
4.4. Data description
The samples of our study are from the BRICS nations: Brazil, Russia,
India, China, as well as South Africa, considering that the group has
become fast-emerging economies in terms of the total population, GDP,
Table 1
Variable definitions and explanations.
Variable
category
Notation Description Source
Explained REW Renewable consumption, %
of total energy consumption
UNCTADSTAT1
,
WDI3
Core
explanatory
variable
FDI Foreign direct investment
net inflow, % of GDP
UNCTADSTAT1
Threshold #1 EPS Environmental Policy
Stringency Index
OECD2
,
Threshold #2 HDI Human Development Index UNDP4
,
Other control
variables
GDPPC GDP per capita, constant
2015 US$
WDI3
TRADE Trade openness, % of GDP WDI3
CREDIT Domestic credit to private
sector by banks, % of GDP
WDI3
URBAN Urbanization rate, % of the
total population
WDI3
Notes: 1. United Nations Conference on Trade and Development database; 2.
OECD statistics database; 3. World Development Indicator database; 4. United
Nations Development Programme database.

143
energy consumption, and carbon emissions. Correspondingly, the panel
data of the five countries from 1990 to 2015 were utilized. The variable
definitions, explanations, and data sources are shown in Table 1.
Table 2 presents the descriptive analysis of the dataset, including the
number of observations, the unit, mean value, standard deviation, and
the minimum and maximum values of each variable.
5. Empirical results and discussions
5.1. Threshold effect significance test and the threshold value
Before estimating the nonlinear effect of FDI on renewable energy
consumption, it is necessary to examine the existence of a threshold level
and determine the number of the threshold afterward by employing the
likelihood ratio (LR) test statistics. To gain the LR statistics and the P-
values, we implemented the self-sampling method (i.e., the Bootstrap
method) by drawing the sample data 1000 times repeatedly. The F-
statistics and P-values of the threshold effect based on the Bootstrap
method are reported in Table 3. A single threshold effect was confirmed
for formal regulation (LnEPS) at a 1% significance level, while two and
three threshold effects were insignificant. Therefore, the interplays
among renewable energy consumption, FDI, and a threshold effect
emanating from environmental regulation can be specified as a single
threshold effect model in Equation (1). A single threshold effect was also
verified for informal regulation (LnHDI) at a 5% significance level.
Hence, Equation (2) was determined.
Simultaneously, the threshold value θ with a minimum residual sum
of squares was acquired through the Bootstrap method. The threshold
values in logarithmic forms of formal and informal environmental
measures (i.e., − 0.7483 and − 0.4370, respectively) are reported in
Table 4. The logarithmic values were transformed into true values,
which are 0.4731 and 0.6459, in the third column of the same table.
5.2. Threshold regression analysis
The impact of FDI on renewable energy consumption was explored in
our research by taking formal and informal environmental policies as
moderating variables and threshold variables simultaneously. We
introduced the threshold values into Equations (1) and (2), and the
model coefficients were therefore estimated, as shown in Table 5.
Regarding the nonlinear association between FDI and renewable
energy consumption when taking formal environmental regulation as a
moderating variable, a single threshold value of 0.4731 is observed in
Equation (1) (model 1). If the level of formal regulation (LnEPS) is lower
than the threshold value of − 0.7483 (true value 0.4731), the estimated
coefficient of FDI is 0.0210 with a negative sign. It reveals that
Table 2
Descriptive analysis.
Variable Unit Obs. Mean Std. Dev. Min Max
REW % of total energy consumption 130 10.7117 12.4569 0.0344 37.3558
GDPPC Constant 2015US $ 130 4839.7420 2783.0594 527.5145 9621.5099
FDI % of GDP 130 2.0176 1.5523 − 0.0655 6.1869
TRADE % of GDP 130 40.6577 15.7302 15.1556 110.5771
CREDIT % of GDP 130 54.4503 22.8167 25.5470 133.0759
URBAN % of the total population 130 56.3231 20.3644 25.5470 85.7700
EPS – 130 0.6504 0.3894 0.2500 2.1625
HDI – 130 0.64456 0.0910 0.4298 0.8091
Source: Authors’ computations based on the datasets listed in Table 1.
Table 3
Threshold effect test results.
Threshold Hypothesis F-stat. P-value Critical value
0.10 0.05 0.01
EPS Single threshold 22.9656 0.0000 10.2675 12.5155 14.4509
Two thresholds 5.7114 0.2300 7.4634 9.1378 12.0447
Three thresholds 2.2164 0.7800 19.5286 29.9452 44.3450
HDI Single threshold 18.4246 0.0500 16.9512 18.3994 49.5325
Two thresholds 34.9617 0.1200 37.2324 59.5963 68.1281
Three thresholds 4.1936 0.8100 27.7462 34.4674 44.4633
Table 4
The threshold value and confidence interval.
Threshold Variable Ln value True value 95% confidence interval
LnEPS − 0.7483 0.4731 [-0.8805, − 0.7357]
LnHDI − 0.4370 0.6459 [-1.6855, 0.8317]
Table 5
Threshold estimated coefficient.
Variable Model 1 Model 2
LnGDPPC 0.5066** 0.8853**
(2.6074) (2.6468)
LnTRADE 0.2900** 0.2229***
(2.3063) (4.0423)
LnCREDIT 0.0564** 0.1577
(2.1057) (1.1715)
LnURBAN 2.6403*** 2.9173***
(4.2375) 4.7962)
LnFDI(LnEPS ≤ -0.7483) − 0.0210*
(-1.8328)
LnFDI(LnEPS > -0.7483) 0.1340***
(4.3814)
LnFDI(LnHDI ≤ -0.4370) − 0.0886***
(-3.1756)
LnFDI(LnHDI > -0.4370) 0.0374***
(8.6456)
Constant − 3.9597*** − 4.6015***
(-3.1846) (-3.7954)
Observations 130 130
R2
0.2911 0.3357
Note: The symbol ***, **, and * indicate the 1%, 5%, and 10% levels of sig
nificance, respectively. The t-statistics are presented in parentheses.

144
renewable energy consumption will be reduced by approximately
0.0210% when the FDI inflow increases by 1%. When the value of LnEPS
exceeds the threshold level of − 0.7383 (which indicates more stringent
formal regulation), the coefficient of FDI changes to 0.1340 with a
positive sign. Thus, a 1% increase in FDI fosters renewable energy
consumption by 0.1340%.
Regarding the consequence of FDI on renewable energy consumption
when taking informal environmental regulation as a moderating vari
able as expressed in Equation (2) (Model 2), a detected single threshold
value in logarithmic form (LnHDI) is − 0.4370 (equivalent to the true
value at 0.6459). The corresponding empirical results are reported in
Table 5. While the degree of stringency of informal environmental rules
is lower than the threshold value, FDI shows a negative impact on
renewable energy consumption. The estimated coefficient reveals that a
1% increase in FDI inflow results in a 0.0886% decrease in renewable
energy consumption. Nonetheless, FDI promotes renewable energy
consumption if the degree of regulatory stringency is higher than the
threshold level. The obtained results illustrate that a 0.0374% increase
in renewable energy consumption is attributed to a 1% increase in FDI
inflow.
5.3. Discussion on the variational relationship between FDI and
environmental regulation
The findings suggest that FDI can have heterogeneous impact on
renewable energy consumption at different level of environmental
regulation stringency. As Hypothesis 1 stated in last section, the FDI
would influence on renewable energy consumption in two directions
accompanied by environmental regulation stringency. The explanations
are twofold. On one hand, the lax environmental regulation below the
threshold value would result in a higher degree for scale effect and the
composition effect of FDI which would discourage the use of renewable
energy consumption. Thus, the pollution haven hypothesis is justifiable.
On the other hand, intensifying the environmental regulation leads to a
dominant role of technique effect of FDI, which could encourage the
renewable energy consumption. Therefore, the pollution halo hypoth
esis is justified in this circumstance.
The empirical findings in Table 5 justify Hypothesis 2 (i.e., the
nonlinear effect of FDI). When the degree of public awareness of the
environment is low, a host country attracts more polluting MNEs. The
composition and scale effects play significant roles in the effect of FDI on
renewable energy consumption. Further, with a high level of environ
mental and social awareness, firms are stimulated to improve green
technology to meet social expectations, helping them avoid any poten
tial business loss, such as a drop in profits emanating from social
movement boycotts.
Our empirical results are partially consistent with certain existing
works. Using FDI and FDI squared as explanatory variables, Shahbaz
[62] found that the relation between FDI and renewable energy con
sumption showed a U-shaped function in 39 selected countries. The
result was explained to be consequent of the fact that the scale effect of
FDI was overridden by the technique effect, which encouraged demand
for renewable energy consumption. Likewise, studying the case of
China, Wang and Liu [65] argued that a lower level of environmental
rule attracted heavy-polluting MNEs to enter the region. Nonetheless,
when the level of the environmental regulatory measure was stricter, the
area was attractive to the polluting firms and encouraged green tech
nological innovation of the MNEs.
However, they both failed to explore the impact of FDI on renewable
energy consumption considering informal environmental regulations as
a moderating effect. In other words, they did not address the issue of the
extent of the informal environmental regulatory stringency in stimu
lating the technique effect of FDI on renewable energy consumption.
Accordingly, at a policy level, the results of Model 2 provide a potential
solution for this issue.
Compared with the previous works investigating the case of the
BRICS nations using ARDL, NARDL, and FADL models, Qin and Ozturk
[18], Yilanci et al. [51], and Zhang et al. [52] found a positive effect of
FDI on the consumption of renewable energy, while Zeng and Yue [49]
reported a negative consequence of FDI. However, our empirical find
ings indicate that FDI can have both negative and positive effects on
renewable energy consumption, depending upon the degree of strin
gency of environmental regulation. Hence, simultaneously taking the
environmental regulation into consideration as a moderating variable
and a threshold variable in the estimations enable us to obtain more
comprehensive outcomes.
Regarding the impact of the control variables, the per capita income
shows a positive effect on renewable energy consumption, as expected.
This is because when the country becomes wealthier, the people will
demand higher environmental quality. Eventually, renewable energy
consumption will be increased to lessen pollution. Noteworthily, our
findings are consistent with the conclusions of Tugcu and Topcu [106]
and Rafique et al. [119]. Likewise, trade openness, financial develop
ment, and urbanization are also found to be important driving factors of
renewable energy consumption in the BRICS countries. Our empirical
results are compatible with the works of Sebri and Ben-Salha [114],
Kahia et al. [117], Chen [127], Yahya and Rafiq [6], Ekanayake and
Thaver [137], Lahiani et al. [132], and Sharma et al. [147].
5.4. Policy implications
Since our empirical results indicate that the impacts of FDI on
renewable energy consumption rely on environmental regulatory
stringency, authorities should consider the differentiated influences of
FDI in formulating the stringency level of environmental regulation to
promote renewable energy consumption. Moreover, intensifying formal
environmental regulations, such as taxes, environmental standards, and
tradable permits, exerts a significant role in promoting the technique
effect of FDI on renewable energy consumption. This indicates that
formulating relatively comprehensive and strong formal environmental
regulations is beneficial for accelerating renewable energy consump
tion. Specifically, only when the EPS is higher than 0.4731 could the
increase in FDI exert significant and positive influences on the promo
tion of renewable energy consumption. As a result, countries with high
values of EPS that already pass the threshold value (e.g., China) could
implement a relatively moderate environmental policy and maintain the
current stringency level; they are simultaneously managing to control
the potential cost of environmental compliance of MNEs and attracting
sustaining inflow of FDI at the present stage. However, in countries with
relatively low EPS values that do not pass the threshold value (e.g.,
Brazil and India), the inflow of FDI may mainly cause scale and
composition effects, discouraging renewable energy consumption.
Intensifying formal environmental regulations would help to encourage
renewable energy consumption.
Regarding the informal environmental regulation proxied by HDI,
only when the HDI level is higher than the threshold value (HDI =
0.6459), could the influx of FDI encourage renewable energy con
sumption. The low HDI level of BRICS in past decades might limit
renewable energy consumption. Fortunately, the average values of HDI
of BRICS members have already been above the threshold currently.
However, taking India as an example; HDI experienced a decline drop
ping to 0.6332 in 2021 compared to the pre-COVID-19 level in 2019 of
0.6421, with a ranking of 131 out of 191 nations [148]. This value is
below the threshold. Therefore, to promote renewable consumption
when FDI inflows and address the degradation of the environment, India
should endeavor to sustain progress in human development through
instrumental steps, such as improving education, literacy, and health
facilities, and addressing social inequality.
6. Robustness test
Apart from the threshold regression analysis, we conducted the

145
generalized method of moments (GMM) estimations for the purpose of
robustness checks. In particular, the GMM technique was adopted due to
its certain advantages (e.g., both individual and time-specific effects are
controlled in the model). In addition, the problem of endogeneity bias
can be lessened when it includes a set of instrumental variables. The
corresponding dynamic empirical GMM models of FDI, renewable en
ergy consumption, and threshold variable (formal environmental regu
lation) are specified as Equations (3) and (4) or Models 3–4, while
Models 5–6 exhibit the interplays among FDI, renewable energy con
sumption, and informal environmental regulation, together with other
core explanatory variables.
LnREWit = α0 + α1LnREWit− 1 + α2LnFDIit + α3LnGDPPCit + α4LnTRADEit
+α5LnCREDITit + α6LnURBANit + α7LnEPSit + εit
(3)
+α5LnCREDITit + α6LnURBANit + α7LnFDIit ∗ LnEPSit + εit
(4)
+α5LnCREDITit + α6LnURBANit + α7LnHDIit + εit
(5)
+α5LnCREDITit + α6LnURBANit + α7LnFDIit ∗ LnHDIit + εit
(6)
Both system GMM and difference GMM techniques were estimated.
In addition, the lag term of the endogenous variable was incorporated
into each model as the instrumental variable. The estimations of the
system GMM models are reported in Table 6. The moderating role of
formal and informal environmental regulations was observed through
the interaction terms, such as LnFDI*LnEPS and LnFDI*LnHDI in Models
4 and 5, respectively. The coefficients of the lag variables of LnREW are
statistically significant and positive. It implies that the renewable energy
consumption of the BRICS countries is correlated to consumption in the
past. The significant and positive coefficients of LnEPS and LnHDI sug
gest that a more stringent environmental policy can generally lead to
higher renewable energy consumption. Further, when the interaction
term was incorporated, the coefficients of the interaction terms were
larger than those of LnFDI. This finding reveals that the formal and
informal environmental regulations strengthen the impact of FDI on
renewable energy consumption in BRICS economies. Notably, these
empirical findings from system GMM are consistent with our panel
threshold regressions shown in Table 5.
The estimation results from the difference GMM are presented as
Models 7–10 in Table 7, which replicate Models 3–6 of the system GMM
shown in Table 6. Models 3–10 passed the AR and the Sargan tests,
confirming the validity and reliability of the model settings. Generally,
the findings from the difference GMM were in line with the estimation
results from the system GMM shown in Table 6. Overall, the empirical
outcomes from the system and difference GMM models verify the
robustness of the findings of our panel threshold analysis.
Table 6
Robustness test results: System GMM model.
Variable Model 3 Model 4 Model 5 Model 6
LnREW(-1) 0.1935*** 0.1822*** 01420*** 0.1363***
(2.9549) (2.4530) (3.9458) (3.2080)
LnFDI 0.0266*** 0.0288*** 0.0693** 0.06188***
(3.2165) (3.3881) (2.4094) (5.1462)
LnGDPPC 0.5785** 0.4944* 1.8057*** 1.7731***
(2.3469) (1.6130) (4.2878) (6.4217)
LnTRADE 0.3070** 0.2195* 1.2615*** 1.2537***
(2.2075) (1.6673) (7.6247) (8.2606)
LnCREDIT 0.0272*** 0.0304*** 0.5834*** 0.3345**
(5.1485) (6.2001) (3.1316) (2.0816)
LnURBAN 2.0017** 2.0052** 4.6839*** 4.2623***
(2.2048) (2.5837) (6.1335) (7.2603)
LnEPS 0.1035***
(2.8930)
LnFDI*EPS 0.1154*
(1.9905)
LnHDI 0.1069***
(5.6170)
LnFDI* LnHDI 0.1551*
(1.6960)
Constant 14.3432*** 15.1526*** 26.1008*** 22.9795***
(6.4046) (6.6179) (14.8642) (20.3320)
Sargan test 13.4410 14.8646 14.8146 13.8916
1.00 1.00 1.00 1.00
Arellano-Bond test for
AR(1)
P = 0.0000 P = 0.0000 P = 0.0000 P = 0.0000
Arellano-Bond test for
AR(2)
P = 0.7881 P = 0.7381 P = 0.3334 P = 0.2535
Observations 130 130 130 130
Note: 1. The symbol ***, **, and * indicate the 1%, 5%, and 10% level of sig
nificance, respectively, and t-statistics are presented in parentheses.
2. AR(1) and AR (2) refer to the Arellano-Bond autocorrelation tests for the first
order and second order difference of the error term, respectively. The Sargan test
indicates the over-identification test.
Table 7
Robustness test results: Difference GMM model.
Variable Model 7 Model 8 Model 9 Model 10
LnREW(-1) 0.1961** 0.2159** 0.1235 0.2025**
(2.1985) (2.3784) (1.3994) (2.3103)
LnFDI 0.0358** 0.0572** 0.0471*** 0.0282***
(2.5323) (2.1039) (3.3617) (3.7366)
LnGDPPC 0.4296** 0.4716** 0.1336** 0.1861***
(2.0365) (2.2866) (2.1630) (2.8878)
LnTRADE 0.1981* 0.2146* 0.2728** 0.2387*
(1.7393) (1.6406) (2.1157) (1.8230)
LnCREDIT 0.0336* 0.0634* 0.0295 0.1488
(1.6515) (1.6042) (0.2336) (1.1118)
LnURBAN 2.1366*** 2.0979*** 1.7358*** 1.7981***
(3.2399) (3.1383) (3.9346) (4.0056)
LnEPS 0.0553*
(1.6732)
LnFDI*EPS 0.1191*
(1.6342)
LnHDI 0.1162***
(4.0573)
LnFDI* LnHDI 0.1380***
(3.2147)
Constant 12.6396
***
13.45049
***
12.13733*** 11.2282***
(14.7846) (14.8246) (14.3023) (14.6503)
Sargan test 12.7142 12.9353 12.0667 12.6275
1.00 1.00 1.00 1.00
Arellano-Bond test
for AR(1)
P = 0.0000 P = 0.0000 P = 0.0000 P = 0.0000
Arellano-Bond test
for AR(2)
P = 0.4552 P = 0.6794 P = 0.7804 P = 0.1485
Observations 130 130 130 130
Note: 1. The symbol ***, **, and * indicate the 1%, 5%, and 10% level of sig
nificance, respectively, and t-statistics are presented in parentheses.
2. AR(1) and AR (2) refer to the Arellano-Bond autocorrelation tests for the first
order and second order difference of the error term, respectively. The Sargan test
indicates the over-identification test.

146
7. Conclusions and research implications
Our empirical analysis provides three main conclusions. First, the
impacts of FDI on renewable consumption are nonlinear. Second, the
directions of effects of FDI on renewable energy change from negative to
positive when the environmental regulatory stringency is higher than
the threshold level. Third, control variables, such as GDP per capita,
trade openness, financial development, and urbanization, also influence
renewable energy consumption. These findings can explain the contra
dictory findings of previous research studies on the effect of FDI and
significantly harmonize the prediction of the pollution haven and
pollution halo hypotheses.
Based on the findings, some policy recommendations are proposed.
First, environmental regulation is suggested as instrumental tool to
moderate the impacts of FDI on renewable energy consumption. Second,
the proper level of environmental regulation stringency is provided by
threshold values. Last but not the least, our research is a preliminary
attempt to explore the moderating effect of environmental regulation on
FDI with some efficient policy implications to encourage renewable
energy consumption in BRICS. A similar analytical framework could also
be applied to other developing countries when the data is accessible.
CRediT authorship contribution statement
Yan Tan: Conceptualization, Modelling, Methodology, Data cura
tion, Software, Formal analysis, Validation, Visualization, Writing –
original draft, Writing – review & editing. Utai Uprasen: Supervision,
Conceptualization, Methodology, Formal analysis, Visualization,
Writing – original draft, Writing – review & editing.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
Data will be made available on request.
References
[1] IRENA, NDCs and Renewable Energy Targets in 2021: Are We on the Right Path
to a Climate-Safe Future? International Renewable Energy Agency, Abu Dhabi,
2022. https://www.irena.org/publications/2022/Jan/NDCs-and-Renewable-Ene
rgy-Targets-in-2021. Accessed September 12 2022.
[2] UNEP, Emissions, Gap Report 2021: the Heat Is on - A World of Climate Promises
Not yet Delivered, 2021. Nairobi, https://wedocs.unep.org/handle/20.500.11
822/36990. Accessed September 12 2022.
[3] Y. Wolde-Rufael, E.M. Weldemeskel, Environmental policy stringency, renewable
energy consumption and CO2 emissions: panel cointegration analysis for BRIICTS
countries, Int. J. Green Energy 17 (10) (2020) 568–582, https://doi.org/
10.1080/15435075.2020.1779073.
[4] M. Borga, A. Pegoue, G.M.H. Legoff, A.S. Rodelgo, D. Entaltsev, K. Egesa,
Measuring Carbon Emissions of Foreign Direct Investment in Host Economies,
International Monetary Fund, 2022. https://www.imf.org/en/Publications/WP/
Issues/2022/05/06/Measuring-Carbon-Emissions-of-Foreign-Direct-Investment
-in-Host-Economies-517531. Accessed September 12 2022.
[5] M. Kalamova, N. Johnstone, Environmental policy stringency and foreign direct
investment, in: E. Elgar (Ed.), A Handbook of Globalisation and Environmental
Policy, 2012, https://doi.org/10.4337/9781849805773.00011.
[6] F. Yahya, M. Rafiq, Brownfield, greenfield, and renewable energy consumption:
moderating role of effective governance, Energy Environ. 31 (3) (2020) 405–423,
https://doi.org/10.1177/0958305X19872936.
[7] M. Mert, A.E. Caglar, Testing pollution haven and pollution halo hypotheses for
Turkey: a new perspective, Environ. Sci. Pollut. Res. Int. 27 (26) (2020)
32933–32943, https://doi.org/10.1007/s11356-020-09469-7.
[8] S.A. Sarkodie, V. Strezov, Effect of foreign direct investments, economic
development and energy consumption on greenhouse gas emissions in developing
countries, Sci. Total Environ. 646 (2019) 862–871, https://doi.org/10.1016/j.
scitotenv.2018.07.365.
[9] J. He, Pollution haven hypothesis and environmental impacts of foreign direct
investment: the case of industrial emission of sulfur dioxide (SO2) in Chinese
provinces, Ecol. Econ. 60 (1) (2006) 228–245. https://halshs.archives-ouvertes.
fr/halshs-00564699.
[10] C. Sun, F. Zhang, M. Xu, Investigation of pollution haven hypothesis for China: an
ARDL approach with breakpoint unit root tests, J. Clean. Prod. 161 (2017)
153–164, https://doi.org/10.1016/j.jclepro.2017.05.119.
[11] F. Seker, H.M. Ertugrul, M. Cetin, The impact of foreign direct investment on
environmental quality: a bounds testing and causality analysis for Turkey, Renew.
Sustain. Energy Rev. 52 (2015) 347–356, https://doi.org/10.1016/j.
rser.2015.07.118.
[12] B. Sung, W.-Y. Song, S.-D. Park, How foreign direct investment affects CO2
emission levels in the Chinese manufacturing industry: evidence from panel data,
Econ. Syst. 42 (2) (2018) 320–331, https://doi.org/10.1016/j.
ecosys.2017.06.002.
[13] S.-C. Xu, Y.-F. Li, J.-N. Zhang, Y. Wang, X.-X. Ma, H.-Y. Liu, H.-N. Wang, Y. Tao,
Do foreign direct investment and environmental regulation improve green
technology innovation? An empirical analysis based on panel data from the
Chinese manufacturing industry, Environ. Sci. Pollut. Res. 28 (39) (2021)
55302–55314, https://doi.org/10.1007/s11356-021-14648-1.
[14] G.M. Grossman, A. Krueger, Pollution and growth: what do we know, Int. J.
Sustain. Dev. 19 (1995) 41, https://doi.org/10.1017/CBO9780511751905.003.
[15] M. Shahbaz, M.A. Nasir, D. Roubaud, Environmental degradation in France: the
effects of FDI, financial development, and energy innovations, Ecol. Econ. 74
(2018) 843–857, https://doi.org/10.1016/j.eneco.2018.07.020.
[16] H.-T. Pao, C.-M. Tsai, Multivariate Granger causality between CO2 emissions,
energy consumption, FDI (foreign direct investment) and GDP (gross domestic
product): evidence from a panel of BRIC (Brazil, Russian Federation, India, and
China) countries, Energy 36 (1) (2011) 685–693, https://doi.org/10.1016/j.
energy.2010.09.041.
[17] W. Jun, M. Zakaria, S.J.H. Shahzad, H. Mahmood, Effect of FDI on pollution in
China: new insights based on wavelet approach, Sustainability 10 (11) (2018)
3859, https://doi.org/10.3390/su10113859.
[18] Z. Qin, I. Ozturk, Renewable and non-renewable energy consumption in BRICS:
assessing the dynamic linkage between foreign capital inflows and energy
consumption, Energies 14 (10) (2021) 2974, https://doi.org/10.3390/
en14102974.
[19] D.I. Stern, The rise and fall of the environmental Kuznets curve, World Dev. 32 (8)
(2004) 1419–1439, https://doi.org/10.1016/j.worlddev.2004.03.004.
[20] P. Pazienza, The environmental impact of the FDI inflow in the transport sector of
OECD countries and policy implications, Int. Advances Econ. Res. 21 (1) (2015)
105–116, https://doi.org/10.1007/s11294-014-9511-y.
[21] P. Pazienza, The relationship between CO2 and Foreign Direct Investment in the
agriculture and fishing sector of OECD countries: evidence and policy
considerations, Intelektinė ekonomika 9 (1) (2015) 55–66, https://doi.org/
10.1016/j.intele.2015.08.001.
[22] U. Uzar, K. Eyuboglu, Is foreign direct investment an engine for energy
consumption? An empirical investigation for Turkey, Environ. Sci. Pollut. Res. 26
(27) (2019) 28092–28105, https://doi.org/10.1007/s11356-019-05996-0.
[23] R. Salim, Y. Yao, G. Chen, L. Zhang, Can foreign direct investment harness energy
consumption in China? A time series investigation, Energy Econ. 66 (2017)
43–53, https://doi.org/10.1016/j.eneco.2017.05.026.
[24] Z. Xin-gang, Z. Yuan-feng, L. Yan-bin, The spillovers of foreign direct investment
and the convergence of energy intensity, J. Clean. Prod. 206 (2019) 611–621,
https://doi.org/10.1016/j.jclepro.2018.09.225.
[25] M. Bu, S. Li, L. Jiang, Foreign direct investment and energy intensity in China:
firm-level evidence, Energy Econ. 80 (2019) 366–376, https://doi.org/10.1016/j.
eneco.2019.01.003.
[26] P.K. Adom, F. Amuakwa-Mensah, What drives the energy saving role of FDI and
industrialization in East Africa? Renew. Sustain. Energy Rev. 65 (2016) 925–942,
https://doi.org/10.1016/j.rser.2016.07.039.
[27] M. Hübler, A. Keller, Energy savings via FDI? Empirical evidence from developing
countries, Environ. Dev. Econ. 15 (1) (2010) 59–80, https://doi.org/10.1017/
S1355770X09990088.
[28] C. Zhang, X. Zhou, Does foreign direct investment lead to lower CO2 emissions?
Evidence from a regional analysis in China, Renew. Sustain. Energy Rev. 58
(2016) 943–951, https://doi.org/10.1016/j.rser.2015.12.226.
[29] A. Samour, M.M. Baskaya, T. Tursoy, The impact of financial development and
FDI on renewable energy in the UAE: a path towards sustainable development,
Sustainability 14 (3) (2022) 1208, https://doi.org/10.3390/su14031208.
[30] M. Mert, G. Bölük, Do foreign direct investment and renewable energy
consumption affect the CO2 emissions? New evidence from a panel ARDL
approach to Kyoto Annex countries, Environ. Sci. Pollut. Res. 23 (21) (2016)
21669–21681, https://doi.org/10.1007/s11356-016-7413-7.
[31] J. Grabara, A. Tleppayev, M. Dabylova, L.W. Mihardjo, Z. Dacko-Pikiewicz,
Empirical research on the relationship amongst renewable energy consumption,
economic growth and foreign direct investment in Kazakhstan and Uzbekistan,
Energies 14 (2) (2021) 332, https://doi.org/10.3390/en14020332.
[32] A.K. Tiwari, S. Nasreen, M.A. Anwar, Impact of equity market development on
renewable energy consumption: do the role of FDI, trade openness and economic
growth matter in Asian economies? J. Clean. Prod. 334 (2022), 130244 https://
doi.org/10.1016/j.jclepro.2021.130244.
[33] M.M. Islam, M. Irfan, M. Shahbaz, X.V. Vo, Renewable and non-renewable energy
consumption in Bangladesh: the relative influencing profiles of economic factors,
urbanization, physical infrastructure and institutional quality, Renew. Energy
184 (2022) 1130–1149, https://doi.org/10.1016/j.renene.2021.12.020.

147
[34] N. Apergis, M. Pinar, The role of party polarization in renewable energy
consumption: fresh evidence across the EU countries, Energy Pol. 157 (2021),
112518, https://doi.org/10.1016/j.enpol.2021.112518.
[35] F. Amri, The relationship amongst energy consumption, foreign direct investment
and output in developed and developing countries, Renew. Sustain. Energy Rev.
64 (2016) 694–702, https://doi.org/10.1016/j.rser.2016.06.065.
[36] F. Amuakwa-Mensah, E. Näsström, Role of banking sector performance in
renewable energy consumption, Appl. Energy 306 (2022), 118023, https://doi.
org/10.1016/j.apenergy.2021.118023.
[37] N. Doytch, S. Narayan, Does FDI influence renewable energy consumption? An
analysis of sectoral FDI impact on renewable and non-renewable industrial
energy consumption, Energy Econ. 54 (2016) 291–301, https://doi.org/10.1016/
j.eneco.2015.12.010.
[38] L.L. Khandker, S.B. Amin, F. Khan, Renewable energy consumption and foreign
direct investment: reports from Bangladesh, J. Account. 8 (3) (2018).
[39] W. Fan, Y. Hao, An empirical research on the relationship amongst renewable
energy consumption, economic growth and foreign direct investment in China,
Renew. Energy 146 (2020) 598–609, https://doi.org/10.1016/j.
renene.2019.06.170.
[40] X. Kang, F.U. Khan, R. Ullah, M. Arif, S.U. Rehman, F. Ullah, Does foreign direct
investment influence renewable energy consumption? empirical evidence from
south Asian countries, Energies 14 (12) (2021) 3470, https://doi.org/10.3390/
en14123470.
[41] R. Sbia, M. Shahbaz, H. Hamdi, A contribution of foreign direct investment, clean
energy, trade openness, carbon emissions and economic growth to energy
demand in UAE, Econ. Modell. 36 (2014) 191–197, https://doi.org/10.1016/j.
econmod.2013.09.047.
[42] J.A. Camacho Ballesta, L. da Silva Almeida, M. Rodriguez, An analysis of the main
driving factors of renewable energy consumption in the European Union,
Environ. Sci. Pollut. Res. Int. (2022) 1–14, https://doi.org/10.1007/s11356-022-
18715-z.
[43] M. Zhang, S. Zhang, C.-C. Lee, D. Zhou, Effects of trade openness on renewable
energy consumption in OECD countries: new insights from panel smooth
transition regression modelling, Energy Econ. 104 (2021), 105649, https://doi.
org/10.1016/j.eneco.2021.105649.
[44] N. Alsagr, S. van Hemmen, The impact of financial development and geopolitical
risk on renewable energy consumption: evidence from emerging markets,
Environ. Sci. Pollut. Res. Int. 28 (20) (2021) 25906–25919, https://doi.org/
10.1007/s11356-021-12447-2.
[45] S.R. Paramati, M. Ummalla, N. Apergis, The effect of foreign direct investment
and stock market growth on clean energy use across a panel of emerging market
economies, Energy Econ. 56 (2016) 29–41, https://doi.org/10.1016/j.
eneco.2016.02.008.
[46] S.G. Anton, A.E.A. Nucu, The effect of financial development on renewable
energy consumption. A panel data approach, Renew. Energy 147 (2020)
330–338, https://doi.org/10.1016/j.renene.2019.09.005.
[47] J.W. Lee, The contribution of foreign direct investment to clean energy use,
carbon emissions and economic growth, Energy Pol. 55 (2013) 483–489, https://
doi.org/10.1016/j.enpol.2012.12.039.
[48] M. Murshed, Are trade liberalization policies aligned with renewable energy
transition in low and middle income countries? An instrumental variable
approach, Renew. Energy 151 (2020) 1110–1123, https://doi.org/10.1016/j.
renene.2019.11.106.
[49] Q. Zeng, X. Yue, Re-evaluating the asymmetric economic policy uncertainty,
conventional energy, and renewable energy consumption nexus for BRICS,
Environ. Sci. Pollut. Res. 29 (14) (2022) 20347–20356, https://doi.org/10.1007/
s11356-021-17133-x.
[50] M. Shahbaz, A. Sinha, C. Raghutla, X.V. Vo, Decomposing scale and technique
effects of financial development and foreign direct investment on renewable
energy consumption, Energy 238 (2022), 121758, https://doi.org/10.1016/j.
energy.2021.121758.
[51] V. Yilanci, O. Ozgur, M.S. Gorus, The asymmetric effects of foreign direct
investment on clean energy consumption in BRICS countries: a recently
introduced hidden cointegration test, J. Clean. Prod. 237 (2019), 117786,
https://doi.org/10.1016/j.jclepro.2019.117786.
[52] Y. Zhang, M. Qamruzzaman, S. Karim, I. Jahan, Nexus between economic policy
uncertainty and renewable energy consumption in BRIC Nations: the mediating
role of foreign direct investment and financial development, Energies 14 (15)
(2021) 4687, https://doi.org/10.3390/en14154687.
[53] M. Qamruzzaman, W. Jianguo, The asymmetric relationship between financial
development, trade openness, foreign capital flows, and renewable energy
consumption: fresh evidence from panel NARDL investigation, Renew. Energy
[54] H. Wang, H. Liu, Foreign direct investment, environmental regulation, and
environmental pollution: an empirical study based on threshold effects for
different Chinese regions, Environ. Sci. Pollut. Res. 26 (6) (2019) 5394–5409,
https://doi.org/10.1007/s11356-018-3969-8.
[55] F. Wang, J. He, Y. Niu, Role of foreign direct investment and fiscal
decentralization on urban haze pollution in China, J. Environ. Manag. 305
(2022), 114287, https://doi.org/10.1016/j.jenvman.2021.114287.
[56] Y. Huang, F. Chen, H. Wei, J. Xiang, Z. Xu, R. Akram, The impacts of FDI inflows
on carbon emissions: economic development and regulatory quality as
moderators, Front. Energy Res. 9 (2022), https://doi.org/10.3389/
fenrg.2021.820596.
[57] D. Kaufmann, A. Kraay, M. Mastruzzi, The worldwide governance indicators:
Methodology and analytical issues1, Hague J. Rule Law 3 (2) (2011) 220–246,
https://doi.org/10.1017/s1876404511200046.
[58] U. Shahzad, M. Radulescu, S. Rahim, C. Isik, Z. Yousaf, S.A. Ionescu, Do
environment-related policy instruments and technologies facilitate renewable
energy generation? Exploring the contextual evidence from developed
economies, Energies 14 (3) (2021) 690, https://doi.org/10.3390/en14030690.
[59] X. Liu, C. Wang, Does foreign direct investment facilitate technological progress?:
evidence from Chinese industries, Res. Pol. 32 (6) (2003) 945–953, https://doi.
org/10.1016/S0048-7333(02)00094-X.
[60] K.-y. Cheung, L. Ping, Spillover effects of FDI on innovation in China: evidence
from the provincial data, China Econ, Rev. 15 (1) (2004) 25–44, https://doi.org/
10.1016/S1043-951X(03)00027-0.
[61] Y. Chang, Y. Huang, M. Li, Z. Duan, Threshold effect in the relationship between
environmental regulations and haze pollution: empirical evidence from PSTR
estimation, Int. J. Environ. Health Res. 18 (23) (2021), 12423, https://doi.org/
10.3390/ijerph182312423.
[62] H.J. Bragdon, T.J. Marlin, Is pollution profitable? Risk Manag.Bus. Soc. Rev. 19
(1972) 9–18. https://www.scirp.org/(S(czeh2tfqw2orz553k1w0r45))/reference
/referencespapers.aspx?referenceid=2934221.
[63] D.G. Rassier, D. Earnhart, Does the porter hypothesis explain expected future
financial performance? The effect of clean water regulation on chemical
manufacturing firms, Environ. Resour. Econ. 45 (3) (2010) 353–377, https://doi.
org/10.1007/s10640-009-9318-0.
[64] B. Doğan, L.K. Chu, S. Ghosh, H.H.D. Truong, D. Balsalobre-Lorente, How
environmental taxes and carbon emissions are related in the G7 economies?
Renew. Energy 187 (2022) 645–656, https://doi.org/10.1016/j.
renene.2022.01.077.
[65] M. Greenstone, The impacts of environmental regulations on industrial activity:
evidence from the 1970 and 1977 clean air act amendments and the census of
manufactures, J. Polit. Econ. 110 (6) (2002) 1175–1219, https://doi.org/
10.1086/342808.
[66] W.B. Gray, R.J. Shadbegian, Plant vintage, technology, and environmental
regulation, J. Environ. Econ. Manag. 46 (3) (2003) 384–402, https://doi.org/
10.1016/S0095-0696(03)00031-7.
[67] A. Blackman, A. Kildegaard, Clean technological change in developing-country
industrial clusters: Mexican leather tanning, Environ. Econ. Pol. Stud. 12 (3)
(2010) 115–132, https://doi.org/10.1007/s10018-010-0164-7.
[68] W. Xia, N. Apergis, M.F. Bashir, S. Ghosh, B. Doğan, U. Shahzad, Investigating the
role of globalization, and energy consumption for environmental externalities:
empirical evidence from developed and developing economies, Renew. Energy
[69] M.E. Porter, America’s green strategy, Sci. Am. 264 (4) (1991) 168, https://doi.
org/10.1038/scientificamerican0491-168.
[70] M.E. Porter, C. Van der Linde, Toward a new conception of the environment-
competitiveness relationship, J. Econ. Perspect. 9 (4) (1995) 97–118, https://doi.
org/10.1257/jep.9.4.97.
[71] R. De Santis, C.J. Lasinio, Environmental policies, innovation and productivity in
the EU, Global Econ. J. 16 (4) (2016) 615–635, https://doi.org/10.2139/
ssrn.2696266.
[72] I. Martínez-Zarzoso, A. Bengochea-Morancho, R. Morales-Lage, Does
environmental policy stringency foster innovation and productivity in OECD
countries? Energy Pol. 134 (2019), 110982 https://doi.org/10.1016/j.
enpol.2019.110982.
[73] T. van Kemenade, A.A. Teixeira, Policy stringency and (eco)-innovation
performance: a cross country analysis, J. Innov. Sustain. RISUS. 8 (2) (2017)
34–60, https://doi.org/10.24212/2179-3565.2017v8i2p34-60.
[74] X. Wang, Q. Shao, Non-linear effects of heterogeneous environmental regulations
on green growth in G20 countries: evidence from panel threshold regression, Sci.
Total Environ. 660 (2019) 1346–1354, https://doi.org/10.1016/j.
scitotenv.2019.01.094.
[75] K. Ahmed, Environmental policy stringency, related technological change and
emissions inventory in 20 OECD countries, J. Environ. Manag. 274 (2020),
111209, https://doi.org/10.1016/j.jenvman.2020.111209.
[76] M. Galeotti, S. Salini, E. Verdolini, Measuring environmental policy stringency:
approaches, validity, and impact on environmental innovation and energy
efficiency, Energy Pol. 136 (2020), 111052, https://doi.org/10.2139/
ssrn.2980849.
[77] M. Hassan, D. Rousselière, Does increasing environmental policy stringency lead
to accelerated environmental innovation? A research note, Appl. Econ. 54 (17)
(2022) 1989–1998, https://doi.org/10.1080/00036846.2021.1983146.
[78] D. Acemoglu, P. Aghion, L. Bursztyn, D. Hemous, The environment and directed
technical change, Am. Econ. Rev. 102 (1) (2012) 131–166, https://doi.org/
10.1257/aer.102.1.131.
[79] Y. Rubashkina, M. Galeotti, E. Verdolini, Environmental regulation and
competitiveness: empirical evidence on the Porter Hypothesis from European
manufacturing sectors, Energy Pol. 83 (2015) 288–300, https://doi.org/10.1016/
j.enpol.2015.02.014.
[80] N. Johnstone, S. Managi, M.C. Rodríguez, I. Haščič, H. Fujii, M. Souchier,
Environmental policy design, innovation and efficiency gains in electricity
generation, Energy Econ. 63 (2017) 106–115, https://doi.org/10.1016/j.
eneco.2017.01.014.
[81] Q. Wang, B. Yuan, Air pollution control intensity and ecological total-factor
energy efficiency: the moderating effect of ownership structure, J. Clean. Prod.
186 (2018) 373–387, https://doi.org/10.1016/j.jclepro.2018.03.106.

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