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INCLUSIVE GREEN GROWTH IN OECD COUNTRIES: WHAT ARE THE IMPACTS OF STRINGENT
ENVIRONMENTAL AND EMPLOYMENT REGULATIONS?

 * February 2023
 * Environmental Economics and Policy Studies

DOI:10.1007/s10018-023-00362-4
Authors:
Béchir Ben Lahouel
 * IPAG Business School



Taleb Lotfi
 * Ecole Supérieure des Sciences Economiques et Commerciales de Tunis



Shunsuke Managi


Shunsuke Managi
 * This person is not on ResearchGate, or hasn't claimed this research yet.



Nadia Abaoub


Nadia Abaoub
 * This person is not on ResearchGate, or hasn't claimed this research yet.



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Citations (11)
References (72)
Figures (1)





ABSTRACT AND FIGURES

Inclusive green growth (IGG) is a new way to achieve sustainable development
through the realization of economic growth, social equity, and environmental
protection. Empirical research about measuring IGG and exploring its driving
factors is scarce. Based on panel data from 26 OECD countries over the period
1990–2012, this study aims to examine the impacts of stringent environmental and
employment regulations on countries’ competitiveness, represented by the IGG
index. Therefore, we adopt the slacks-based measure model with directional
distance function (SBM-DDF) and the global Malmquist-Luenberger productivity
index (GMLPI) to calculate an IGG index, and a dynamic panel data regression
analysis to establish the impacts of different regulatory policies on IGG. The
improvement in the IGG and its components is modest over the period and there is
room for improvement. The regression results show that the environmental policy
stringency, the employment protection legislation, and their interaction are
beneficial to the promotion of IGG. Some implications for the OECD countries are
provided.
Inclusive green growth and its sources
… 




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Vol.:(0123456789)
Environmental Economics and Policy Studies
https://doi.org/10.1007/s10018-023-00362-4
1 3
RESEARCH ARTICLE
Inclusive green growth inOECD countries: what are
theimpacts ofstringent environmental andemployment
regulations?
BéchirBenLahouel1· LotTaleb2· ShunsukeManagi3· NadiaAbaoub4
Received: 18 February 2022 / Accepted: 27 January 2023
© Society for Environmental Economics and Policy Studies 2023
Abstract
Inclusive green growth (IGG) is a new way to achieve sustainable development
through the realization of economic growth, social equity, and environmental
pro-
tection. Empirical research about measuring IGG and exploring its driving
factors
is scarce. Based on panel data from 26 OECD countries over the period 1990–2012,
this study aims to examine the impacts of stringent environmental and employment
regulations on countries’ competitiveness, represented by the IGG index.
Therefore,
we adopt the slacks-based measure model with directional distance function (SBM-
DDF) and the global Malmquist-Luenberger productivity index (GMLPI) to calcu-
late an IGG index, and a dynamic panel data regression analysis to establish the
impacts of diﬀerent regulatory policies on IGG. The improvement in the IGG and
its components is modest over the period and there is room for improvement. The
regression results show that the environmental policy stringency, the employment
protection legislation, and their interaction are beneﬁcial to the promotion of
IGG.
Some implications for the OECD countries are provided.
Keywords Inclusive green growth· Environmental policy stringency· Employment
protection legislation· Global Malmquist-Luenberger productivity index· Data
envelopment analysis· OECD countries
1 Introduction
The concept of inclusive green growth (IGG) was oﬃcially put forward at the
2012 United Nations Conference on Sustainable Development (Rio + 20) to merge
the green growth interests of the industrialized world with the inclusive
develop-
ment interests of the developing world. The theme of the conference was “green
economy in the context of sustainable development and poverty eradication” and
* Béchir Ben Lahouel
b.benlahouel@ipag.fr
Extended author information available on the last page of the article



Environmental Economics and Policy Studies
1 3
proposed the IGG to ﬁnd new pathways of sustainable development. In the same
year and in the lead up to Rio + 20, the World Bank released the “Inclusive
green
growth: the road to sustainable development” report arguing that sustained
growth
is necessary to meet the urgent development needs of the world’s poor and that
it
is possible to grow more cleanly without growing more slowly. Along with the new
United Nations Sustainable Development Goals (SDGs) in 2016, it was announced
that the international community should pay more attention to addressing the
triple
bottom line, which means inclusive growth must be green and green growth must be
inclusive. Thus, the concept of IGG, which aims to eradicate poverty and protect
the
environment, has been adopted as one of the development strategies of several
coun-
tries around the world (Chen etal. 2020).
The concept of IGG comes from the integration of two development concepts,
namely green growth (GG) and inclusive growth (IG). GG is considered as envi-
ronmentally sustainable economic growth. It is deﬁned as a growth that is
eﬃcient,
clean, and resilient. Eﬃcient in its use of natural resources, clean in that it
promotes
environmental pollution and mitigates climate change, and resilient in that it
consid-
ers natural hazards and the role of environmental management and natural capital
in
preventing physical catastrophes (World Bank 2012). However, GG is not
inherently
inclusive as it cannot eﬀectively deal with the current welfare gap and poverty
(Sun
etal. 2020). That is, if GG is not inclusive, then GG for any purpose is
unsustain-
able (World Bank 2012). On the other hand, IG is a concept that encompasses
equal
opportunity, social equity, and beneﬁt sharing (Chen etal. 2020). IG recognizes
the
complementarity between economic growth and social achievements through the
creation, promotion, and provision of fair and equal access to economic opportu-
nities. Both concepts (i.e., GG and IG) emphasize the integration of the three
sys-
tems of economy, environment, and society in development. Whereas GG mainly
focuses on the coordination between economic growth and environmental sustain-
ability, IG considers the overall coordination between economic growth and
social
improvement.
In this paper, we follow Bouma and Berkhout (2015) and argue that the con-
cept of IGG should be understood in the context of the current non-green and
non-
inclusive growth of the global economy. From this perspective, IGG recognizes
the trade-oﬀs between growth, nature, and inclusion, but emphasizes that within
the overarching goal of social welfare, there is room for synergies. From its
social
dimension, IGG means improving human welfare, reducing social inequality, and
distributing necessary goods such as work, life, and energy. From its economic
per-
spective, which refers to utility and not just income, IGG means that the
economy
is not simply deﬁned by GDP growth, but rather is a green economy with continu-
ous technological innovation, continuous environmental improvement, and reduced
economic inequality. From its environmental dimension, IGG means “sustainable
development” alongside resource conservation and environmental protection under
conditions of ecological balance (Albagoury 2016).
There are synergies between growth, inclusiveness, and ecology, but there are
often trade-oﬀs. Although the economic growth, recorded over the past two dec-
ades, has lifted hundreds of millions of people out of poverty, it has too often
been at the expense of the environment (World Bank 2012). Due to a variety of


1 3
Environmental Economics and Policy Studies
market, policy, and institutional failures, the world’s natural capital tends to
be
used economically ineﬃciently and wasted, without adequate consideration of the
true social costs of resource depletion and without appropriate ploughing back
into other sources of revenue (World Bank 2012). This implies that policies for
IGG should be carefully designed to maximize beneﬁts and minimize costs for
the most disadvantaged and that policies and actions with irreversible and neg-
ative environmental impacts should be avoided. Therefore, the eﬀectiveness of
IGG strategies depends on policymakers’ attention to the underlying market and
governance failures that prevent current growth paths from being inclusive and
green (Bouma and Berkhout 2015).
As how to make growth greener and more inclusive, most countries in the world
have enacted a series of environmental and social regulations. Currently, schol-
ars mainly discuss the impact of environmental regulations on GG at the
national,
regional, industry, and corporate levels (Filippini and Srinivasan 2021; Xie
etal.
2017; Yang etal. 2021). However, whether the combination of environmental and
social regulations can promote IGG has not yet been studied. Although there is
not
yet a consensus or established models for measuring an IGG index, it is still
possible
to analyze it from a total factor productivity (TFP) perspective (along with
green
growth and inclusive growth), taking into consideration economic, social, and
envi-
ronmental factors as outputs. Data envelopment analysis (DEA) technique has been
widely applied (Lahouel 2016; Nakano and Managi 2008) in previous research to
carry out multidimensional input–output analysis, i.e., evaluating desirable
outputs
(economic and social beneﬁts) against undesirable outputs (environmental
burdens).
Combined with the Malmquist index, DEA is currently one of the most important
methods employed to calculate the TFP (Lahouel etal. 2021, 2022). Therefore,
this
paper introduces the concept of inclusive green growth total factor productivity
(IGGTFP) and aims to analyze the impacts of environmental and social regulations
on inclusive and green development in a panel of OECD countries. The IGGTFP is
used to measure a country’s competitiveness instead of the traditional TFP,
because
it adds energy consumption, environmental pollution, and social welfare as addi-
tional constraints into the analytical framework of economic development (Jiang
etal. 2021; Zhao and Yang 2017). This paper adopts the global Malmquist-Luen-
berger productivity index (GMLPI) based on the slacks-based measure directional
distance function (SBM-DDF) to measure the IGGTFP.
It was argued that environmental and inclusiveness outcomes are not simply a
function of economic development, but also a consequence of policy choices (Li
etal. 2019; Wang and Shen 2016). Smart solutions to tackle free rider behavior,
market and governance failures are needed by making use of standards,
incentives,
regulations, and taxes (Bouma and Berkhout 2015; Esty and Porter 2005). In this
respect, a country’s environmental regulatory regime and other institutional
under-
pinnings (e.g., labor regulations) might signiﬁcantly inﬂuence IGG.
As far as we know, no study has simultaneously examined the impact of environ-
mental and employment protection regulations on inclusive green growth. There-
fore, our main research question can be deﬁned as follows: What are the eﬀects
of
the environmental and employment policy stringency as well as their interaction
on
inclusive green growth of OECD countries?


Environmental Economics and Policy Studies
1 3
This article contributes to ﬁlling this gap by providing cross-country evidence
on the impact of stringent environmental and employment protection regulations
on
inclusive green growth productivity to better inform policy action, in a sample
of
OECD countries. Our analysis is conducted at the national level because Porter’s
original hypothesis focused on competition between nations considering the
eﬀects
of environmental regulations (Ambec etal. 2013). Most of the existing empirical
research tests separately either the impacts of environmental regulations on
green
growth productivity or the growth productivity impacts of EPL. However, as men-
tioned above, strong inclusive green growth is the sine qua none of the improved
welfare and living standards, which are determined by green and social policies.
To
ﬁll this gap, we conduct an empirical analysis of the eﬀect of environmental
policy
stringency (OECD’s EPS) and employment protection legislation (OECD’s EPL) on
IGGTFP.
The contribution of this paper is threefold. First, it can enrich the theoreti-
cal research of inclusive green growth, clarify the impact mechanism of EPS and
EPL, and provide reliable experience support for the construction of inclusive
green
growth theory. Second, in the context of methodology, our contribution relies on
applying the SBM-DDF model, incorporating GHG emissions as undesirable out-
put and the human development index (HDI) as a desirable output reﬂecting social
welfare, to measure the Global Malmquist-Luenberger productivity index of OECD
countries instead of traditional TFP. Third, from an empirical research
perspective,
this study can provide support or challenge to Porter’s hypothesis and the
ineﬃcient
retention argument, policy suggestions on how to better conceive environmental
and
employment protection regulations to improve IGG levels.
The rest of the paper is structured as follows. Section2 is devoted to the
presenta-
tion of the literature review regarding the impact of environmental and
employment
regulations on economic/inclusive/green growth. Section3 presents the methodol-
ogy, data sources and variables, including models such as the GMLPI based on the
SBM-DDF model and the econometric model. Then, Sect.4 displays and discusses
the empirical results. The ﬁnal section concludes the paper and provides policy
implications.
2 Literature review
One particular focus of attention has been the issue of whether environmental
regulations inﬂuence the patterns of green growth, since the seminal articles of
Porter (1991) and Porter and van der Linde (1995) suggesting that “properly
designed environmental regulation (economic instruments like green taxes and
tradeable permits) can trigger innovation that may partially or more than fully
oﬀset the costs of complying with them” (Porter and van der Linde 1995, p. 98),
thereby improving a country’s productivity. The argument of this “Porter hypoth-
esis” (PH) is that strict environmental regulations if they are well-designed
and
implemented correctly, can induce innovation that may oﬀset the short-term pri-
vate costs of such regulations and ultimately may lead to a competitive
advantage
in the country level (Lahouel etal. 2020; Managi 2004; Managi etal. 2005).
Both


1 3
Environmental Economics and Policy Studies
regulations and strict environmental standards lead to win–win situations where
social welfare and private net beneﬁts of ﬁrms (i.e., competitiveness and
resource
productivity) can be increased (Porter and van der Linde 1995). However, envi-
ronmental regulations a la Porter have received skeptical responses from econo-
mists who have objected to the idea of a “free lunch”. Indeed, conventional eco-
nomic wisdom holds that strict environmental regulations imply private costs for
prevention and remediation. Therefore, adding new constraints to the production
possibility set will have a negative eﬀect on a country’s competitiveness (Kumar
and Managi 2009; Palmer etal. 1995). According to Brännlund and Lundgren
(2009), environmental regulations not only impose costs and negatively aﬀect
competitiveness, but also may have negative social economic impacts such as
lower employment and welfare. Because welfare depends on income distribution
and employment, these regulations may create jobs for some types of workers in
some areas and eliminate jobs for other types of workers in other areas (World
Bank 2012). In this regard, Foa (2009) argues that environmental regulations
have distributive eﬀects such as leading to improved gender equality, with many
economic and social beneﬁts. Accordingly, the present study not only considers
the eﬀect of environmental regulations on GG but also accounts for its heterog-
enous eﬀects on diﬀerent social groups and regions, which means that environ-
mental regulations may aﬀect IGG.
On the other hand, countries aiming for an inclusive and green strategic transi-
tion need to design coherent policies to anticipate how market changes will
aﬀect
employment and inclusive growth. The primary motivation for many of these poli-
cies is to encourage producers to change their behavior to increase employment
and
distribute it fairly. Governments need to design and maintain a
business-friendly
environment by eliminating market distortions to create employment opportunities
and promote higher IGG productivity (Ali and Zhuang 2007). Over the past few
decades, employment protection legislation (EPL) has been at the center of
policy
concerns in OECD countries. The attention has focused on the impact of EPL on
ﬁrms’ incentives to invest in innovation and growth that improve productivity.
So,
how does EPL aﬀect productivity? To date, several theoretical and empirical
studies
have viewed EPL—generally designed to protect employment and increase job sta-
bility by reducing job destruction—as a cost borne by ﬁrms, and the focus has
been
on employment and labor market ﬂows (Bassanini etal. 2009; Damiani etal.
2016).
Such reforms are usually justiﬁed by the economic argument of ineﬃcient
retention
(Bierhanzl 2005). According to this argument, high ﬁring costs should be reduced
because they are likely to prevent ﬁrms from shedding underperforming employees.
At the same time, low worker turnover—induced by high EPL—would undermine
the ability of a labor market to match the right workers to the right jobs
eﬃciently.
Equilibrium models of the labor market show that strict employment protection
implies a slower speed of adjustment towards equilibrium. For example, Cazes
(2013) and Noelke (2016) reach similar conclusions about the negative impact of
EPL on occupational mobility. Therefore, average productivity declines and the
eco-
nomic system becomes less competitive. In turn, low competitiveness weakens eco-
nomic growth, employment and, ultimately, welfare (Berton etal. 2017; Garibaldi
and Violente 2005).


Environmental Economics and Policy Studies
1 3
However, this perspective only considers the way in which EPL aﬀects the allo-
cation of given skills to jobs but neglects the fact that skill development is
also
impacted by EPL. Belot etal (2007) propose a framework in which, by providing
additional job security, employment protection can increase workers’ incentives
to
invest in match-speciﬁc human capital by increasing the probability of match
sur-
vival, thereby improving productivity growth. However, there is a trade-oﬀ
between
the positive eﬀects of EPL and the costs raised upon separations (Bassanini
etal.
2009). Therefore, Belot etal. (2007) show that the relationship between EPL and
productivity is well described by an inverted U-shaped curve: a strictly
positive
optimal level of EPL can be identiﬁed, so that increasing employment protection
does indeed improve welfare. The optimal level of EPL depends on other labor
market institutions that regulate wage rigidity and redistribution patterns
(Damiani
etal. 2016). In terms of empirical evidence, few studies have examined the
produc-
tivity impacts of EPL in cross-country analyses, and the results are
inconclusive.
For example, Bassanini etal. (2009) ﬁnd that the regulations governing employee
dismissals in OECD countries have a depressive eﬀect on productivity growth in
industries where layoﬀ restrictions are more likely to be binding. Bartelsman
etal.
(2013) ﬁnd that high-risk innovative industries are smaller in countries with
strict
EPL, which helps explain the productivity slowdown in Europe relative to the
United States since the mid-1990s. In the same vein, Berton etal. (2017) exam-
ine the eﬀects of the “Fornero Law”, introduced as part of Italy’s 2012
austerity
reforms and relaxing employment protection requirements, show that reducing the
EPL promotes labor reallocation, increases good matches, and boosts
productivity.
Conversely, Nickell and Layard (1999) and Koeniger (2005) ﬁnd a positive
relation-
ship between EPL stringency and TFP growth and R&D intensity, respectively, for
samples of OECD countries. This is because productivity improvements depend on
worker cooperation and investment in on-the-job training, which in turn are
helped
by layoﬀ costs. Based on a study of 17 OECD countries and three periods, from
the
early 1960s to the late 1990s, Belot etal. (2007) show that the relationship
between
EPL and productivity is nonlinear.
3 Methods anddata
3.1 Inclusive green growth index
The combination of the data envelopment analysis method with the Malmquist-
Luenberger productivity index (MLPI) has received increased scholarly attention
for the measurement of either green growth or inclusive growth. DEA maximizes
economic outputs and minimizes undesirable outputs (e.g., wastewater, greenhouse
gases, etc.), thus laying the foundation for an inclusive green growth metric.
Spe-
ciﬁcally, the MLPI based on DDF (Chung etal. 1997), considering both desirable
and undesirable outputs, makes DEA one of the most important methods for IGG
estimation (Jiang etal. 2021; Song etal. 2020). However, when linear
programming
is implemented, MLPI can have infeasibility problems due to inter-period
computa-
tion, making the estimates unstable or inconsistent with actual production
activities


1 3
Environmental Economics and Policy Studies
(Xue and Harker 2002). Two major developments in DEA have been advanced to
overcome these problems. First, Tone (2001) extends traditional DEA models and
proposes a non-radial slacks-based measure (SBM) model that accounts for ineﬃ-
ciencies associated with an excess inputs and shortfalls of outputs. Then,
Fukuy-
ama and Weber (2009) and Färe and Grosskopf (2010) combine SBM and DDF to
obtain a new SBM-DDF model, which solves the problem of eﬃciency overestima-
tion. Second, based on the concept of global production possibility set, Pastor
and
Lovell (2005) develop the global Malmquist index. Oh (2010) further extended
their
work and propose the global Malmquist-Luenberger productivity index (GMLPI)
by incorporating undesirable outputs. Therefore, GMLPI is circular and free from
infeasibility problems (Oh 2010). Currently, most researchers opt to use the
non-
radial direction distance function to measure total factor productivity, which
allows
for the resolution of the measurement error problem by introducing input and
out-
put slack variables (Jiang etal. 2021). Based on the developments in DEA models
presented above, this paper adopts the GMLPI based on SBM-DDF to measure the
inclusive green growth total factor productivity index (IGGTFP) in OECD
countries.
3.1.1 Slacks-based measure directional distance function (SBM-DDF)
Each OECD country consists of a decision-making unit (DMU) and is denoted
DMUk
(
k=1, 2, …,K
). The production technology for each DMU pro-
duces
N
desirable outputs:
y
=
(
y
1
,…,y
N)
∈R
+
N
and
I
undesirable outputs:
b
=
(
b
1
,…,b
I)
∈R
+
I
, by using
M
inputs:
x
=
(
x
1
,…,x
M)
∈R
+
M
. The expression
(
x
t
k
,y
t
k
,b
t
k)
reﬂects the set of inputs and outputs of a country
k
in period
t
. So, the
contemporaneous production possibility set (PPS), of the current period is:
where
zt
k
denotes the weight of each cross-section observation, and the constraints.
∑K
k=1
zt
k
=1, zt
k
≥0, indicatesthatthePPSexhibitsvariabler etur nstoscale
.
However,
the contemporaneous
Pt(xt)
often yields to a counterintuitive long-term technologi-
cal regress (Xue and Harker 2002). Therefore, Oh (2010) proposes to replace
Pt(xt)
with a global PPS,
PG(x)
, where
PG
(x)=P
1(
x
1)
∪P
2(
x
2)
∪⋯∪P
T(
x
T)
, used for
building a single global production frontier emphasizing the consistency and
com-
parability of eﬃciency.
PG(x)
can be expressed as follows:
(1)
P
t
(
xt
)
=
{(
yt,bt
)
∶
T
∑
t=1
K
∑
k=1
zt
kyt
kn ≥yt
kn,∀n;
T
∑
t=1
K
∑
k=1
zt
kbt
ki =bt
ki,∀i;
K
∑
k=1
zt
k=1, zt
k≥0, ∀k
}
(2)
P
G(x)=
{(
yt,bt
)
:
T
∑
t=1
K
∑
k=1
zt
kyt
kn
≥
yt
kn,∀n;
T
∑
t=1
K
∑
k=1
zt
kbt
ki =bt
ki,∀i;
T
∑
t=1
K
∑
k=1
zt
kxt
km
≤
xt
km,∀m;
K
∑
k=1
zt
k=1, zt
k
≥
0, ∀k
}


Environmental Economics and Policy Studies
1 3
Although DDF has many favorable features, Fukuyama and Weber (2009) argue
that it does not account for slacks in the constraints when estimated by
applying
DEA, which are important sources of ineﬃciency. Hence, we follow Fukuyama and
Weber (2009) and deﬁne the global SBM-DDF that covers undesirable outputs as:
where
x
t,k
′
,y
t,k
′
andb
t,k
′
are the vectors of inputs and outputs of a country
k
in period
t
.
The directional vectors
gx
,gyandgb
indicate the decrease of inputs, increase of desir-
able outputs, and the decrease of undesirable outputs, respectively.
Sx
m
,S
y
n
andS
b
i
rep-
resent the slacks in inputs, desirable outputs and undesirable outputs,
respectively.
3.1.2 Global Malmquist-Luenberger productivity index (GMLPI)
Although the GMLPI can correct the defect of no solution when linear program-
ming the MLPI, a single GMLPI cannot solve the radial angle problem. Therefore,
with reference to Oh (2010), this paper adopts the GMLPI based on SBM-DDF as
follows:
In this study, we measure the inclusive green growth index by calculating the
change in the GMLPI from period
t
to
t+1
. GMLPI
>(<)1
corresponds to produc-
tivity increase (decrease). GMPLI
=1
indicates that productivity tends to be stable.
Further, GMLPI can also be derived as the technical eﬃciency change
(
GEC
t+1
t)
and
the technological change
(
GTC
t+1
t)
as follows:
(3)

DG
v
(
xt,k′
,yt,k′
,bt,k′
,gx,gy,gb
)
= max
1
N
∑
M
m=1
S
x
m
gx
m
+1
M+I
[∑
n
n=1
S
y
n
gy
n
+
∑
I
i=1
S
b
i
gb
i
]
2
s
.t.
⎧
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎩
∑
T
t=1
∑
K
k=1zt
kxt
km +Sx
m=xt
k′m,∀m
;
∑T
t=1∑K
k=1zt
kyt
kn −Sy
n=yt
k′n,∀n;
∑T
t=1∑K
k=1zt
kbt
ki +Sb
i=bt
k′i,∀i;
∑
K
k=1zt
k=1, zt
k≥0, ∀k
Sx
m≥0, ∀m;Sb
i≥0, ∀i;Sy
n≥0, ∀n
(4)
GMLPI
t+1
t=
1+

DG
V(xt,yt,bt,gx,gy,gb)
1+

DG
V
(xt+1,yt+1,bt+1,gx,gy,gb
)
GMLPIt+1
t
= GEC
t+1
t
×GTC
t+1
t,
GEC
t+1
t=
1+

Dt
V(xt,yt,bt,gx,gy,gb)
1+

Dt+1
V
(xt+1,yt+1,bt+1,gx,gy,gb
)


1 3
Environmental Economics and Policy Studies
GECt+1
t
corresponds to the eﬃciency change of inclusive green growth that meas-
ures the catch-up eﬀect, capturing the degree a DMU moves closer to the
contempo-
raneous frontier over time.
GTCt+1
t
corresponds to the technological change of inclu-
sive green growth that captures whether the contemporaneous frontier moves
closer
to the global frontier over time. When
GECt+1
t
and
GTCt+1
t
are greater than 1 (less
than 1), they represent an increase (decrease) in technical eﬃciency and
technologi-
cal progress, respectively.
3.2 Data andvariables
The purpose of this paper is to explore the eﬀect of the environmental policy
stringency (EPS) and the employment protection legislation (EPL) on inclusive
green growth (IGG) on a balanced panel of 26 OECD countries over the period
1990–2012. Data on inputs and outputs for the calculation of the GMLPI are
obtained from the World Development Indicators of the World Bank and the Pen
World Tables9.0 databases. The output corresponding to the Human Development
Index (HDI) is collected from United Nations Development Programme (UNDP).
Data on EPS, EPL, and control variables in the econometric model are sourced
from
OECD.Stat database. We follow the previous literature for the choice of these
vari-
ables (e.g., Lahouel etal. 2021; Jiang etal. 2021; Wang etal. 2019; Xie
etal. 2017).
Three inputs are included corresponding to the capital stock, labor, and energy
con-
sumption. The capital stock represents the gross capital formation in each
country
and is expressed in US dollars (millions) at constant 2011 national prices.
Labor is
the labor force measured by the number of employed (millions) workers at the
year’s
end. Energy consumption is expressed in Terajoules (thousands). Two desirable
out-
puts are considered in our analysis. The ﬁrst is the economic growth proxied by
the
GDP, which is expressed in US dollars (billions) at constant 2011 national
prices.
The second corresponds to the social output, which expresses inclusive growth.
It is
measured by the HDI. The undesirable output is measured by greenhouse gas emis-
sions and expressed in tones of CO2 equivalent (thousands).
For the determinants of IGG, our main explanatory variables are EPS and EPL.
EPS index is a composite indicator of environmental policy stringency, which is
developed by the OECD for the 27 OECD countries for the period 1990–2012 and is
publicly available in OECD.Stat (see Albrizio etal. 2017; Botta and Kozluk
2014).
The EPS index aggregates both market-based and non-market-based indicators,
which are given equal weights. It is calculated using the following formula:
(5)
GTC
t+1
t=
[
1+

DG
V(xt,yt,bt,gx,gy,gb)
]
∕
[
1+

Dt
V(xt,yt,bt,gx,gy,gb)
]
1+

DG
V
(xt+1,yt+1,bt+1,gx,gy,gb)∕1+

Dt+1
V
(xt+1,yt+1,bt+1,gx,gy,gb
)
(6)
EPS index =(0.5 ∗market −based instruments)
+(0.5 ∗non −market −based instruments)


Environmental Economics and Policy Studies
1 3
The EPL index, which is developed by the OECD and available OECD.Stat, is
an indicator of employment protection legislation, which measures the stringency
of regulations on layoﬀs and the use of temporary contracts. For each year, the
EPL
index refers to the regulations in force on January 1. Based upon a review of
the
literature,1 we retain four variables as being of particular importance in
explaining
inclusive green growth. Government budget allocation for R&D (GBRD) reﬂects
the investment in research and development activities, which is supposed to
raise the
technical level of production activities, and thus increase IGG to a higher
level. Data
on a patent for environmental-related technologies (PERT) are used as a measure
of technological innovation (i.e., an indicator of the innovative performance of
an
economy) as they focus on the outputs and impacts of the inventive process.
Foreign
direct investment (FDI) can play a signiﬁcant role in the convergence of
inclusive
and green technical eﬃciency in various OECD countries as it can bring a
technol-
ogy spillover from the endogenous advantages of foreign ﬁrms. Renewable energy
consumption (REC) denotes clean energy usage and is expected to improve IGG
by helping to decrease the quantity of negative environmental inputs. The
required
input and output variables and their measures are described in Table1.
3.3 Econometric framework
The estimation strategy for this study is a two-step system generalized method
of
moments (Sys-GMM). The current values of our dependent variable, inclusive green
growth (IGG), are likely to depend on their one-year lagged values, which can be
accounted for using dynamic panel data estimation techniques. Several
motivations
guide us towards the choice of an endogeneity-robust two-step Sys-GMM estimator.
First, the number of countries (N = 26) is greater than the number of years (T =
23)
to control for dynamic panel bias (Roodman 2009a). Second, for small samples the
Sys-GMM estimator, developed by Blundell and Bond (1998), appears to be more
suitable than the diﬀerence GMM (Diﬀ-GMM) estimator suggested by Arellano
and Bond (1991) who might produce biased estimates. Third, given that the data
structure is a panel, in the adopted Sys-GMM method, cross-country variations
are
considered in the estimations. Fourth, endogeneity is addressed by the
estimation
process from two main levels. On the one hand, the concern about simultaneity
or reverse causality is considered by means of an instrumentation process. On
the
other hand, time-invariant variables are also tackled for the unobserved
heterogene-
ity. In the present study, we adopt the Roodman (2009a, 2009b) extension of
Arel-
lano and Bover (1995), which has been considered to restrict over-identiﬁcation
and
reduce the proliferation of instruments (Love and Zicchino 2006). Hence, the
chosen
speciﬁcation is a two-step Sys-GMM with forward orthogonal deviations instead of
diﬀerencing. We prefer the two-step to the one-step procedure because the latter
is
homoscedasticity-consistent while the former controls for heteroscedasticity.
1 See, for example, Ambec etal. (2013), Cecere and Corrocher (2016), Johnstone
etal. (2012), Maji
(2019), Song etal. (2019), Xie etal. (2017), Zhu and Ye (2018) are some
studies that include a sample of
OECD and non-OECD economies.


1 3
Environmental Economics and Policy Studies
Table 1 Variable deﬁnition
PWT 9.0, WDI, UNDP indicate Penn World Trade version 9.0, World Development
Indicators, United Nations Development Programme, respectively
Variables Sign Description Source Unit
Capital stock K Input PWT 9.0 Us dollars, million
Labor force L Input WDI Employed worker, million
Energy consumption EC Input WDI Terajoules, thousand
Gross domestic product GDP Output (desirable) WDI Us dollars, billion
Human development index HDI Output (desirable) UNDP
Greenhouse gas GHG Output (undesirable) OECD.Stat Tone of CO2 equivalent,
thousand
EPS index EPS Explanatory 1 OECD.Stat –
Employment protection legislation EPL Explanatory 2 OECD.Stat –
Government budget allocation for R&D GBRD Control WDI Us dollars, million
Patent for environmental-related Technologies PERT Control WDI Number of patents
Foreign direct investment FDI Control WDI % of GDP
Renewable energy consumption REC Control WDI % Of total ﬁnal energy consumption


Environmental Economics and Policy Studies
1 3
The following equation summarizes the two-step Sys-GMM estimation procedure
for the full model form:
where
IGG
is the dependent variable standing for inclusive green growth. Coun-
try and time are denoted by the subscripts i and t, respectively.
EPS
and
EPL
are
the independent variables and stand for the environmental policy stringency and
employment protection legislation, respectively. As
IGG
requires changes in tech-
nology and/or production processes which may take some time to occur, changes in
the severity of environmental and employment regulations adopted today will
aﬀect
countries’
IGG
a few years later (Albrizio etal. 2017; Lanoie etal. 2008). Therefore,
we allow a one-year lag in the variables of regulation stringency. The matrix
Xit
con-
tains a set of control variables (GBRD, PERT, FDI, and REC) that are 1-year
lagged
to avoid two-way causation with
IGG
(Rubashkina etal. 2015). Individual (country)
eﬀects are captured by
𝜇i
and
𝜀i,t
stands for the disturbance term.
𝜏
represents the
coeﬃcient of auto-regression (lag order). We estimate a ﬁrst model (model 1) by
considering variables in level (
𝜏=0
) and a second model (model 2) by considering
a one-year lag (
𝜏=1
). In addition, we introduce a cross-term of the environmental
policy stringency and the employment protection legislation
(
EPS
i,t−𝜏
×EPL
i,t−𝜏)
to explore whether there is a substitution eﬀect or a complementary eﬀect
between
these two types of regulations. If the estimated coeﬃcient of the interaction
eﬀects
is positive, the two types of regulations have a complementary relationship.
How-
ever, if it is negative, EPS and EPL are mutual substitutes.
As mentioned above, we use the two-step Sys-GMM as our primary estima-
tion technique to alleviate the concerns about dynamic panel bias and endogene-
ity. Sys-GMM technique involves a system of equations in diﬀerences and in lev-
els which allow us to treat all the explanatory variables in Eq.(7) as
endogenous.
For each model, and to apply the procedure recommended by the GMM method
(i.e., identiﬁcation and exclusion restrictions), a certain number of diagnostic
tests
were considered. First, we verify the absence of autocorrelation with the
second-
order Arellano and Bond autocorrelation test AR (2) in diﬀerence.2 Second, to
con-
ﬁrm the absence of correlation between the error term and the set of
instrumental
variables we employ the Sargan or Hansen overidentifying restrictions (OIR)
tests.3
The validity of the Sys-GMM estimator depends on whether the lagged instrumen-
tal variables are exogenous (Roodman 2009a, b). For this reason, we empirically
(7)
IGG
it
=𝛼
0
+𝛼
1
IGG
i,t−1
+𝛼
2
EPS
i,t−𝜏
+𝛼
3
EPL
i,t−𝜏
+𝛼
4
(EPS
i,t−𝜏
×EPL
i,t−𝜏
)
+𝛼5Xi,t−𝜏+𝜇i+𝜀i,t
2 The null hypothesis of the second-order Arellano and Bond autocorrelation test
(AR (2)) in diﬀerence
for the absence of autocorrelation in the residuals should not be rejected for a
good speciﬁcation of the
model.
3 It is relevant to highlight that the discussed criterion is broadly consistent
with a standard instrumen-
tal variable (IV) approach, in which failure to reject to null hypothesis of the
Hansen-Sargan Overi-
dentifying Restrictions (OIR) test is a sign that the instrumental variables
aﬀect the outcome variables
exclusively through the adopted mechanisms. The OIR tests should not be
signiﬁcant because their null
hypotheses are the positions that instruments are valid or not correlated with
the error terms.


1 3
Environmental Economics and Policy Studies
check the validity of the Sys-GMM estimator through the use of the Hansen-J test
of over-identiﬁcation and diﬀerence-in-Hansen (DHT) test of exogeneity of
instru-
ment subsets. Furthermore, the current study considers all explanatory variables
as
predetermined variables, whereas the time-invariant variables (i.e., year
dummies
variables) are strictly exogenous. Hence, the procedure for treating ivstyle
(years) is
‘iv (years, eq(diﬀ))’ whereas the gmmstyle is employed for predetermined
variables.
Fischer test for the joint validity of estimated coeﬃcients is also used.4
4 Empirical results anddiscussion
To calculate the IGG index and its sources by OECD countries over the period
1990–2012,5 we run the model presented in Eqs.(4) and (5) using the GMLPI with
the SBM-DDF model. Table2 reports the geometric average6 values of the GMLPI
and its decompositions: eﬃciency change and technological change. The GMLPI
increased on average by 1% from 1990 to 2012, which denotes an improvement in
inclusive green growth. All OECD countries showed an increase in their inclusive
green growth except Austria, Greece, Portugal and Turkey that recorded a decline
by 0.2%, 0.2%, 0.4%, and 0.4%, respectively. This improvement is the result of a
combination of two small positive complementary eﬀects: an increase in eﬃciency
change of 0.3% and increase in technological change of 0.7%.
The discussion of the results from the econometric estimations begins with sum-
mary statistics and correlation analysis of the regression variables as
presented in
Tables5 and Figs.1 and 2 in the appendix. The results show the absence of any
correlation that exceeds 0.723 between the explanatory variables except for the
one
between EPS and EPS×EPL, which is a completely expected result, not posing any
problems in our estimations, since the interaction term includes the EPS
variable.
None of the correlation coeﬃcients between the independent variables is greater
than the 0.80 threshold. Thus, multicollinearity is unlikely to be an issue
among
these variables.
The regression results are presented in Tables3 and 4 and proceed in two
stages.7 As reported in Table3 (the same reasoning was also applied to
Table4),
the Hansen OIR test yields the p-value of 0.109 (model 1) and 0.910 (model 2)
conﬁrming that the instruments (as a group) used in the Sys-GMM model are
valid. We also follow Roodman (2009a) and apply the diﬀerence-in-Hansen tests
4 Given the adopted GMM methodology, the assumption of exclusion restrictions is
conﬁrmed if the
DHT on the exogeneity of instruments is not valid. A rejection of the null
hypothesis proves that the
adopted strictly exogenous variables are not valid.
5 Tables6, 7, and 8 of the appendix show the average of inclusive green growth
productivity (GMLPI),
eﬃciency change (EC) and technological change (TC) calculated using Eqs.4 and
5.
6 Since MPI is multiplicative, the use of geometric averages ensures that the
multiplicative decomposi-
tion shown by Eq.(5) holds exactly (Kerstens etal. 2019).
7 For comparison purposes, Tables3 and 4 are supplemented with ordinary least
squares (OLS) regres-
sion results to show how their estimated coeﬃcients diﬀer from GMM estimators,
which are known to be
more robust (Lahouel etal. 2019).


Environmental Economics and Policy Studies
1 3
Table 2 Inclusive green growth and its sources
Country GMLPI Eﬃciency change Technical change Country GMLPI Eﬃciency change
Technical change
Australia 1.022 0.989 1.034 Japan 1.000 1.000 1.000
Austria 0.998 0.996 1.003 Korea 1.025 1.015 1.009
Belgium 1.005 0.995 1.010 Netherlands 1.023 1.008 1.015
Canada 1.012 0.999 1.013 Norway 1.004 1.000 1.004
Czech Republic 1.012 1.011 1.002 Poland 1.022 1.031 0.992
Denmark 1.019 1.015 1.004 Portugal 0.994 0.972 1.022
Finland 1.010 1.010 1.000 Slovak Republic 1.000 1.000 1.000
France 1.016 1.005 1.012 Spain 1.009 0.996 1.012
Germany 1.017 1.010 1.008 Sweden 1.013 1.008 1.005
Greece 0.998 0.994 1.004 Switzerland 1.012 1.000 1.012
Hungary 1.009 1.009 1.000 Turkey 0.994 1.000 0.994
Ireland 1.000 1.000 1.000 United Kingdom 1.025 1.023 1.002
Italy 1.006 0.994 1.012 United States 1.018 1.000 1.018


1 3
Environmental Economics and Policy Studies
of exogeneity to the subsets of Sys-GMM-type instruments and standard instru-
ments. The tests are under the null hypothesis of joint validity of a speciﬁc
instru-
ment subset. Speciﬁcally, we test the validity of several subsets of
Sys-GMM-type
instruments including: (i) GMM-type instruments (as a group) for the equation
in levels; (ii) GMM instruments for lagged dependent variable for the equation
in diﬀerences; (iii) GMM-type instruments for lagged dependent variable for the
equation in levels. The subset of standard instruments for the equation in
levels is
also tested for their validity.
In the ﬁrst instance, Table3 presents the results of the reduced-form model in
which we examine the pure eﬀects of EPS, EPL, and their interaction on IGG. The
estimated coeﬃcients of model 1 and model 2 consider variables in level (
𝜏=0
)
and variables with a one-year lag (
𝜏=1
), respectively. The coeﬃcient of lagged
inclusive green growth is signiﬁcantly positive for both models, indicating that
IGG of the current period is inﬂuenced by the level of IGG recorded in the
previous
period. Our results are homogenous across the two models (i.e., current period
and
one-year lagged environmental and employment regulations) and reveal that EPS,
EPL, and their interaction have signiﬁcant positive eﬀects on IGG. This implies
that
more stringent environmental and employment regulations stimulate inclusive and
green productivity growth in OECD countries. First, our results are in line with
the
“Porter hypothesis” because when environmental regulation stringency increases,
IGG improves. That is, when contemporaneous EPS increases by 1%, IGG improves
by 0.190% and when the one-year lag eﬀect of EPS is considered, IGG improves
Table 3 Reduced form model (without control variables)
***, **, * indicate signiﬁcance at 1%, 5% and 10%, respectively. The number in
the parentheses is the
p-value. IGG, EPS and EPL denote inclusive green growth, environmental policy
stringency, employ-
ment protection legislation.
𝜏
represents the coeﬃcient of auto-regression (lag order)
Dependent variable:
IGG
Estimation method Two step Sys-GMM OLS
Models Model 1 (
𝜏=0)
Model 2 (
𝜏=1)
Model 1 (
𝜏=0)
Model 2 (
𝜏=1)
Constant 1.447*** (0.000) 1.670*** (0.000) 1.022*** (0.000) 1.118*** (0.000)
IGGt−1
0.421*** (0.000) 0.445*** (0.000) 0.285** (0.04) 0.235* (0.07)
EPSt−𝜏
0.190** (0.037) 0.111** (0.012) 0.101* (0.09) 0.851 (0.125)
EPLt−𝜏
0.145*** (0.007) 0.122** (0.014) 0.095* (0.07) 0.091 (0.125)
EPSt−𝜏×EPLt−𝜏
0.059** (0.025) 0.012** (0.013) 0.014** (0.012) 0.012** (0.011)
Observations 542 540 544 542
Fisher statistic 14.80*** 6.64***
Year dummies Yes Yes No No
Nb. of instruments 16 18 - -
Countries/groups 26 26 26 26
AR (1) (p-value) (0.092) (0.085)
AR (2) (p-value) (0.117) (0.197)
Hansen OIR (p-value) (0.109) (0.910)
Sargan OIR (p-value) (0.890) (0.119)


Environmental Economics and Policy Studies
1 3
by 0.111%. Similarly, Wang etal. (2019) ﬁnd that both current and lagged envi-
ronmental regulations are positively correlated with green productivity growth
in
OECD countries. Our results are consistent with the idea that strict
environmental
regulation creates more “innovation oﬀset eﬀects” than “compliance costs”.
There-
fore, as marginal pollution control expenditures tend to decrease over time,
ﬁrms
place more emphasis on improving production processes and using cleaner produc-
tion technologies.
Second, we ﬁnd that stringent labor market regulations are positively associ-
ated with IGG, which is in line with the existing evidence in the OECD countries
that increasing employment protection does indeed improve welfare. The estimated
coeﬃcient of the contemporaneous and the 1-year lagged EPL variable are positive
(0.145 and 0.122) and signiﬁcant at 1% and 10% levels, respectively. Our results
are consistent with those of Nickell and Layard (1999) and Koeniger (2005) who
ﬁnd that strict EPL positively impacts productivity growth. Hence, employment
protection could help stimulating workers’ investment in ﬁrm-speciﬁc skills and
determining the mix of these skills. We argue that the positive welfare eﬀects
of
Table 4 Model with added control variables
𝜏
Represents the coeﬃcient of auto-regression (lag order)
IGG inclusive green growth, EPS environmental policy stringency, EPL employment
protection legisla-
tion, GDRD patent for environment-related technologies (PERT), PERT patent for
environment-related
technologies (PERT), FDI foreign direct investment (FDI), REC renewable energy
consumption (REC)
***, **, *Indicate signiﬁcance at 1%, 5% and 10%, respectively. The number in
the parentheses is the
p-value
Dependent variable: IGG
Estimation method Two step Sys-GMM OLS
Models Model 1 (
𝜏=0)
Model 2 (
𝜏=1)
Model 1 (
𝜏=0)
Model 2 (
𝜏=1)
Constant 1.326*** (0.000) 1.547*** (0.000) 1.061*** (0.000) 1.142*** (0.000)
IGGt−𝜏
0.235*** (0.000) 0.343*** (0.000) 0.131** (0.056) 0.142* (0.091)
EPSt−𝜏
0.184*** (0.003) 0.166*** (0.002) 0.104* (0.078) 0.106* (0.091)
EPLt−𝜏
0.112** (0.021) 0.138** (0.023) 0.102** (0.041) 0.118** (0.013)
EPSt−𝜏×EPLt−𝜏
0.041** (0.023) 0.023** (0.012) 0.011* (0.093) 0.123** (0.112)
GBRDt−𝜏
0.503** (0.031) 0.341** (0.012) 0.103** (0.011) 0.147** (0.011)
PETt−𝜏
−0.218 (0.444) −0.051 (0.124) −0.117 (0.141) −0.151 (0.321)
FDIt−𝜏
0.08** (0.029) 0.03** (0.012) 0.08** (0.029) 0.03** (0.012)
RECt−𝜏
-0.004 (0.244) −0.07 (0.113) −0.012 (0.247) −0.071 (0.103)
Observations 477 470 478 471
Year dummies Yes Yes No No
Nb. of instruments 16 18 – –
Countries/groups 26 26 26 26
Fisher statistic 11.43*** 7.09***
AR (1) (p-value) (0.049) (0.092)
AR (2) (p-value) (0.112) (0.155)
Hansen OIR(p-value) (0.918) (0.822)
Sargan OIR (p-value) (0.118) (0.261)


1 3
Environmental Economics and Policy Studies
employment protection are higher than the costs of workers’ reallocation which
may
hinder productivity growth. Third, the estimated coeﬃcients cross-term are
positive
and signiﬁcant no matter whether we include contemporaneous or one-year lagged
eﬀect, meaning that environmental regulation and employment protection have com-
plementary eﬀects on promoting IGG. This result conﬁrms previous evidence that
environmental and inclusion outcomes are not simply a function of economic
devel-
opment, but also a consequence of public policy choices such as environmental
and
employment regulatory regimes (e.g., Li etal. 2019; Wang and Shen 2016).
In the second instance, Table4 reports the results from the estimation of the
full
model with the introduction of control variables that may inﬂuence the impacts
of
environmental and employment regulation on IGG. Our results remain qualitatively
similar to those of our main inferences, after controlling for additional
explana-
tory variables. That is, EPS, EPL and their interaction have positive and
signiﬁcant
eﬀects on IGG. For the control variables, only GBRD and FDI exert a signiﬁcant
positive eﬀect on IGG no matter whether contemporaneous or 1-year lagged eﬀects
are considered. Because the inclusive green growth index has not yet been widely
discussed with respect to the impacts of environmental and employment
regulations,
our results could not be compared to studies that have examined these eﬀects on
green productivity growth. However, we can make a connection with previous stud-
ies such as those of Albrizio etal. (2017) and Johnstone etal. (2012) who ﬁnd
that
OECD countries with higher R&D expenditures are likely to have higher productiv-
ity growth. Moreover, our ﬁndings are in line with the study of Zhu and Ye
(2018)
who ﬁnd that FDI can promote inclusive green growth. Regarding PET and REC,
these two variables are both negatively correlated with IGG but do not pass the
sig-
niﬁcance test.
5 Conclusion andpolicy implication
One of the major challenges of the twenty-ﬁrst century is to design a generic
soci-
etal and economic paradigm capable of articulating economic viability, social
inclu-
sion, and environmental sustainability. Inclusive green growth is, therefore, a
new
paradigm for public authorities who have a central role to play in terms of
collective
regulation for sustainable and shared prosperity. Over the past two decades,
OECD
governments have implemented a wide variety of environmental and employment
regulations to enhance environmental quality, social welfare, and productivity.
Therefore, the current strengthening of environmental regulation and employment
protection legislation is likely to aﬀect not only environmental outcomes and
social
welfare but also economic performance.
In this paper, we have extended in two diﬀerent directions the existing
empirical
literature related to the discussion of the Porter Hypothesis. First, we use
inclusive
green growth, a new concept that encompasses the three dimensions of sustainable
development, as a proxy for country competitiveness, allowing the assessment of
how to make growth greener and more inclusive. Therefore, we apply an extended
GMLPI based on the SBM-DDF model incorporating GHG emissions as an unde-
sirable environmental output and HDI and GDP as desirable outputs reﬂecting the


Environmental Economics and Policy Studies
1 3
social and economic dimensions of inclusive green growth, respectively. Second,
while previous studies only examine the impacts of environmental regulations on
green productivity growth, we additionally examine the eﬀects of employment pro-
tection legislation and its interaction with environmental regulations on
inclusive
green growth. This is an important feature of this study.
Dynamic panel regression analysis shows that current and lagged OECD stringent
environmental and employment policies (i.e., EPS and EPL) and their interaction
signiﬁcantly promote inclusive green growth. Thus, our results support the
strong
version of Porter Hypothesis that stringent environmental regulations improve
coun-
tries’ competitiveness. However, we ﬁnd no evidence of the ineﬃcient retention
hypotheses as our results indicate that the stringency of employment protection
does
not impede countries’ competitiveness.
Based on the above analysis, we draw some policy implications. First, the inclu-
sive green growth index remains very modest, on average, for OECD countries. It
reﬂects low average technological change and eﬃciency gains. OECD countries,
therefore, need to devote more eﬀort to protecting the ecological environment by
avoiding the “pollution ﬁrst, treatment later” path, raising labor incomes, and
reforming the income distribution system. OECD countries are therefore invited
to better manage the structure of factor allocation and to increase the ﬁnancial
resources allocated to investment in technological innovation. Second, because
inclusive green growth can be fostered by well-designed environmental and
employ-
ment regulations, mitigating the signiﬁcant national heterogeneities in these
regula-
tions is essential if OECD countries are to enhance inclusive green growth
through
collective and convergent policies.
Although we have reached important conclusions, there are inevitably several
limitations. First, we did not study the nonlinear impacts of environmental and
employment policies on inclusive green growth. Second, we have not considered
the
heterogeneous eﬀects of diﬀerent types of environmental regulation, such as
market-
based and command-and-control policies, on inclusive green growth. Third, it
would
be appropriate to use subgroups or homogeneous groups of OECD countries based
on their economic trajectories and environmental strategies to better reﬂect
country
heterogeneity. Future research can move in these directions.
Appendix
See Figs.1, 2 and Tables5, 6, 7, 8.


1 3
Environmental Economics and Policy Studies
Table 5 Summary statistics
This table shows summary statistics for all regressors and dependent variables
for the period 1990–2012.
It reports the mean, standard deviation (SD), ﬁrst quantile (Q1: 0.25), median,
third quantile (Q3: 0.75),
minimum (Min) and maximum (Max). IGG, EPS, EPL, GDRD, PERT, FDI, and REC, denote
inclusive
green growth, environmental policy stringency, employment protection
legislation, government budget
allocation for R&D (GBRD), patent for environment-related technologies (PERT),
foreign direct invest-
ment (FDI), and renewable energy consumption (REC), respectively.
Mean SD Q1 Median Q3 Min Max N
IGG 1.010 0.041 0.994 1.009 1.028 0.628 1.325 572
EPS 1.740 0.897 0.994 1.583 2.404 0.208 4.133 598
EPL 2.206 0.825 1.702 2.333 2.678 0.256 4.833 592
GBRD 3.572 0.612 3.168 3.452 4.052 2.299 5.220 529
PERT 5.391 1.823 4.300 5.330 6.314 −1.108 9.577 596
FDI 3.926 7.067 0.846 1.954 4.286 −15.989 87.442 578
REC 13.214 13.435 4.207 8.021 20.555 0.441 61.378 598
Fig. 1 Correlation with bivariate relationships. This ﬁgure shows the
correlation with bivariate rela-
tionships between the variables used in the empirical model for the period
1990–2012. IGG, EPS, EPL,
GDRD, PERT, FDI, and REC, denote inclusive green growth, environmental policy
stringency, employ-
ment protection legislation, government budget allocation for R&D (GBRD), patent
for environment-
related technologies (PERT), foreign direct investment (FDI), and renewable
energy consumption (REC),
respectively
Fig. 2 Network correlations between the variables in the model. This ﬁgure shows
the network corre-
lations between the variables used in the empirical model for the period
1990–2012. IGG, EPS, EPL,
GDRD, PERT, FDI, and REC, denote inclusive green growth, environmental policy
stringency, employ-
ment protection legislation, government budget allocation for R&D (GBRD), patent
for environment-
related technologies (PERT), foreign direct investment (FDI), and renewable
energy consumption (REC),
respectively


Environmental Economics and Policy Studies
1 3
Table 6 Averaged inclusive green growth: 1990–2012
1990–1991 1991–1992 1992–1993 1993–1994 1994–1995 1995–1996 1996–1997 1997–1998
1998–1999 1999–2000 2000–2001
Australia 1.003 1.050 1.037 1.030 1.026 1.028 1.037 1.046 1.029 1.000 1.032
Austria 0.970 1.031 0.990 1.013 0.981 0.967 1.012 1.004 1.012 1.009 0.969
Belgium 0.984 1.003 0.994 1.000 0.998 0.980 1.024 0.989 1.016 1.003 0.983
Canada 0.968 0.997 1.017 1.030 1.013 0.998 1.029 1.035 1.034 1.034 1.017
Czech
Republic
0.967 1.001 1.005 1.035 1.030 1.010 0.999 1.015 1.034 1.007 1.007
Denmark 0.951 1.032 0.982 1.013 1.011 0.966 1.042 1.019 1.032 1.033 0.998
Finland 0.996 1.015 1.013 1.004 1.038 0.992 1.016 1.024 1.015 1.030 0.988
France 0.977 1.020 1.002 1.035 1.013 0.991 1.032 1.021 1.037 1.034 1.003
Germany 1.073 1.034 0.980 1.032 1.007 0.984 1.028 1.022 1.026 1.032 1.009
Greece 1.005 0.993 0.981 0.994 0.988 0.976 1.003 0.980 1.014 1.002 1.001
Hungary 0.961 1.067 1.007 1.022 1.011 0.974 1.027 1.010 0.994 1.027 0.988
Ireland 1.000 1.000 1.000 1.000 1.000 0.989 1.012 1.000 1.000 1.000 0.974
Italy 0.999 1.011 0.997 1.045 1.008 1.013 1.017 0.999 0.999 1.039 1.003
Japan 1.000 0.965 0.969 0.965 0.989 1.008 1.001 0.966 0.969 1.015 0.999
Korea 1.033 0.994 1.001 1.033 1.035 1.024 1.027 0.995 1.076 1.045 1.028
Netherlands 1.005 1.019 1.002 1.049 1.023 1.010 1.069 1.053 1.068 1.053 1.012
Norway 1.045 1.039 0.990 1.011 1.000 0.983 1.017 1.000 0.971 1.030 0.980
Poland 0.802 1.025 1.034 1.088 1.117 1.054 1.060 1.011 1.001 1.042 0.961
Portugal 0.983 0.964 0.988 0.973 0.991 0.989 0.981 0.967 0.968 0.983 0.991
Slovak
Republic
1.000 1.000 1.000 1.000 1.000 0.966 0.973 0.976 1.014 1.075 0.879
Spain 1.000 0.996 0.999 1.001 1.008 1.021 1.006 1.010 1.006 1.012 1.015
Sweden 0.988 0.987 1.007 1.017 1.013 0.992 1.045 1.025 1.031 1.022 1.003
Switzerland 0.945 0.995 1.031 1.030 0.972 0.982 1.041 0.978 1.001 1.042 0.965
Turkey 0.851 1.052 1.117 0.629 1.045 1.020 1.049 0.971 0.945 1.027 0.972


1 3
Environmental Economics and Policy Studies
Table 6 (continued)
1990–1991 1991–1992 1992–1993 1993–1994 1994–1995 1995–1996 1996–1997 1997–1998
1998–1999 1999–2000 2000–2001
United
Kingdom
0.979 1.022 1.033 1.049 1.027 1.002 1.041 1.034 1.032 1.038 1.024
United
States
0.996 1.030 1.013 1.028 1.017 1.021 1.039 1.059 1.063 1.148 0.869
2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009
2009–2010 2010–2011 2011–2012 Geometric
average
Australia 1.020 1.038 1.013 1.016 1.023 1.013 0.998 1.006 1.006 1.019 1.011
1.022
Austria 0.994 0.965 1.006 0.995 1.019 1.028 0.995 0.998 0.977 1.029 1.004 0.998
Belgium 1.024 0.987 1.011 1.016 1.018 1.053 0.986 0.976 1.011 1.057 0.994 1.005
Canada 1.010 0.995 1.018 1.031 1.023 0.987 1.003 0.984 1.023 1.010 1.008 1.012
Czech
Republic
1.017 1.003 1.021 1.035 1.032 1.031 1.010 0.988 1.010 1.026 0.993 1.012
Denmark 1.015 0.992 1.029 1.020 0.972 0.998 1.030 0.999 1.003 1.213 1.097 1.019
Finland 0.988 0.982 1.081 1.048 0.964 1.011 1.020 0.987 0.964 1.046 1.001 1.010
France 1.016 0.996 1.032 1.018 1.042 1.037 0.995 0.982 1.007 1.071 1.000 1.016
Germany 1.004 0.983 1.020 1.017 1.037 1.074 0.991 0.949 1.019 1.067 1.000 1.017
Greece 1.007 1.000 1.026 0.984 1.019 0.994 1.007 0.987 1.006 0.965 1.031 0.998
Hungary 1.020 0.989 1.027 1.004 1.024 1.022 1.006 1.000 0.976 1.011 1.030 1.009
Ireland 1.027 1.000 1.000 1.000 0.941 0.992 0.980 1.022 0.995 1.076 1.000 1.000
Italy 0.989 0.967 1.012 1.000 1.026 1.020 0.990 0.978 1.015 1.034 0.976 1.006
Japan 0.984 1.021 1.023 1.005 1.025 1.057 1.046 0.918 1.089 0.966 1.036 1.000
Korea 1.054 1.013 1.031 1.023 1.041 1.037 1.014 0.994 1.027 1.016 1.007 1.025
Nether-
lands
0.993 0.995 1.025 1.034 1.046 1.048 1.009 0.956 0.987 1.064 0.987 1.023
Norway 1.011 1.000 1.010 1.000 1.000 1.000 1.000 1.000 0.974 1.027 1.000 1.004


Environmental Economics and Policy Studies
1 3
Table 6 (continued)
2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009
2009–2010 2010–2011 2011–2012 Geometric
average
Poland 1.016 1.029 1.065 1.018 1.064 1.085 0.982 0.994 1.008 1.058 1.009 1.022
Portugal 0.971 1.004 0.993 0.995 1.019 1.006 1.017 0.999 1.033 1.023 1.038 0.994
Slovak
Republic
1.047 0.996 0.979 1.006 1.042 1.064 1.000 0.964 0.996 1.017 1.025 1.000
Spain 1.001 0.993 0.993 1.005 1.045 1.013 1.040 1.018 1.008 1.007 0.995 1.009
Sweden 1.006 1.016 1.023 1.025 1.024 1.019 1.002 0.992 0.985 1.064 1.007 1.013
Switzer-
land
1.028 0.967 1.016 1.021 1.084 1.106 1.009 0.963 1.032 1.074 1.000 1.012
Turkey 1.045 1.029 1.091 1.326 0.848 0.978 1.038 0.890 1.153 1.132 0.886 0.994
United
Kingdom
1.039 1.031 1.025 1.037 1.032 1.036 0.993 0.995 0.994 1.201 0.912 1.025
United
States
0.999 1.026 1.054 1.041 1.024 1.000 0.943 0.990 1.010 1.026 1.034 1.018


1 3
Environmental Economics and Policy Studies
Table 7 Averaged eﬃciency change (EC): 1990–2012
1990–
1991
1991–
1992
1992–
1993
1993–
1994
1994–
1995
1995–
1996
1996–
1997
1997–
1998
1998–
1999
1999–
2000
2000–
2001
2001–
2002
2002–
2003
2003–
2004
2004–
2005
2005–
2006
2006–
2007
2007–
2008
2008–
2009
2009–
2010
2010–
2011
2011–
2012
Geo-
metric
aver-
age
Australia 1.000 1.000 1.000 0.700 1.429 1.000 1.000 0.753 1.018 0.966 1.023
1.009 1.037 0.981 0.990 1.002 0.985 1.003 1.033 1.006 0.985 0.999 0.989
Austria 0.940 0.999 0.995 1.007 0.976 0.969 0.998 1.024 1.021 0.993 0.976 0.981
0.967 1.004 0.999 1.015 1.038 1.008 0.993 1.006 1.001 1.001 0.996
Belgium 0.989 0.973 0.968 0.978 0.977 0.943 0.993 0.998 1.041 0.987 0.971 1.019
0.983 0.997 1.003 1.011 1.040 0.998 0.997 1.031 1.032 0.964 0.995
Canada 0.961 0.973 1.014 1.031 1.006 0.978 1.015 1.042 1.010 1.012 1.009 0.997
0.978 0.988 1.010 1.005 0.979 1.005 0.990 1.020 0.987 0.982 0.999
Czech
Republic
0.942 0.980 1.005 1.030 1.025 1.009 0.985 1.037 1.043 1.005 1.019 1.006 1.014
1.028 1.049 1.031 1.028 1.022 0.986 1.021 0.984 0.990 1.011
Denmark 0.931 1.006 0.985 1.012 1.006 0.967 1.028 1.040 1.033 1.035 0.998 0.992
1.003 1.025 1.048 0.984 1.012 1.032 0.977 1.013 1.238 1.000 1.015
Finland 0.981 0.999 1.011 1.002 1.042 1.003 1.014 1.019 1.024 1.036 0.996 0.978
0.995 1.051 1.060 0.971 1.028 1.034 0.980 0.979 1.017 0.996 1.010
France 0.980 1.035 1.004 1.086 1.000 0.915 1.010 1.082 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.005
Germany 1.233 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.010
Greece 0.980 0.965 0.983 0.988 0.980 0.974 0.986 0.999 1.022 0.989 1.008 0.992
1.002 1.023 0.991 1.017 1.001 1.022 0.984 1.029 0.926 1.018 0.994
Hungary 0.940 1.046 1.010 1.022 1.005 0.980 1.020 1.037 1.005 1.042 1.009 1.010
1.004 1.039 1.003 1.010 1.020 1.022 0.984 1.001 0.986 1.006 1.009
Ireland 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Italy 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 0.913 1.095 1.000 1.000 0.885 0.986 0.994
Japan 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Korea 1.027 0.994 1.009 1.028 1.027 1.002 1.005 1.006 1.086 1.009 1.015 1.041
1.022 1.010 1.010 1.013 0.988 1.023 1.039 1.012 0.966 1.012 1.015
Nether-
lands
1.025 1.002 1.001 1.028 0.998 0.976 1.030 1.045 1.064 1.009 1.005 0.983 0.999
0.990 1.011 1.017 1.004 1.017 0.990 0.984 1.016 0.978 1.008
Norway 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Poland 0.878 1.004 1.002 2.196 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.031
Portugal 1.000 1.000 0.630 0.918 0.986 0.994 0.964 0.988 0.975 0.972 1.001 0.955
1.015 0.995 1.010 1.017 1.007 1.031 0.982 1.053 0.963 1.026 0.972
Slovak
Republic
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Spain 1.012 0.989 1.011 0.962 0.994 1.006 0.981 1.009 1.005 0.975 1.013 1.004
1.005 0.965 0.984 1.015 0.953 1.051 1.226 1.000 0.813 0.997 0.996
Sweden 0.945 0.925 1.011 1.010 1.008 0.996 1.037 1.032 1.043 1.006 1.013 0.994
1.014 1.018 1.020 1.023 1.033 1.020 0.991 1.013 1.026 1.006 1.008


Environmental Economics and Policy Studies
1 3
Table 7 (continued)
1990–
1991
1991–
1992
1992–
1993
1993–
1994
1994–
1995
1995–
1996
1996–
1997
1997–
1998
1998–
1999
1999–
2000
2000–
2001
2001–
2002
2002–
2003
2003–
2004
2004–
2005
2005–
2006
2006–
2007
2007–
2008
2008–
2009
2009–
2010
2010–
2011
2011–
2012
Geo-
metric
aver-
age
Switzer-
land
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Turkey 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
United
King-
dom
0.988 1.043 1.053 1.065 1.032 0.985 1.032 1.049 1.034 1.006 1.018 1.033 1.046
1.003 1.020 1.045 0.932 1.001 1.143 0.892 1.121 1.000 1.023
United
States
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000


1 3
Environmental Economics and Policy Studies
Table 8 Averaged technological change (TC): 1990–2012
1990–
1991
1991–
1992
1992–
1993
1993–
1994
1994–
1995
1995–
1996
1996–
1997
1997–
1998
1998–
1999
1999–
2000
2000–
2001
2001–
2002
2002–
2003
2003–
2004
2004–
2005
2005–
2006
2006–
2007
2007–
2008
2008–
2009
2009–
2010
2010–
2011
2011–
2012
Geo-
metric
aver-
age
Australia 1.003 1.050 1.037 1.471 0.718 1.028 1.037 1.390 1.011 1.035 1.009
1.011 1.002 1.033 1.026 1.021 1.029 0.996 0.974 1.000 1.035 1.012 1.034
Austria 1.032 1.032 0.996 1.006 1.005 0.999 1.015 0.980 0.991 1.017 0.992 1.013
0.998 1.002 0.996 1.003 0.990 0.987 1.005 0.971 1.028 1.003 1.003
Belgium 0.995 1.031 1.027 1.023 1.021 1.039 1.031 0.991 0.976 1.016 1.013 1.005
1.004 1.013 1.013 1.008 1.013 0.987 0.979 0.980 1.024 1.030 1.010
Canada 1.007 1.025 1.003 1.000 1.007 1.020 1.014 0.993 1.024 1.022 1.007 1.013
1.017 1.031 1.021 1.018 1.008 0.999 0.994 1.004 1.023 1.027 1.013
Czech
Repub-
lic
1.027 1.022 1.000 1.005 1.005 1.001 1.014 0.979 0.991 1.002 0.989 1.011 0.989
0.993 0.987 1.001 1.003 0.988 1.002 0.989 1.043 1.003 1.002
Denmark 1.021 1.026 0.997 1.002 1.005 0.999 1.013 0.980 0.999 0.998 1.000 1.023
0.988 1.003 0.974 0.988 0.986 0.999 1.023 0.990 0.980 1.097 1.004
Finland 1.015 1.017 1.002 1.003 0.996 0.989 1.002 1.005 0.991 0.994 0.992 1.010
0.987 1.028 0.989 0.993 0.983 0.987 1.007 0.984 1.028 1.006 1.000
France 0.997 0.985 0.998 0.953 1.013 1.083 1.022 0.944 1.037 1.034 1.003 1.016
0.996 1.032 1.018 1.042 1.037 0.995 0.982 1.007 1.071 1.000 1.012
Germany 0.871 1.034 0.980 1.032 1.007 0.984 1.028 1.022 1.026 1.032 1.009 1.004
0.983 1.020 1.017 1.037 1.074 0.991 0.949 1.019 1.067 1.000 1.008
Greece 1.026 1.029 0.999 1.007 1.007 1.002 1.018 0.980 0.992 1.013 0.993 1.015
0.999 1.004 0.993 1.002 0.993 0.985 1.004 0.978 1.042 1.013 1.004
Hungary 1.023 1.021 0.997 1.000 1.007 0.995 1.007 0.974 0.990 0.985 0.980 1.010
0.985 0.989 1.002 1.014 1.002 0.984 1.016 0.975 1.026 1.024 1.000
Ireland 1.000 1.000 1.000 1.000 1.000 0.989 1.012 1.000 1.000 1.000 0.974 1.027
1.000 1.000 1.000 0.941 0.992 0.980 1.022 0.995 1.076 1.000 1.000
Italy 0.999 1.011 0.997 1.045 1.008 1.013 1.017 0.999 0.999 1.039 1.003 0.989
0.967 1.012 1.000 1.026 1.117 0.904 0.978 1.015 1.168 0.989 1.012
Japan 1.000 0.965 0.969 0.965 0.989 1.008 1.001 0.966 0.969 1.015 0.999 0.984
1.021 1.023 1.005 1.025 1.057 1.046 0.918 1.089 0.966 1.036 1.000
Korea 1.006 1.000 0.992 1.005 1.008 1.022 1.022 0.989 0.990 1.035 1.013 1.013
0.991 1.020 1.014 1.028 1.050 0.991 0.957 1.014 1.052 0.996 1.009
Nether-
lands
0.981 1.017 1.001 1.021 1.026 1.035 1.039 1.008 1.004 1.044 1.007 1.011 0.997
1.035 1.023 1.028 1.044 0.991 0.966 1.003 1.047 1.009 1.015
Norway 1.045 1.039 0.990 1.011 1.000 0.983 1.017 1.000 0.971 1.030 0.980 1.011
1.000 1.010 1.000 1.000 1.000 1.000 1.000 0.974 1.027 1.000 1.004
Poland 0.914 1.022 1.032 0.496 1.117 1.054 1.060 1.011 1.001 1.042 0.961 1.016
1.029 1.065 1.018 1.064 1.085 0.982 0.994 1.008 1.058 1.009 0.992
Portugal 0.983 0.964 1.567 1.060 1.005 0.996 1.018 0.978 0.993 1.011 0.989 1.018
0.989 0.998 0.985 1.002 0.998 0.987 1.018 0.982 1.062 1.012 1.022
Slovak
Repub-
lic
1.000 1.000 1.000 1.000 1.000 0.966 0.973 0.976 1.014 1.075 0.879 1.047 0.996
0.979 1.006 1.042 1.064 1.000 0.964 0.996 1.017 1.025 1.000


Environmental Economics and Policy Studies
1 3
Table 8 (continued)
1990–
1991
1991–
1992
1992–
1993
1993–
1994
1994–
1995
1995–
1996
1996–
1997
1997–
1998
1998–
1999
1999–
2000
2000–
2001
2001–
2002
2002–
2003
2003–
2004
2004–
2005
2005–
2006
2006–
2007
2007–
2008
2008–
2009
2009–
2010
2010–
2011
2011–
2012
Geo-
metric
aver-
age
Spain 0.988 1.007 0.988 1.041 1.014 1.015 1.026 1.001 1.001 1.039 1.002 0.997
0.989 1.029 1.021 1.029 1.064 0.989 0.831 1.008 1.239 0.998 1.012
Sweden 1.045 1.067 0.996 1.007 1.005 0.996 1.008 0.994 0.989 1.015 0.991 1.012
1.002 1.005 1.005 1.001 0.986 0.983 1.001 0.973 1.037 1.001 1.005
Switzer-
land
0.945 0.995 1.031 1.030 0.972 0.982 1.041 0.978 1.001 1.042 0.965 1.028 0.967
1.016 1.021 1.084 1.106 1.009 0.963 1.032 1.074 1.000 1.012
Turkey 0.851 1.052 1.117 0.629 1.045 1.020 1.049 0.971 0.945 1.027 0.972 1.045
1.029 1.091 1.326 0.848 0.978 1.038 0.890 1.153 1.132 0.886 0.994
United
King-
dom
0.991 0.980 0.981 0.985 0.995 1.017 1.009 0.986 0.998 1.032 1.005 1.006 0.985
1.022 1.017 0.988 1.112 0.992 0.871 1.114 1.071 0.912 1.002
United
States
0.996 1.030 1.013 1.028 1.017 1.021 1.039 1.059 1.063 1.148 0.869 0.999 1.026
1.054 1.041 1.024 1.000 0.943 0.990 1.010 1.026 1.034 1.018


1 3
Environmental Economics and Policy Studies
Data availability The data that support the ﬁndings of this study are openly
available in Penn World
Tables9.0 databases at https:// www. rug. nl/ ggdc/ produ ctivi ty/ pwt/ pwt-
relea ses/ pwt9.0? lang= en (DOI:
https:// doi. org/ 10. 15141/ S5J01T) OECD.Stat at https:// stats. oecd. org/.
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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional
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applicable law.
Authors and Aliations
BéchirBenLahouel1· LotTaleb2· ShunsukeManagi3· NadiaAbaoub4
Lotﬁ Taleb
loootﬁ63@yahoo.fr
Shunsuke Managi
managi@doc.kyushu-u.ac.jp
Nadia Abaoub
nadiaabaoub@gmail.com
1 IPAG Business School, Paris, France
2 École Supérieure Des Sciences Économiques Et Commerciales Tunis, Université de
Tunis,
Tunis, Tunisia
3 Kyushu University, Fukuoka, Japan
4 École Supérieure de Commerce de Tunis, Université de Tunis, Tunis, Tunisia



CITATIONS (11)


REFERENCES (72)




... Streimikis et al. (2021) employed the Global DEA model to assess the energy
efficiency of agriculture in the European Union. Ben Lahouel et al. (2023), in
their study of inclusive green growth in OECD member countries, utilized the
Global Malmquist index. Zhao et al. (2022) incorporated the Global Metafrontier
SBM Super-efficiency model in their research on the impact of environmental
regulations on green economic growth in China. ...

An integrative study on the green cultural industry and its determinants in
Jiangsu province, China under the cultural revitalization initiative: a global
perspective
Article
Full-text available
 * Nov 2024

 * Yaoyao Ding
 * Rong Zhang
 * Yuntao Zou

Introductions The cultural industry is pivotal in promoting sustainable economic
development. This study aims to evaluate the economic efficiency of the cultural
industry in Jiangsu Province by establishing a Data Envelopment Analysis (DEA)
model and analyzing influencing factors using the Tobit model, all within the
broader context of China’s cultural industry. Methods A DEA model was developed
to assess the economic efficiency of the cultural industry across Chinese
provinces, allowing for a comparative analysis of performance. The Tobit
regression model was utilized to investigate factors influencing these
efficiency outcomes, with a particular emphasis on inter-provincial comparisons
to understand the position and challenges faced by Jiangsu’s cultural industry.
Results Despite Jiangsu’s cultural industry ranking among the largest in scale
nationally, its economic efficiency is only moderate, consistently experiencing
diminishing returns to scale. The study identifies low scale efficiency, small
enterprise size, and suboptimal urbanization processes within the province as
the main issues. Discussion The inefficiencies highlighted by the DEA model
suggest a misalignment between the scale of operations and the economic outputs
in Jiangsu’s cultural industry. Urbanization emerges as a crucial factor, with
current practices not sufficiently supporting the growth potential of the
cultural sector. Conclusion Based on these findings, the study proposes targeted
policy recommendations for Jiangsu, including avoiding blind scale expansion,
adjusting industrial structures, encouraging enterprise consolidation and
optimization, and identifying new growth areas to better support the cultural
industry’s development and contribute to sustainable economic progress.
View
Show abstract
... Streimikis et al. [51] used the Global DEA model to assess energy efficiency
in EU agriculture, while Zhao et al. [52] employed a global frontier SBM
super-efficiency model to study the impact of China's environmental regulations
on green economic growth. Ben Lahouel et al. [53] further applied the Global
Malmquist index to address feasibility issues in studying inclusive green growth
among OECD countries. ...

Urbanization and Cultural Industry Correlation: An Empirical Analysis from China
Article
Full-text available
 * Aug 2024

 * Wen Zhang
 * Rong Zhang
 * Yuntao Zou

The cultural industry has been recognized as an indispensable component of
sustainable economic development. Urbanization often represents a country’s
level of economic development. While China is advancing its new urbanization
strategy, it is also vigorously promoting cultural revitalization plans. This
study employs a global Data Envelopment Analysis (DEA) model and Tobit
regression analysis to examine the correlation between China’s cultural industry
and urbanization. The results indicate that although the overall economic
efficiency of China’s cultural industry is continuously improving, the returns
to scale in many provinces are declining. Changes brought about by new
urbanization, such as increases in per capita GDP, per capita income, and
enterprise scale, have significant positive impacts on the cultural industry.
However, the rising urban population ratio has a significant negative impact on
the cultural industry. This study suggests that the current new urbanization in
China faces issues of oversimplification and excessive advancement. It
recommends adjusting relevant policies to allow sufficient time and space for
the cultural industry to absorb the benefits brought by urbanization. Given
China’s specific national conditions, the conclusions of this study may not
necessarily apply to other regions. However, the global DEA-Tobit combination
method used in this study aligns more closely with reality and achieves a higher
degree of fit, thus possessing a certain level of universality.
View
Show abstract
Political Risk and Sustainable Development: Digitization and Environmental
Policy Stringency
Article
Full-text available
 * Nov 2024

 * Chong Zhang
 * Menglu Zhang
 * Yunqiu Zhan
 * Jiale Yan

View
Paving towards the Sustainable Development Goals: Analyzing the Nexus of
Financial Technology, Business-Centric-Tourism, and Green Growth
Article
 * Nov 2024
 * J ENVIRON MANAGE

 * Huma Iftikhar
 * Atta Ullah
 * Ningyu Qian
 * Magdalena Radulescu

The world is facing crucial challenges such as environmental degradation, social
inequality, and slow economic growth due to the transformative era. These
challenges constitute a significant barrier to unlocking the world’s full
potential. In response, inclusive green growth (IGG) has emerged as a focal
point in global discourse due to the pivotal shift of legislators and
researchers from conventional economic growth to inclusive green growth. This
study aims to investigate the impact of business-centric-tourism and Fintech on
inclusive green growth across 148 Belt and Road Initiative (BRI) economies
during the period spanning 2004-2021. Principal component analysis (PCA) is
utilized to create indexes of inclusive green growth and Fintech. The Fintech
index introduces twenty-one enabling and integrated indicators related to
finance and technology while inclusive green growth is comprised of social
inclusiveness, economic growth, and environmental sustainability. The two-step
system-GMM corroborated by 2SLS (two-stage least square) technique indicates
that business-centric-tourism and Fintech endorse inclusive green growth.
Moreover, inclusive green growth is positively influenced by socioeconomic and
energy factors such as renewable energy, globalization index, and business
freedom index, while negatively impacted by urbanization and socio-economic
conditions. This study adds value to business literature on inclusive green
growth, particularly in emerging economies. Aligning research outcomes with
diverse theoretical frameworks and Sustainable Development Goals (SDGs) targets
to offer significant policy implications for balanced and inclusive growth.
View
Show abstract
Coupling and coordination relationship of tourism inclusive green growth system:
Evidence from Shandong Province
Article
 * Sep 2024

 * Tianjun Xu
 * Gangmin Weng
 * Wei Guo

View
Green Investments and Inclusive Growth: The Case of the BRICS Economies
Article
 * Sep 2024

 * Jamiu Olamilekan Badmus
 * Oluwadamilola Samuel Alawode
 * Sodiq Olaide Bisiriyu

View
Effect of digital finance on inclusive green growth: Evidence from China's urban
agglomerations
Article
 * Jun 2024

 * Jiasen Sun
 * Tong Liu
 * Ruizeng Zhao

This study uses a data envelopment analysis model to assess the inclusive green
growth (IGG) level for five major urban agglomerations in China from 2013 to
2020. In addition, it analyzes the potential digital finance (DIF) mechanism
affecting IGG. Several conclusions are obtained. First, the IGG levels of the
five major urban agglomerations in China increase yearly, narrowing their gaps.
Second, DIF can significantly promote IGG. Third, heterogeneity exists in the
impact of DIF on IGG owing to the differences in city tiers and sizes.
Meanwhile, the coverage and digitization level of DIF significantly and
positively promote IGG. Fourth, financial supervision intensity and human
capital level play a single‐threshold effect in the relationship between DIF and
IGG. The contribution of DIF to IGG is further enhanced when financial
regulation intensity and human capital level exceed the thresholds 0.0013 and
1.5084, respectively. Lastly, green technology innovation, regional
entrepreneurship, and industrial structure upgrading have intermediary roles in
the baseline path of DIF impacting IGG.
View
Show abstract
Does Globalization Promote Green Growth? Empirical Evidence from Organisation
for Economic Co-operation and Development Countries
Chapter
 * Jun 2024

 * Muhammed Sehid Gorus

When the Sustainable Development Goals (SDGs) were adopted by the United Nations
in 2015, green growth was at the center of this initiative. Although many of the
SDGs’ guiding principles and objectives are intimately tied to green growth,
Goals 8 and 13 are directly related to this concept. Even though there have been
several empirical investigations that examined the factors affecting the green
growth performance of the countries, the impact of globalization has been
neglected, especially at disaggregated levels. The current investigation aims to
fill a gap in the empirical literature by exploring the effects of globalization
on green growth by considering its different dimensions: aggregate
globalization, economic globalization, trade globalization, financial
globalization, social globalization, and political globalization. Besides, the
environmental policy stringency index is incorporated into models as a control
variable. The sample consists of 33 OECD countries covering the period from 2010
to 2020. The two-step system GMM and the Dumitrescu-Hurlin (DH) panel
non-causality test are employed to reveal the aforementioned relationship. The
empirical findings show that the green growth performance of the countries is
positively and significantly affected by globalization, at both aggregate and
disaggregate levels. The findings provide significant policy suggestions for
sustainable development purposes of the countries.
View
Show abstract
Demystifying circular economy and inclusive green growth for promoting energy
transition and carbon neutrality in Europe
Article
 * May 2024
 * Struct Change Econ Dynam

 * Olatunji Shobande
 * Aviral Tiwari
 * Lawrence Ogbeifun
 * Nader Trabelsi

View
Towards the goal of going green: Do green growth and innovation matter for
environmental sustainability in Pakistan
Article
 * Oct 2023
 * ENERGY

 * Boqiang Lin
 * Sami Ullah

View
Show more

Adoption of environmental standards and a lack of awareness: evidence from the
food and beverage industry in Vietnam
Article
Full-text available
 * Sep 2021

 * Massimo Filippini
 * Suchita Srinivasan

Voluntary approaches to environmental policy can contribute to stemming
environmental degradation in developing countries with weak institutions. We
evaluate the role of a lack of awareness of a law in explaining the voluntary
adoption of environmental certification by small and medium enterprises (SMEs)
in the food and beverage industry in Vietnam. We find that firms, where owners
or managers were unaware of the law were 38 percentage points less likely to
receive environmental certification. Moreover, this effect is larger for firms
that exported, had internet access or paid bribes, and it is weaker for
household enterprises. Our results suggest that increasing legal awareness can
weaken informational constraints for SMEs, where weak institutions and a lack of
information often hamper the uptake of environmental policy initiatives.
View
Show abstract
A non-parametric decomposition of the environmental performance-income
relationship: Evidence from a non-linear model
Article
Full-text available
 * Mar 2021
 * ANN OPER RES

 * Béchir Ben Lahouel
 * Younes Ben Zaied
 * Guoliang Yang
 * Yaoyao Song

This paper attempts to examine whether the Environmental Kuznets Curve
hypothesis is supported in Middle Eastern and North African countries. We use, a
novel range-adjusted measure-based global Malmquist-Luenberger productivity
index, accounting for slacks of inputs as well as desirable and undesirable
outputs, to evaluate and decompose “green” productivity growth rates into
technical change, pure efficiency change, and scale change. By employing a panel
smooth transition regression model, we investigate the income elasticity of
environmental performance with respect to the decomposition factors. Our
empirical results show that there are double thresholds when technical change
and scale change are taken as transition variables, then leading to an inverted
N-shaped curve between income and environmental performance. A single threshold
has been found when pure efficiency change is considered as a transition
variable, yielding to an inverted U-shaped curve. Thus, our research does not
find support for the Environmental Kuznets Curve hypothesis.
View
Show abstract
Social And Governance Dimensions Of Climate Change: Implications For Policy
Book
 * May 2009

 * Roberto Foa

View
Reprint of: Initial conditions and moment restrictions in dynamic panel data
models
Article
 * Mar 2023
 * J ECONOMETRICS

 * Richard Blundell
 * Stephen Bond

View
Does primary stakeholder management improve competitiveness? A dynamic network
non-parametric frontier approach
Article
 * Aug 2022
 * ECON MODEL

 * Béchir Ben Lahouel
 * Taleb Lotfi
 * Younes Ben Zaied
 * Shunsuke Managi

This study examines whether positive primary stakeholder management is reflected
in firm competitiveness. To conceptualize firm competitiveness, we follow a
productivity perspective, in which technological and economic relationships
between input consumption and output production are considered. Utilizing a
three-stage dynamic network data envelopment analysis approach, we compute the
Malmquist productivity index, which allows to examine the dynamics of the
technology frontier and the levels of catch-up among a sample of international
airlines observed between 2005 and 2019. We find that productivity,
technological, and efficiency changes are enhanced by two dimensions of
stakeholder management (i.e., employees and product/customer responsibility)
that are socially required and capture economic and legal responsibilities.
Dimensions of stakeholder management that are socially desired or expected
(i.e., community, environment, and human rights) and are not directly related to
operations or factors that create economic value, do not appear to play a
significant role in improving airline productivity change.
View
Show abstract
Job Protection Legislation and Productivity Growth in OECD Countries
Article
 * Jan 2008

 * Andrea Bassanini
 * Luca Nunziata
 * Danielle Venn

View
Employment Protection Legislation and Mismatch: Evidence from a Reform
Article
 * Jan 2017

 * Fabio Berton
 * Francesco Devicienti
 * Sara Grubanov-Boskovic

View
Effects of environmental regulation on firm entry and exit and China’s
industrial productivity: a new perspective on the Porter Hypothesis
Article
 * May 2021

 * Mian Yang
 * Yining Yuan
 * Fuxia Yang
 * Dalia Patiño-Echeverri

This paper dissects the effects of environmental regulation on the productivity
of pollution-intensive industries and by doing so offers a new perspective on
the Porter Hypothesis. A theoretical model that incorporates firm’s productivity
heterogeneity shows that tighter environmental regulations impose two opposite
effects on aggregate industry productivity: a negative productivity erosion
effect on all the firms, and a positive productivity selection effect through
impacts on firms’ entry and exit. Thus, the final effect of environmental
regulation on industry productivity depends on the magnitude of the two
individual effects. An empirical study supports the theoretical model. Data from
184,186 firms from 15 Chinese pollution-intensive industries during 1998–2007
shows that environmental regulation has imposed a significant negative effect on
firm-level productivity but at the same time has affected the probability of
entry and exit of low productivity firms. Stricter environmental regulation
increases the probability of exit for the lower productivity firms and reduces
the probability of entry for potential pollution-intense entrants, leading to
significant resource reallocation within the industries. These two effects
result in an inverted U-shaped relationship between environmental regulation
stringency and aggregate industry productivity; aggregate industry productivity
increases when the stringency of environmental regulation is neither too high
nor too low.
View
Show abstract
The impact of the free trade zone on green total factor productivity ——evidence
from the shanghai pilot free trade zone
Article
 * Jan 2021
 * ENERG POLICY

 * Yufan Jiang
 * Hongyan Wang
 * Zuankuo Liu

The green development of free trade zones conforms to the theoretical principles
of sustainable development and is key to promoting regional transformation and
upgrading. By using synthetic control methods (SCM) based on microscopic data,
this paper investigates the net effect of the establishment of the China
(Shanghai) pilot free trade zone (SPFTZ) on green total factor productivity
(GTFP) in Shanghai. The results show that the SPFTZ has promoted the GTFP in
Shanghai. As far as time trend is concerned, the effects of this promotion
become more apparent after a short-term period of slow growth. In terms of
impact paths, the main driving force of policy is technological progress.
Therefore, the SPFTZ should improve GTFP and create greater incentives for
technological innovation. Institutional innovation for green development should
play a central guiding role in the construction. We need continue to explore
other green development models as well as means of creating higher levels of
economic openness.
View
Show abstract
How to do Xtabond2: An Introduction to Difference and System GMM in Stata
Article
 * Mar 2009

 * David Roodman

The difference and system generalized method-of-moments estimators, developed by
Holtz-Eakin, Newey, and Rosen (1988, Econometrica 56: 1371–1395); Arellano and
Bond (1991, Review of Economic Studies 58: 277–297); Arellano and Bover (1995,
Journal of Econometrics 68: 29–51); and Blundell and Bond (1998, Journal of
Econometrics 87: 115–143), are increasingly popular. Both are general estimators
designed for situations with “small T, large N″ panels, meaning few time periods
and many individuals; independent variables that are not strictly exogenous,
meaning they are correlated with past and possibly current realizations of the
error; fixed effects; and heteroskedasticity and autocorrelation within
individuals. This pedagogic article first introduces linear generalized method
of moments. Then it describes how limited time span and potential for fixed
effects and endogenous regressors drive the design of the estimators of
interest, offering Stata-based examples along the way. Next it describes how to
apply these estimators with xtabond2. It also explains how to perform the
Arellano–Bond test for autocorrelation in a panel after other Stata commands,
using abar. The article concludes with some tips for proper use.
View
Show abstract
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