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Citation: Bezi´c, H.; Mance, D.; Balaž,
D. Panel Evidence from EU Countries
on CO2Emission Indicators during
the Fourth Industrial Revolution.
Sustainability 2022,14, 12554.
https://doi.org/10.3390/
su141912554
Academic Editor: Ali
Bahadori-Jahromi
Received: 29 August 2022
Accepted: 27 September 2022
Published: 2 October 2022
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4.0/).
sustainability
Article
Panel Evidence from EU Countries on CO2Emission Indicators
during the Fourth Industrial Revolution
Heri Bezi´c, Davor Mance * and Davorin Balaž *
Faculty of Economics and Business, University of Rijeka, 51000 Rijeka, Croatia
*Correspondence: davor.mance@efri.hr (D.M.); davorin.balaz@efri.hr (D.B.); Tel.: +385-91-949-3325 (D.M.);
+385-98-988-6703 (D.B.)
Abstract:
Research question and the most important issue in this paper relates to the determination
of CO
2
emission drivers in EU and the possibility of its reduction in the era of the fourth industrial
revolution. EU strategies and economic policies are directed toward sustainable development, with
special emphasis on reducing CO
2
emissions towards carbon neutrality. The method used in this
research is the Panel Generalized Method of Moments (GMM) two-step dynamic estimator on
27 EU
countries in the period 2012–2019. The research resulted with the following findings: innovation
activity, industrial structure and development, human capital, and institutional framework; these are
all statistically associated with CO
2
emission levels in a negative manner, thus, contribute significantly
to the reduction in CO
2
emissions. Following the empirical results, it may be concluded that
reaching sustainable development goals requires the EU to enhance innovation activity, technological
development, reshape its industrial structure, create high-quality human capital, and increase the
quality of its public institutions.
Keywords:
EU; CO
2
emissions; innovation activity; human capital; technological development; institutions
1. Introduction
Climate changes caused by carbon dioxide (CO
2
) emissions represent one of major
challenges in the 21st century. The World Bank [
1
] (WB), as the biggest data base, developed
its section on sustainable development goals. Sustainable development emphasizes the
importance of thinking about the inheritance of future generations. Except economic di-
mension, sustainable development encompasses social and ecological dimensions. Ecology
or environmental pollution is an issue and concern that the world is facing. CO
2
is not only
a trace gas vital for the growth of plants, it is also a greenhouse gas posing a variety of
threats to society if its concentration in the atmosphere is too high: it endangers human
health and increases the global temperature with the consequent rising of the sea level via
ice melting. It endangers various species up to their extinction. It is a threat to biodiversity.
Increasing CO
2
emissions endanger economic development. Human capital is the key
asset to an economy. It determines the productive power of all other resources. Rising CO
2
emissions endanger human capital, mostly by affecting human health. The rising sea level
and temperature threatens global tourism vital for some low-income economies. It can
cause the disappearance of coastal cities, endanger production facilities, and economies
in general.
The fourth industrial revolution is the technological period and phenomenon that
society lives in. It affects all aspects of society, including daily life, business, the public
sector, education, science, and so on. It is the result of rapid technological advancements
and the implementation of technologies such as artificial intelligence, the Internet of Things,
virtual and augmented reality, and smart systems in a variety of fields. The fourth industrial
revolution and its technologies are expected to bring a variety of benefits to society, such as
increased productivity and improved global health due to new methods for discovering
Sustainability 2022,14, 12554. https://doi.org/10.3390/su141912554 https://www.mdpi.com/journal/sustainability
Sustainability 2022,14, 12554 2 of 25
cures or vaccines for certain diseases and physical disorders. One of the most significant
societal benefits is that it should help to reduce pollution levels through the process of
creative destruction and reshaping of industrial structures to become more environmentally
friendly. Variables included in the econometric model in this research are recognized drivers
of the fourth industrial revolution, as explained more thoroughly in the literature review
section. Human capital stands at the forefront of any technological shift and development.
Green and eco-innovations will drive industrial and overall economic development, helping
to achieve sustainable development goals. CIP, or competitive industrial performance index,
is included as an independent variable in the econometric model. Index measures the
export competitiveness of an industry in a specific national economy and the level of
its transition to the fourth industrial revolution. Restructuring the industry to rely on
green energy and innovations, as well as to reduce pollution, is a top priority not only
in the EU, but also globally. It will aid in the mitigation of climate change, which is a
major challenge for the twenty-first century. Despite its worldwide significance and rising
interest, the fourth industrial revolution and associated technologies are an understudied
topic. This paper seeks to add to the study of the fourth industrial revolution and its
consequences, particularly in the field of pollution reduction, which is a key social concern
in the twenty-first century. A reason regarding why it is necessary to study fourth industrial
revolution and why it is included in this research is because this topic is not examined
enough, despite high interest and a rising number of different studies. Another reason
is that it is a relatively new topic due to its short period, since it started in 2011. This is
confirmed by different authors such as [
2
] and [
3
]. The COVID-19 epidemic accelerated the
digital transformation. There is a scientific discussion regarding if it is the beginning of the
fifth industrial revolution according to various authors such as [
4
–
7
]. It is critical to study
and research the effects of the fourth industrial revolution so that this topic does not go
unstudied during the period of a possible new industrial revolution, especially focusing on
its impact on the most important global challenge—the pollution level.
This study aims to identify the variables that significantly contributed to the EU’s
lower CO
2
emissions throughout the fourth industrial revolution, long after the European
Union Environmental Trading System was implemented (EU-ETS). The fourth industrial
revolution represents a new paradigm for EU development, and new technologies are
expected to help reduce CO2emissions.
This research provides some new contributions in terms of hypothesis testing, co-
efficient estimation, and policy recommendations. The literature review section and the
discussion sections are sources of discussions on CO
2
and overall emission levels and
its determinants. The variables of interest are industrial and technological development,
institutional quality, innovation activity and human capital. As far as the authors know,
there is no study showing statistical associations between the same set of variables on the
same or similar set of countries using the same methods of analysis, thus, making it an
understudied field. This research is focused on the period of the fourth industrial revolution
marked by the EU with the goal to increase European competitiveness. Pollution reduction,
sustainable development and industrial revolutions are driven by human capital. In this
study, human capital and innovation activity factors are represented by the knowledge
and technology output as well as patent per capita indicators. Both variables are incor-
porated in the model explained in detail in chapters 3 and 4. Technological development
and upgraded industrial structure are necessary for lowering pollution levels, industrial
revolution and sustainable development, and this variable is incorporated in the model
together with the competitive industrial performance index that is further explained in the
Results and Discussion section. Since progress does not happen in a societal vacuum, these
variables are supplemented by measures of societal institutional quality: the rule of law and
corruption perception index (inverted). This research contributes to the scientific discussion
twofold. It tests the hypotheses and coefficients regarding independent variables and their
statistical association to the dependent variable: CO
2
emissions. Some of these variables,
such as human capital, industrial and technological development and innovation activity,
Sustainability 2022,14, 12554 3 of 25
are recognized drivers of fourth industrial revolution. By testing these hypotheses, it pro-
vides new insights about the fourth industrial revolution and its effects on the environment.
The use of the two variables related to institutional quality is in line with the importance of
public organizations in promoting environmental norms and rules at the EU level, such
as the EU-ETS, and with the idea that institutional framework is important in enhancing
technological development and the transition toward the fourth industrial revolution. New
cognitions from this research may ultimately help create better policies related to reducing
pollution levels and combating climate changes at the EU and global levels.
The paper is divided into sections. The first section is the Introduction. Section 2
provides a review of the literature. Section 3represents the Materials and Methods. The
econometric model used for the purposes of this research is shown in Section 4. Section 5is
a discussion of the model’s results. In the final and concluding section, a conclusion with
policy recommendations is provided.
2. Literature Review
The literature review will provide a discussion and theoretical background related
to CO
2
emissions, its determinants and harmful effects. The EU wants to become carbon
neutral, and not just to reduce CO
2
emissions. Developed countries agreed to reduce emis-
sion levels according to the Kyoto Protocol. Consequently, since it represents a departure
from the customary technologies, the reduction in CO
2
emissions has implications on the
selected variables.
2.1. CO2Emissions—Effects and Determinants
There are different drivers of environmental pollution. According to the Environ-
mental Kuznets Curve (EKC) hypothesis, environmental pollution should decrease with
increasing human development above a certain threshold. This theory is subject to harsh
disagreement. Rahmal and Alam [
8
] were not able to reject the EKC hypothesis on their set
of data. According to their findings, pollution levels increase throughout the early stages of
economic development while decreasing during later stages because of improved national
economic standards that enable technical advancement. They discovered that higher levels
of energy consumption, trade volume, and enterprise credit amounts have a detrimental
impact on the environment through higher CO
2
emissions. They advocate advancements
in renewable energy to lower pollution. They state that CO
2
emissions are key drivers of
climate change and environmental pollution.
Erdogan, Kirca and Gedikli [
9
] stated that CO
2
emissions represent the most impor-
tant indicator of environmental pollution. Their analysis of six countries confirmed that
rising CO
2
emissions negatively affect human capital by causing various diseases, affecting
working efficiency and productivity, and subsequently, causing a rise in public costs of
healthcare. Instead of being directed towards economic growth or education, these costs
are directed towards consequence remediation of environmental pollution. They suggest
to raise awareness among populations about harmful effects of CO
2
emissions and empha-
size technological progress as a driver of declining the emissions. The reduction in CO
2
emissions represents a challenge for governments, institutions, multinational enterprises
(MNEs), small and medium enterprises (SMEs) and the overall global community. The EU,
a leading global trader and investor and one of the most important players in the global
economy in its development strategies, emphasizes sustainable development as its priority.
A reduction in CO
2
emissions and becoming carbon neutral are among their sustainable
development goals. Harvard School of Public Health [
10
] states that environmental re-
searchers from Harvard University found that CO
2
emissions endanger human health and
increase poverty and the risk of undernourishment, especially in less-developed economies,
by reducing crop quality.
Cioca et al. [
11
] agreed with the fact that sustainable development is a major global
challenge, with special emphasis on CO
2
emission levels, and that it is the most important
issue for each stakeholder in society: governments, regional integrations, business sectors,
Sustainability 2022,14, 12554 4 of 25
and science. They focused on EU and found that transport, the manufacturing sector,
thermal and electricity production are the biggest drivers of environmental deterioration.
They also found that the pollution level is reducing and that the main drivers of the
emission level reduction are innovation activity, transition to green energy and technology.
The same variables, according to Mignamisi and Djeufack Dongmo [
12
], have different
consequences in nations with abundant and restricted resources. Urbanization is a major
determinant of pollution, but other elements including income per capita, industrial and
technological development, energy consumption, institutional quality, agricultural output,
and involvement in global trade also have a big role. In terms of income per capita, the
article provided evidence in favor of the EKC theory. Globalization levels, GDP per capita,
and environmental taxes reduce emission levels, whereas car counts raise emissions, claim
Vlahini´c Lenz and Fajdeti´c [
13
]. Rong et al. [
14
] found that economic growth and GDP
actually increase CO
2
emissions, whereas innovation activity and the move to renewable
energy sources have a positive impact.
According to Roth [
15
], the reduction in emission levels is a top priority for the global
community due to the negative effects on human capital. It has a negative impact on
children and young people’s life quality, cognitive abilities, and educational outcomes,
as well as working productivity due to missed days at work. According to Apergis,
Bhattacharya, and Hadhri [
16
], environmental degradation killed 7 million people in 2012.
They studied 170 economies and discovered that higher emission levels endanger health
and increase healthcare costs, while its drivers are the increase in GDP, energy consumption,
and population.
Terjanika and Pubule [
17
], in their study of the EU economy, found that reducing
pollution is the most important social problem today. Countries want to become carbon
neutral, but new technologies are the most important factors for this. Since the industrial
sector poses significant challenges, it is necessary to advance the industrial structure.
The biggest obstacle to reducing CO
2
emissions and moving to green solutions is the high
complexity of these processes, the lack of knowledge among managers and workers, and the
high costs. Improving institutional quality is one of the key factors in reducing emissions.
The regulatory framework is an important driving force in reducing emissions. They agree
with several authors [
18
–
20
], that barriers include not only cost, but also the speed of
ROI (return on investment), the inability of human capital to accept new technologies,
maintenance and functionality of existing technologies.
Nguyen [
21
], when analyzing 100 economies, could not reject the EKC hypothesis in
the form that higher industrialization levels reduce pollution. Mahmood, Furquan and
Bagais [
22
] emphasize the importance of declining pollution levels for health and biodiver-
sity. The drivers of higher emission levels are FDI, whereby foreign companies very often
export dirty industries to developing countries; trade, whereby dirty production is held at
bay in developing countries; and energy usage, whereby most of the energy is still derived
from fossil sources. Mance, Vilke and Debeli´c [
23
] tested the EKC hypothesis on solid
municipal waste by analyzing a panel of Croatian municipalities. Their results show the
existence of the EKC due to detailed waste treatment policies in the
richest municipalities.
Rios and Gianmoena [
24
] analyzed the spillover effects of CO
2
emissions from 141
neighboring countries with positive results. Mance et al. [
23
] found a positive statistical
association in a dynamic panel regarding the relation of institutional and human capital
quality to environmental indicators [25].
Population levels and the integration into global trade flows do not have any statisti-
cally significant effect. Chen [
26
], when analyzing OECD member states, found that EU
countries perform better than other economic or trade associations in reducing pollution
levels. This is probably due to the comprehensiveness of the EU-ETS. Results were different
for the 18 EU member states from the 32 OECD members. In the EU, the EKC could not
be rejected. In both cases, the increased use of fossil fuels increases the pollution level,
and the transition to green energy reduces it. Green energy implies innovative energy
solutions and confirms that innovation activities reduce pollution levels. Anwar, Younis
Sustainability 2022,14, 12554 5 of 25
and Ullah [
27
] state that an increased urban population, GDP and integration into global
trade flows increase the pollution level.
To reduce pollution levels, policy priorities have to improve the industrial structure,
increase institutional and regulatory quality, enhance innovation activities through tran-
sition to green energy and create quality urban policies. Atici [
28
] analyzed Central and
Eastern European countries (CEECs) and found that increased energy consumption in-
creases emission levels, while integration into global trade flows and an increase in the
income per capita level reduces pollution. Wu, Zhu and Zhu [
29
] agree with the fact that
pollution is the major global concern and the harmful effects are greater than previously
thought. Greenhouse gas emissions affect not only global warming, but also economic
performance, quality of life and health. Additionally, industrial structure, industrialization
and development may reduce CO2emissions.
Khan and Hou [
30
] were also unable to reject the EKC hypothesis among the
19 EU
member states of the International Energy Agency. They investigated the impact of envi-
ronmental sustainability policies on CO
2
emissions and concluded that shifting toward
such policies reduces emissions in the end. Human capital, population, R&D spending,
investment, price level and personal consumption were all examined indicators. Natural
resources, mortality rates, percentage of land covered by forest, surface area, fertility rate,
green energy, and non-renewable sources are examples of environmental indicators. Their
conclusion is that if sustainability is implemented in all of these areas, CO
2
emissions will
be reduced.
According to Magazzino and Cerulli [
31
], the main determinants of declining pollu-
tion levels are increasing the per capita income and reducing energy intensity, whereas
urbanization and economic involvement in global trade flows increase pollution levels.
In a study of 11 CEE economies, Bayar, Diaconu, and Maxim [
32
] found that eco-
nomic growth reduces pollution levels, whereas credits provided to businesses and energy
use raise pollution levels, not only across the panel of 11 nations, but also when tested
individually for each economy in the model.
According to Duro and Padilla [
33
], the primary factors contributing to the EU’s
leadership in the world in terms of emissions reduction are its high per capita income
and reduced energy intensity. They verified that more developed nations outperform
less-developed nations in terms of reducing CO2emissions. The EKC theory was verified
regarding the long term in the EU, according to Dogan and Seker [
34
]. While initial devel-
opment raises CO
2
emissions, development through an increase in the use of innovative
and environmentally friendlier technologies manages to decrease the overall environmental
impact of development.
The transition toward green, innovative energy solutions, and a higher level of in-
volvement in global trade flows also reduce CO
2
emissions, while the usage of traditional
energy sources increases emissions.
Dogan and Inglesi-Lotz [
35
] examined determinants of CO
2
emissions in EU countries
and confirmed the long-run validity of EKC. The usage of traditional energy sources, higher
energy usage and the rising population increase the pollution level. The urban population
rate can have both negative and positive effects for environment, while industrial develop-
ment contributes to a reduction in the short and higher emission levels in the end. Aydin
and Essen [
36
] examined determinants of CO
2
emissions in the EU with a special focus
on different types of taxation related to pollution. They found that the income per capita
and urban population level increase CO
2
emissions, while innovation activity, industrial
structure, development, and price level reduce it. Regarding taxation, different types of
taxation have different impacts on emission levels. All of them reduce emissions, but with
a lower percentage. The total environmental taxes have the highest impact, while energy
taxation is second. Transport and pollution taxes have smallest impact on the reduction in
CO
2
emissions. Morales-Lage, Bengochea-Morancho and Martínez-Zarzoso [
37
] analyzed
the determinants of CO
2
emissions in the EU. In a panel data analysis that included all
28 members
from 1971 to 2012, researchers discovered that a higher population, income
Sustainability 2022,14, 12554 6 of 25
level, technological development, and energy consumption all increase emission levels.
They did, however, conduct separate analyses of old and new members after that. The
results for new members are the same, while technological and industrial development
reduces CO
2
emissions in old members, implying that industrialized countries outperform
the least industrialized in terms of the reduction in CO2emissions.
CO
2
emissions are a complex topic representing huge challenges to reduce its level.
There are a variety of determinants that affect it differently in different periods and sets
of countries.
2.2. Industrial Structure, Development, and CO2Emissions
The pollution level increased with industrial revolutions. People were not aware of the
environmental effects of industry and there was no possibility to create environmental laws.
With further developments in industry and its upgrading, emissions changed. Technologi-
cal progress enabled lower emission levels. Bets [
38
] states that emission levels increased
by 50%, from 278 ppm (parts per million) to 417 ppm. The American Chemical Society [
39
]
confirmed a higher level of pollution through time. They measured it from the beginning of
the new era and confirmed that emissions of CO
2
, CH
4
(methane) and N
2
O (nitrous oxide)
increased, but with the industrial revolutions, the emissions growth rate was higher. In the
20th century, emissions increased exponentially, and the growth was slightly higher than in
the previous periods. CO2emissions are the biggest cause of pollution.
Fowler et al. [
40
] confirmed that CO
2
emissions represent a problem, but the level of
concentration significantly increased during the industrial revolutions. The global level
increased, but there are differences among different groups of countries. Countries with a
better industrial structure and a higher standard and level of industrial development are the
frontrunners in reducing CO
2
emissions, while the pollution level increases in economies
such as India, where the standard and industrialization level is lower. A higher level of
institutional quality and rule of law impact the reduction in CO2emissions.
Li, Ma and Wei [
41
] state that technological progress, upgrading industrial structure,
innovation activity and institutional quality have positive effects on the reduction in emis-
sion levels; meanwhile, GDP growth does not have quantitatively large effects, although
its effects are positive and statistically significant. They recommend focusing on new
technologies and the development of an industrial structure of higher quality.
Hill and Magnani [
42
] found that there are vague impacts regarding income and
industrialization levels on CO
2
emissions, but low-income countries, with a lower level of
technological progress, are faced with an increased pollution level. The same was found
with the quality of human capital, while providing education services in these countries
increases pollution due to a higher energy intensity.
Xu, Li and Huang [
43
] state that studies confirm the importance of transferring credits
to the private sector because they enhance industrial development and advance industrial
structure, thus reducing level of CO
2
emissions; they confirmed this based on examples in
China and the EU. They recommend a transformation toward innovative industries and
green innovative solutions.
Olah et al. [
44
] examined the impact of the fourth industrial revolution and its tech-
nologies on pollution. They state that it provides a great opportunity to reduce pollution
levels and mitigate emissions, and can be a foundation for environmental policies and the
harmonization of economic and industrial policies with environmental protection. They
state there are benefits of the fourth industrial revolution and its technologies in mitigating
emission levels, but the impacts will differ in countries with different levels of economic and
technological development, thus, again confirming the necessity for upgrading industrial
structure. Institutions and innovation activity will be necessary to enable these technologies
to mitigate emission levels. The fourth industrial revolution implies upgrading industrial
structure through innovation activity, better infrastructure and a transformation toward
green energy.
Sustainability 2022,14, 12554 7 of 25
Bonilla et al. [
45
] state that impacts will differ and there are different scenarios. The
fourth industrial revolution will change business models, require higher digitization levels,
and therefore, can contribute to the reduction in emission levels. However, there is a
possibility that with a higher industrialization level, digitization will increase emissions
through increasing car use, traditional energy sources, raw material usage and energy
intensity, but there is also a possibility that these technologies will enable the tracking of
environmental data and provide the necessary information to reduce pollution levels. The
reorganization of business processes can reduce energy intensity, waste creation and usage
of raw materials and durability of products. It is expected that through time, the fourth
industrial revolution will bring more positive than negative effects.
Kahia and Ben Jebli [
46
] state that the industrial revolutions caused higher pollution
levels due to the usage of non-renewable energy sources. They found that industrial
upgrading has different effects in different national economies in the long and short term.
In certain economies, it reduces emissions, while in others, it can increase emission levels
in the long term, while in the short term, it reduces in all economies. Green energy
reduces pollution levels, and they consider that industrial development based on innovative
solutions can contribute towards mitigating CO
2
emissions. The same situation was
observed with income per capita level, where effects differ in various economies.
Han and Chatterjee [
47
] state that poor economies with low levels of industrialization
are bigger polluters then high income and industrialized economies. They found that
industrial development in less-developed economies causes higher pollution levels, and
in industrialized economies, further development of industrial structure and transition
from traditional toward innovative industries with low energy intensity causes lower
pollution levels.
According to Zhou, Zhang, and Li [
48
], improving industrial structure and technologi-
cal development lowers pollution levels. Technologically advanced industries contribute
to lower CO
2
emissions. The share of public companies, fixed capital formation, and FDI
reduce CO2emissions, while the total and urban populations increase them.
Wang et al. [
49
] state that industrial development, urbanization, energy consumption
and income level increase GDP per capita in the panel of 14 countries, while effects differ
per each country. They suggest to improve the industrial structure and enhance innovation
activities in order to reduce the pollution level.
Kofi Adom et al. [
50
] found that shifting industrial structure toward low energy
intensity sectors centered on technological development and innovation can reduce CO
2
emissions. Economic growth can be affected by reshaped industrial structures. Lower CO
2
emissions can be a barrier to economic growth, whereas higher growth rates imply lower
emissions. Reduced pollution levels are influenced by technological advancements, green
energy, and innovative solutions.
According to Aiginger [
51
], decision makers in the EU and industrialized countries
must continue to develop strategies and policies for industrial development in order to
achieve the sustainable development goals. One of the objectives should be to restructure
the industry in order to reduce CO
2
emissions. Technological advancement, innovation
activity, human capital creation and institutional quality are important drivers. These
industrial strategies and policies complement environmental policies. More developed and
industrialized economies that implement these ideas are more likely to achieve long-term
development goals. Technological advancement, which includes reindustrialization with
a different structure, as well as the transition to new technologies, all contribute to lower
emission levels. Such industrial policies, which are already in place in Europe, have yielded
some early results, as absolute and relative (per unit of GDP) pollution levels today are
lower than in the last decade of the twentieth century, and countries with a higher level
of industrialization and greener industrial structure outperform those with a lower level
of industrialization.
Canal Vieira, Longo and Mura [
52
] examined impacts of the EU-ETS (European Union
Environmental Trading System) and industrial policy in mitigating CO
2
emissions from
Sustainability 2022,14, 12554 8 of 25
2005 to 2017 in EU members. They found that different production sectors have different
performances—the same as in different economies. Sectors that are more successful in
the transformation toward new technologies and industrial structure reshaping processes
perform better in mitigating emissions.
Muûls et al. [
53
] confirmed that industrial development toward new technologies that
were innovation-driven resulted in a nearly 5% reduction in pollution levels between 2005
and 2017. Developed EU economies with higher levels of industrialization and a diverse
industrial structure outperform new members in terms of pollution reduction. It was the
result of the EU-ETS and ideas about industrial and environmental policy harmonization.
Kaivo-oja et al. [
54
] compared CO
2
emission reduction determinants in the EU, China,
and the United States. Industrial structure and industrial development, which imply techno-
logical advancement, were important factors in pollution reduction. Innovations, particularly
in green technology, fueled industrial development and technological advancement.
2.3. Innovation Activity and CO2Emissions
One of the primary drivers of societal development is innovation activity. Innovations
improved life quality by making everyday tasks and communication easier, and they en-
abled higher levels of development by increasing productivity and creating new paradigms.
In this chapter, the role of innovation in reducing CO
2
emissions will be discussed. In the
fourth industrial revolution era, innovation, particularly in the fields of green energy and
technological advancement, should be a key to reducing pollution levels.
Ali et al. [
55
] found that innovation activity reduced pollution levels, while invest-
ment activity, an increasing GDP and higher energy consumption caused higher pollu-
tion levels. Results regarding the effects of innovation on environmental pollution agree
with [
8
,
11
,
14
,
24
,
26
,
35
,
40
,
43
,
48
–
50
,
52
,
53
]. Wang et al. [
56
] examined the effects of economic
policy uncertainty on pollution levels on a panel of 137 economies. They found that the
uncertainty level increased emissions, but the effects were lower in developed and industri-
alized countries with an upgraded industrialized structure. Innovation activity, a higher
average population age and rising population reduce the pollution level, while economic
growth and added manufacturing value increase it. Carrion-Flores and Innes [
57
] state
that innovation activity—expressed with investments in R&D and an increased number
of patents—reduces pollution levels in 127 manufacturing sectors across the USA, and
reduced pollution levels enhance innovation activity. Mensah et al. [
58
] examined OECD
economies and found that innovation activity expressed in patent application have different
effects in different economies. In certain economies, it reduces pollution levels, while in
others, it increases pollution; the reason for this might be that patents are not related to
the innovation activity that mitigates CO
2
emissions. However, they emphasized that
innovation activity is a key driver in reducing emission levels. R&D and green energy are
also key drivers in reducing emission levels, while GDP growth can have vague effects.
Khan et al. [
59
] examined the determinants of CO
2
emissions in 176 countries, and the EU
member states was among these economies. Innovation measured with patent applications
resulted in economic growth and a short-term increase in FDI, while it decreased pollution
levels in the long term. FDI affects technological development and industrial structure,
and can confirm that industrial development and structure reduce CO
2
emissions. Trade
openness is a significant driver of the reduction in CO
2
emissions because it enhances
technology and innovation transfer across the world. Institutional quality measured with
different indicators—including the rule of law and corruption, and the transition to green
energy that also represents innovation activity—contribute to decreasing pollution levels.
Institutional quality is important because quality institutions can create a macroeconomic
environment that enhances investment activity, technology transfer, and consequently, the
advancement of industrial structure in certain economies. Welmin et al. [
60
] found that
CO
2
emissions are major source of environmental pollution compared to SO
2
, NO
2
or other
polluters. They found that an increase in innovation activity reduces CO2emissions since
it develops green energy solutions and affects the technological upgrading of industry.
Sustainability 2022,14, 12554 9 of 25
The transition to green energy is another determinant of pollution reduction—the same
as the globalization level. Economic growth, FDI and traditional energy sources increase
CO
2
emissions. Choi and Han [
61
] state that innovation activity and an increase in patent
applications, especially in green technologies, should play a key role in mitigating pollution
levels, and FDI as a main technology transfer channel that reshapes industrial structure
and upgrades industry could reduce pollution. They examined developed and developing
economies and found that innovation activity expressed through patent applications in the
field of green technology and involvement in global trade flows reduce pollution levels
in developed economies, while this is not always the case in developing countries. In the
end, economic development level reduces CO
2
emissions in both groups, while in the short
term, it increases pollution levels in developing economies. FDI and institutional quality
reduce pollution levels.
Grosso et al. [
62
] state that R&D and innovation activity are key drivers in reaching
EU goals related to the zero-emission rate, especially because they enable technological
development and the reshaping of industrial structure. The main finding of the paper
is that all types of innovations should reduce pollution levels in the EU. Such types of
innovations include technological innovations, but also the innovation of business models.
Gilli, Mancinelli and Mazzanti [
63
] found that innovation activity represents and will
represent key determinants in mitigating the pollution level, but it will be necessary to
complement green innovation with other types of innovation, such as business model
innovation, process, product or service. Despite developed EU members outperforming
the less-developed part, the major differences are among manufacturing sectors; however,
in all sectors, innovation activity reduces the pollution level. Balsalobre-Lorente [
64
], when
analyzing five EU countries, stated that investments in innovation and transition to green
energy reduced CO
2
emissions, while EKC was confirmed in the long term, but not in the
short term. FDI increased the pollution level.
According to Wolf et al. [
65
], several factors influence the achievement of zero emis-
sions. Innovation, industrial development and the transformation of industrial structures
through digitization processes should reduce pollution levels. The EU will need to shift
its economic policy paradigms, reshape its innovation policy by ensuring a high-quality
institutional and regulatory framework, and increase and improve spending on such ac-
tivities. Innovation should help the EU compete with its main global competitors, such as
the United States and East Asia, by lowering pollution and increasing economic growth.
Human capital and education quality are also important drivers of lower emission levels.
Constantin et al. [
66
] observed that overall innovation activity, capacity, and R&D
are the major drivers of sustainable development in the EU, allowing it to achieve higher
GDP growth rates while lowering pollution levels. They noticed that developed and highly
industrialized EU economies outperform new Europe economies in industrial structure,
development, and innovation capacity, and thus, in pollution reduction.
According to Vollenbroek [
67
], innovation activities and their outcomes enabled the
highest development and growth rates ever by increasing productivity levels and changing
industrial structure, and they should now be the key in balancing economic growth and
pollution levels in the EU and the rest of the world.
Mazzanti and Rizzi [
68
] investigated the factors that contribute to lower CO
2
emis-
sions in the EU and its manufacturing sector. Innovations such as new products, services,
processes, and business models geared toward sustainable development should be a key
driver in reducing CO
2
emissions. Such innovations reshape industrial structure, acceler-
ate technological and industrial development, and facilitate the transition to renewable
energy sources.
According to Aghion, Veugelers and Serre [
69
], innovation and R&D are major deter-
rents to reducing pollution levels in the EU. Policymakers must increase both private and
public investment in such activities.
Sustainability 2022,14, 12554 10 of 25
2.4. Human Capital and CO2Emissions
Human capital determines the productivity of all other resources, making it a critical
driver of not only economic growth, but also overall societal development. Previous
chapters emphasized and confirmed that innovation drives economic growth. Innovation
is the inexorable result of human activity that adds to human capital. This section will
focus on the role of human capital quality in reducing CO
2
emissions. Human capital is
expected to contribute to lower pollution levels. Different reasons are given, such as the
negative effects on life quality and health, as well as work productivity. Because innovation
activity and the transition to green energy reduce pollution, human capital becomes more
important, and all of these solutions will be the result of quality human capital.
Kwon [
70
] emphasized that human capital is a key driver of overall societal develop-
ment, including a reduced pollution level through innovative solutions in the transition
toward green energy and industry. It is necessary for national economies to find as many as
possible indicators to determine its quality; in addition, they should increase investments
in human capital. This is in accordance with the findings of [14,23,50] and [58].
Wang and Xu [
71
] examined the drivers of CO
2
emissions in developed, developing
and emerging economies. They found that the quality of human capital reduces the
pollution level. A higher percentage of people that use the internet, economic growth,
developed industrial structure and financing enterprises through credit activity reduce the
pollution level. A higher number of people that live in the cities and investment activity
increase the pollution level. Their findings also show different results in different groups of
countries. In developed economies, human capital and internet penetration have higher
effects on pollution reduction.
Khan [
72
], when analyzing 122 economies, found that the quality of human capital is
essential to mitigate CO
2
emissions—the same as financial development. He confirmed EKC
in the long term. The involvement in global trade flows, FDI and an increasing population
increase the pollution level. Salahodjaev [
73
] stated that human capital is necessary in
mitigating pollution—the same as institutional quality. He confirmed EKC in the long
term. Population, level of globalization and bio-capacity increase pollution levels. Iqbal,
Majeed and Luni [
74
] found that quality human capital is an important driver in reducing
pollution levels, while the increasing number of people that live in cities and a higher level
of involvement in global trade increase it. However, the effects differ in developed than
in developing and transition economies; developed economies have advanced industries
and manufacturing, and their products and services are environmentally friendly; thus, in
their case, inclusion in global trade flows reduces CO
2
emissions. Lin et al. [
75
] examined
the correlation among human capital and pollution in Chinese regions. Human capital
is an important driver of the decline in pollution levels, but the most important is that
human capital is innovative, and the personnel involved in R&D, science and engineering
must apply as many patents as possible; the number of patents per applied researcher
reveals their innovativeness. Such human capital will be the driver of innovative ideas
and solutions for technological and industrial development, and the transition toward
green energy. They recommend to all types of economies: high, middle, upper-middle and
low-income to increase investments in human capital, but especially in innovative human
capital. Economic growth in the short term increases pollution levels, while decreasing it in
the long term, thus, confirming the EKC hypothesis. Investment activity, which is the main
channel of technology transfer and industrial development, reduces the pollution level. The
increasing population, manufacturing and energy usage are drivers of higher emissions.
Mance, Kruni´c and Mance [
25
] conjecture that in the 21st century, the HDI is a more
comprehensive measure of societal development, since it includes not only economic, but
also other social components. Nadeem et al. [
76
] found that human capital is an important
driver in reducing pollution levels—the same as the transition toward renewable energy
sources. A higher level of economic complexity, export, involvement in global trade flows,
economic development and urbanization lead to higher emission rates. They suggest that
it is necessary to transfer knowledge across the world to reduce pollution levels, since it
Sustainability 2022,14, 12554 11 of 25
can increase the quality of human capital. Ali, Akram and Burhan [
77
] found that there
are different drivers of pollution levels in various national economies by creating three
groups of economies by pollution convergence levels. Human capital is the most important
driver of CO
2
emissions, while economic complexity, investment activity, inclusion in
global trade flows and total factor productivity have different effects. The effects of each
variable—especially the economic complexity level, since it regards the indices level of
industrial development—depend on the level of technological upgrading of industry
toward green technologies. It is necessary to technologically upgrade industrial structure
with eco-friendly technologies, products, services, organization and business models.
Chen and Wang [
78
] analyzed EU economies in a 30-year period. They found that
human capital—which encompasses knowledge, education levels and general health
conditions—is an important driver of pollution reduction and economic growth, espe-
cially because human capital is a driver of innovation activity and technology upgrading
in industry, of which are very important for lower emission rates. Flores Chamba et al. [
79
]
found that European economies that perform best in the quality of human capital and its
knowledge level reach lower pollution levels. They conjecture that human capital and
knowledge level are key drivers of lower energy usage and pollution reduction in EU
member states and the rest of Europe. Increasing the price of fossil fuels reduce pollution
and energy intensity in the EU, but increase it in the rest of Europe.
Alsaleh, Oluwaseyi Zubair, and Abdul-Rahim [
80
] investigated what factors influence
higher levels of bioenergy usage in the EU, because shifting to such sources is one of the
key enablers of lower emission rates and meeting the EU’s zero-carbon emission targets.
A lack of knowledge can be a significant impediment to a successful transition to such
sources. Their main discovery is that high-quality human capital with a higher knowledge
level, institutional quality, innovation activity and capacity, and economic development are
the most important drivers of the transition to bioenergy sources and, as a result, lower
emission rates. The EU must invest in these factors and increase private investments in
people and innovative solutions. One of the major tasks of the EU is to create a regulatory
and institutional environment that will allow for the development of human capital and
the enhancement of innovation, as well as to invest in infrastructure that will allow for the
transition to bioenergy sources.
According to Braun [
81
], human capital and increasing its knowledge level through
knowledge diffusion were and continue to be important determinants of lower pollution
levels. EU-ETS, which is now one of the EU’s top priorities, necessitates a higher level of
knowledge about the environment, technology (particularly green solutions and energy)
and innovation. For the EU, it is necessary to encourage all stakeholders, including the
public, academic and educational sectors, as well as the business sector, to invest in human
capital and knowledge about EU-ETS and pollution reduction.
Cakar et al. [
82
] identified human capital as one of the most important drivers of lower
emission rates; however, this does not have to be the case in less-developed EU members.
Increasing the number of patent applications is a similar situation because it depends on
whether the patents are environmentally friendly and committed to pollution reduction.
One of the main findings is that increasing the quality and knowledge of human capital
reduces pollution levels, as increased knowledge leads to patents and innovations that can
help reduce emissions.
Alsarayreh et al. [
83
] stated that human capital—through increased knowledge and
quality—contributes to lower pollution levels, while an increase in the general and ur-
ban population increases pollution. They recommend investments in human capital and
innovation activity that will create new technologies necessary for green transition.
2.5. Institutional Quality and CO2Emissions
National economies must build high-quality institutions that are resilient to corruption
because this is the only way to create a quality regulatory and macroeconomic framework.
Such a framework enhances innovation activity, economic and social development, tech-
Sustainability 2022,14, 12554 12 of 25
nological progress, the creation of high-quality human capital, and therefore, is one of the
major drivers of mitigating pollution. It can be measured with a variety of indicators such
as the rule of law, corruption level, government effectiveness of integrity, regulatory quality,
political stability and level of democracy.
Gani [
84
] found that institutional quality—measured with various indicators, such
as the rule of law or corruption levels—reduces pollution levels. Such findings agree
with [
12
,
17
,
24
,
27
,
40
,
41
,
51
,
59
,
60
,
65
,
73
,
80
]. He confirmed EKC in the long term, but in the
short period, he stated that a higher economic growth increases the pollution level—the
same as the industrial and trade-to-GDP ratio. Muhammad and Long [
85
] found that insti-
tutional quality measured through the rule of law, corruption and political stability levels
contributes to lower emission rates in developed, developing and transition economies.
The trade-to-GDP ratio and FDI have different effects in different group of economies,
while energy usage causes higher emission rates. Results of their research emphasize the
importance of building high-quality institutional frameworks through enhancing the rule
of law and reducing the corruption level.
Lisciandra and Migliardo [
86
] found that institutional quality is inversely associated
with emission rates—the same as energy usage—while industrial development in the long
term reduces emissions, and increases it in the short term. Economic growth represents an
important driver of reduced pollution.
Runar, Amin and Patrik [
87
], when examining 124 developed, developing, and tran-
sition economies, found that institutional framework is an important driver for reducing
emission levels. They emphasized the importance of innovations and technological progress
in the transition toward environmentally friendly business and growth models.
Eskander and Fankhauser [
88
] found on a panel of 133 national economies that insti-
tutional quality, quality of legal framework related to emission levels, new laws related
to pollution, economic growth in the long term and service-based economy reduce pollu-
tion levels, while economic growth in the initial stages and higher trade levels increase
emission rates.
Li, Rishi and Bae [
89
] examined economies that receive Official Development Aid
(ODA). Not only is institutional quality in these economies an important driver of pollution
reduction, but it also affects it indirectly, because a higher level of institutional quality effec-
tiveness of ODA programs increases pollution reduction in terms of economic development
and increasing the environmental quality. With a low level of institutional quality, ODA
programs could contribute to higher pollution levels.
Stef and Ben Jabeur [
90
] examined 83 economies and found that institutional qual-
ity is a key driver in lowering CO
2
emissions and that they are key determinants of the
effectiveness of environmental legislation. Regarding poor institutions, new regulations
related to the environment will not contribute to a lower pollution level. Human capital
and the share of territory covered by forests contribute to reduced pollution, while ur-
banization, higher energy intensity and investments increase it. Jian et al. [
91
] found that
institutional quality indicators are important determinants in reducing the pollution level
in China—the same as human capital and globalization—while an increasing population
and energy intensity affects higher emission rates. Strengthening domestic institutions, a
higher globalization level, increasing the quality of human capital, having a sustainable
population growth and transitioning toward green energy are recommendations for China
to reduce pollution levels.
Galeotti [
92
] stated that institutional quality and legal frameworks are key drivers
in implementing any type of policy, including those related to the reduction in pollution
levels in each economy, including the EU. Another important driver of lower emission rates
is technological and industrial progress, because it enables the harmonizing of objectives
related to higher economic growth and lower pollution levels.
Castiglione, Infante, and Smirnova [
93
] investigated the links between fiscal policy
and taxes, environmental protection, economic growth and institutional quality. They
classified EU economies as developed or developing: Scandinavian countries, Benelux,
Sustainability 2022,14, 12554 13 of 25
Austria, Germany, France, and the United Kingdom. The second group consisted of
Mediterranean EU economies that were old EU members. The third group consisted
of ex-communist economies. Their main finding is that institutional quality and legal
framework quality are critical in achieving environmental tax efficiency and balancing
higher levels of environmental tax burden with economic growth. This effect was stronger
in developed and more industrialized EU economies, whereas in the other two groups,
where institutional quality was lower, the effect of institutional quality on lower emission
rate was weaker.
The United Nations Environmental Program research [
94
] stated that institutional
quality is key for successful and effective environmental protection policies and programs
in the EU and the rest of the world. Corruption is one of key indicators of institutional
quality, and therefore, it will affect all programs and policies related to pollution reduction.
Haring [
95
] found that institutional quality is a very important driver for effective
environmental policies in the EU. Countries with lower corruption and inequality levels
are more successful in pollution reduction because people are more likely to believe in
benefits of such policies; the reason for this is a higher level of trust in public institutions.
Fan [
96
] stated that the EU is a pioneer in forming policies and programs related to
environmental pollution. The EU enhances the rule of law and strengthens its institutions
to reach objectives of lower emissions and economic growth. Such institutions enhance
innovation activity and technological and industrial upgrading. The EU adapts its regula-
tory framework to challenges of the 21st century, and one of them is pollution. The legal
framework and quality institutions are factors of success of environment protection policies.
The problems in pollution reduction are disparities among member states, where developed
and highly industrialized economies outperform the rest of the EU. The author suggests to
Chinese authorities to set the EU as a benchmark for their environmental policies that will
be harmonized with economic growth policies.
Chang and Hu [
97
] found that institutional quality plays an important role in environ-
mental protection in the EU, but different indicators have different effects. Reducing the
corruption level is essential to reduce the pollution level. Higher political involvement can
always represent a problem for reduction of pollution level.
According to Apergis and Garcia [
98
], institutional and governance quality, as well
as a regulatory framework, are critical in lowering emission rates in the EU because they
reduce corruption, ensure that public funds are distributed fairly, and increase investments
in appropriate technologies and programs. In such cases, government policies enable not
only business sector development, but also investment in environmental programs and
solutions. In highly developed and highly industrialized EU members, emission rates are
lower. Their level of industrialization and welfare allows them to invest in innovations,
technological and industrial structure upgrades, and green energy.
Ojonugwa, Osama and Osama [
99
] found that public institutions and their quality in
the EU increase the level of environmental protection in the EU when regulatory frame-
works and judicial systems are characterized with the rule of law, the corruption level
is reduced and controlled, authorities are effective, the political situation is stable and
threats related to terrorist or criminal activities are on a low level. It also enables economic
development, while economic growth and tourist activity increase CO2emissions.
Based on the theoretical background acquired from the literature review, the following
research hypotheses regarding statistical associations were formulated:
1.
The change in innovation activity is negatively associated with the level of CO
2
emissions.
2.
The improvement in industrial structure is negatively associated with the level of CO
2
emissions.
3.
The quality of human capital is negatively associated with the level of CO
2
emissions.
4. Higher institutional quality is negatively associated with the level of CO2emissions.
For research hypotheses 1, 3 and 4, besides the usual statistical significance, they are
also expected to bear a negative sign in the statistical association. Research hypothesis 2
Sustainability 2022,14, 12554 14 of 25
only expresses an expectation regarding the statistical significance regardless of the sign
since it is a purely qualitative variable.
3. Material and Methods
3.1. Model Explanation
CO
2
emissions are very harmful for society and cause negative effects, such as en-
dangering human capital, causing health issues, reducing education performance, global
warming, climate changes and various problems in the functioning of society—which
agrees with the findings of [
8
–
10
,
16
] and [
22
]. The majority of pollution comes from CO
2
emissions—which agrees with [
8
]—while [
11
,
15
] and [
17
] consider it as a major issue for
global society. The EU tends to reduce emission rates to zero, which agrees with [
13
,
61
,
64
]
and [
80
]. The existing literature found various determinants of lower emission rates, such
as institutions, human capital, innovation activity, transition to green energy solutions,
environmental taxes and investments in R&D, while there are vague effects of industrial
development and GDP growth.
CO2= f(CPIi,t; CIPi,t; knowi,t; patit; lawit) (1)
The dynamic panel model in this paper is made from 123 observations from the period
2012 to 2019 and is based on the EU. Equation (2) describes a dynamic model with a single
time-changeable lagged variable based on Galovi´c and Bezi´c [100].
yit =βyit−1+ui+vit,|β|<1 (2)
where y
it
is the dependent variable in period t; y
t−1
is the dependent variable with lag
for one period from t; u
i
represents individual time-invariant effects. Value v
it
is the
random error. Individual effects are taken as stochastic. Additional significant assumptions
about stability of the model are errors v
it
, which are serially uncorrelated. Individual
time-invariant effects are mostly related to the previous effect of the dependent variable in
the model, which resolves the problem of endogenity.
Subsequently, the following model is tested (Equation 3):
dCO2it =β0+β1CPIi,t +β2CIPi,t +β3knowi,t +β4pati,t +β5lawi,t + ui,t + vi,t (3)
Generalised Method of Moments (GMM), two-step dynamic panel is chosen to be the
appropriate estimator for this study because it is useful for both hypothesis testing and
coefficient estimation, and has many other useful properties, as shown in more detail in
Piper [
101
]. Accounting for autoregression by the use of a lagged dependent variable is
also one these useful properties described in more detail in Baum [
102
]. Hall [
103
] states
that GMM is an appropriate method to get precise and asymptotically normally distributed
estimators of variables, and agrees with [
102
] about the increasing use of GMM in economic
research. GMM sets two types of restrictions. The identifying restrictions include data that
are employed for estimations, and the overidentifying restrictions are related to the rest of
the data and are the key driver in relation to the reliability of a dynamic panel. Hansen [
104
]
agrees with [
101
] about the accuracy of the method regarding the asymptotic distribution of
normality, and states that GMM ensures the reliability of the model under certain restricting
conditions. Hansen [
105
] points out that the GMM estimator is applicable to large samples
and eases comparisons. It enables calibration verification because it chooses the best linear
combination between the variety of moment restrictions.
The Panel GMM is an often-used method when the variables are non-stationary,
when the dependent variable is dynamic i.e., when it depends on the previous values of
itself. Panel GMM allows econometric models to be specified while avoiding unwanted or
unnecessary assumptions and heteroscedasticity of unknown origins. When the number of
cross-sections is greater than the number of time periods, GMM provides better predictions
of coefficients in terms of a lower standard error.
Sustainability 2022,14, 12554 15 of 25
For the appropriate decision on the method, one should take a careful look at the data.
Data begets the method.
3.2. Data and Variables
This research measures the effects of institutional quality, technological and industrial
development, innovation activity and human capital on CO
2
emissions. For the purposes
of this paper, 27 EU economies for the period 2012–2019 are observed. UK is not included in
the model due to Brexit. As a proxy for pollution levels, CO
2
emissions are most consistent
in providing stable datasets. All CO
2
emission data are in 2015 USD per kg taken from
the World Bank [
106
] World Development Indicators. It agrees with the relevant literature
that states how CO
2
emissions are a major cause of pollution levels. Two measures of
institutional quality are used. The World Bank [
106
] rule of law estimation measures the
perception of inhabitants in relation to the state of rule of law in their country. It measures
the quality of contract enforcement, property rights, policing, and the courts, as well as the
likelihood of crime and violence. The estimate gives the country’s score on the aggregate
indicator in units of a standard normal distribution, i.e., ranging from approximately 2.5 to
2.5. Data are taken from World Bank, Worldwide Governance Indicators [
107
]. Another
indicator is the corruption perception index that estimates the rate of institutional corrup-
tion in public administration. Data are taken from Transparency International [
108
]. The
literature review found that institutional quality is measured with these two indicators, and
it represents one of the key determinants in lowering emission rates. Institutional quality
ensures equal framework and a fair distribution of public funds. As a proxy for techno-
logical and industrial development, CIP or competitive industrial performance index is
chosen and is measured by the United Nations Industrial Development Organization [
109
],
which states how measuring this index is important in the period of the fourth industrial
revolution, where the industrial sector is a driver of innovation and transition toward the
fourth industrial revolution. This index measures the level of technological advancement
in the industrial sector of certain national economies, the ability of industry of certain
economies to produce products that are competitive on the global market and its share
in global exports. As a proxy for human capital, the knowledge and technology output
variable is chosen, which is an indicator of global innovation index and is measured by
WIPO [
110
]. It measures the results of knowledge processes and investments in human
capital. Human capital determines the productive capacity of all resources and is a major
driver of development, especially in the knowledge economy. Educated people are more
aware of the pollution problem and will have more capacity to find solutions to mitigate
CO
2
emissions. As a proxy for innovation capacity, the indicator of patents per capita
is used, which is taken from World Bank [
106
], World Development Indicators database.
Calculation of this indicator is shown in the formula in Equation (4).
Patents per capita =Total numb er o f patent s
Total po pulation (4)
This indicator for the innovation activity variable is also in accordance with the relevant
literature. Innovation activity is essential for reducing pollution, since the transition to
renewable energy requests innovative solutions and patents that will enable industries to
accept environmentally friendly solutions. The variables are shown in Table 1.
Sustainability 2022,14, 12554 16 of 25
Table 1. Explanation of the variables.
Symbol Variable Explanation Database
d_CO2Pollution CO2emissions (kg per 2015
USD of GDP)
World Bank–World
Development Indicators
d_CPI Institutional quality Corruption perception index Transparency International
d_CIP Technological and industrial
progress
Competitive industrial
performance index
United Nations–United Nations Industrial
Development organization
d_know Human capital Knowledge and technology
output Global innovation index database
d_pat Innovation activity Patents per capita World Bank–World
Development Indicators
d_law Institutional quality Rule of law World Bank–Worldwide
governance indicators
3.3. Correlation Matrix
For the purpose of testing for multicollinearities, a correlation matrix shown in Table 2
was calculated.
Table 2. Correlation matrix.
d_CO2d_CPI d_CIP d_law d_know d_Pat
1.0000 −0.1977 −0.1189 −0.0942 −0.2185 −0.0056 d_CO2
1.0000 −0.0858 0.0809 0.0564 0.0188 d_CPI
1.0000 0.877 0.1061 0.0259 d_CIP
1.0000 0.0107 −0.0975 d_law
1.0000 −0.0577 d_know
1.0000 d_Pat
The correlation matrix in Table 2shows no multicollinearity between variables. None
of the coefficients are higher than the critical 0.5 value. Moreover, all of the variables are
below 0.22. There are three instances of non-expected signs, i.e., where the signs are not
strictly commensurate with the theory. Firstly, there is a positive relationship between the
changes in the corruption perception index and the competitive industrial performance
index. Instead, negative but close-to-zero values were found. Secondly, negative signs but
with values close to zero were also found for the changes in the patents per capita and
the changes in the rule of law. Lastly, changes in patents per capita and changes in the
knowledge and technology output also have a negative sign, although small in magnitude.
None of the coefficients are close to the critical value of 0.5.
3.4. Descriptive Statistics
The descriptive statistics of the used variables’ first differences are shown in Table 3.
Table 3. Descriptive statistics.
Variable Mean Median S.D. Min Max
d_CO2−0.0113 −0.00867 0.0231 −0.122 0.0993
d_CPI 0.175 0.000 3.99 −34.0 34.0
d_CIP −0.00117 0.000153 0.00820 −0.0362 0.0620
d_law −0.00404 −0.00449 0.0785 −0.319 0.209
d_know −0.818 −0.700 5.10 −20.1 18.8
d_Patent 0.000000621 −0.00000187 0.000054 −0.000470 0.000357
3.5. Panel Unit-Root Test
The unit-root test results are shown in Table 4.
Sustainability 2022,14, 12554 17 of 25
Table 4. Results of unit-root tests.
Test Variable Level First Difference
KPSS
CO20.0103 0.6792
CPI 0.0023 0.6393
CIP 0.0077 0.5419
know 0.1323 0.9446
pat 0.0006 0.3418
law 0.7920 0.8743
Levin, Lin and Chu
CO20.0000 0.0000
CPI 0.3936 0.0000
CIP 0.0128 0.0000
know 0.1456 0.0000
pat 0.0000 0.0000
law 0.4540 0.0000
Augmented
Dickey–Fuller
CO20.8745 0.0000
CPI 0.9709 0.0000
CIP 0.2687 0.0000
know 0.7997 0.0000
pat 0.0004 0.0000
law 0.7573 0.0000
Phillips and Perron
CO20.0003 0.0000
CPI 0.4374 0.0000
CIP 0.0000 0.0000
know 0.0070 0.0000
pat 0.0000 0.0000
law 0.1265 0.0000
The unit roots were tested with the Kwiatkowski–Phillips–Schmidt–Shin (KPSS);
Augmented Dickey–Fuller (ADF); Im, Pesaran, and Shin; Levin Liu; and Chu and Phillips
Perron test. The null hypothesis of the KPSS test assumes the non-existence of unit roots,
i.e., that the time series is stationary around the trend, whereas all other tests assume non-
stationarities, i.e., the existence of unit roots in their null hypotheses. Table 4confirms that
all variables are stationary in the first differences, but the variable pat is stationary in levels
in all tests except in the KPSS test. According to Arltova and Fedorova [
111
], the KPSS test
is reliable and applicable for shorter periods of time and provides very accurate test results.
By making the data stationary, one avoids a spurious correlation due to data dynamics.
The results of the stationarity tests confirm the model’s robustness. The results of the
stationarity tests confirm the need to difference the variables. All variables are stationary
after first differencing. Thus, this research is determined toward statistical associations
between the changes in variables.
4. Results
The test results of the Panel GMM dynamic model are shown in Table 5.
Variable d_CO
2
(
−
1) represents the lagged dependent variable and is measured in
kilograms per USD. All other variables show negative signs. CPI—corruption perception
index—is measured from 0–100, where 0 is the highest and 100 is the lowest level of
corruption. The results show that increasing the index value by one point will reduce CO
2
emissions by 0.002 kg. The competitive industrial performance index (CIP), measured by
UNIDO, is a measure of industrial structure “quality” and technological development,
with values ranging from 0 to 1—where lower values that are closer to zero imply a lower
quality of industrial structure and technological advancement. Increasing the value of CIP
by 1% will reduce CO
2
emissions by 0.577%. The knowledge output as an indicator of
the global innovation index is measured from 0 to 100, where the higher indicator values
imply better results of knowledge processes and investments in human capital. The results
indicate that increasing the value of this indicator by 1% coincides with the reduction in
Sustainability 2022,14, 12554 18 of 25
CO
2
emissions by 0.00034%. Patents per capita represent the result of the formula patent
applications/total population. Results indicate that an increase by 1% in the number of
patents per capita will reduce CO
2
emissions by 13.14%. The rule of law is measured on a
scale ranging from
−
2,5 to +2,5, where higher values imply higher levels of the rule of law.
The results indicate that a 1% increase in this value is commensurate with the reduction in
CO2emissions by 0.032%.
Table 5. Results of the model (dependent variable: d_CO2).
Coefficient Std. Error z p-Value Significancy
d_CO2(−1) 0.0809932 0.0136867 5.918 < 0.0001 ***
d_CPI −0.00185476 8.38444 ×10−5−22.12 < 0.0001 ***
d_CIP −0.577332 0.140301 −4.115 < 0.0001 ***
d_know −
0.000347782
0.000172190 −2.020 0.0434 **
d_pat −13.1497 2.97081 −4.426 < 0.0001 ***
d_law −0.0321677 0.00671299 −4.792 < 0.0001 ***
T3 −0.00737523 0.00111335 −6.624 < 0.0001 ***
T4 −0.0148437 0.00295451 −5.024 < 0.0001 ***
T5 −0.00821629 0.00123617 −6.647 < 0.0001 ***
T6 −0.00331857 0.00100970 −3.287 0.0010 ***
T7 −0.0114142 0.00116085 −9.833 < 0.0001 ***
Note: In the last column Significancy is explained p-value, where pvalue labeled ***, indicate the level up to 1%
significance, and pvalue labeled **, indicate the level up to 5% significance. Sum-squared resid = 0.044741, S.E. of
regression = 0.019987, Number of instruments = 24, Test for AR(1) errors: z =
−
2,07249 [0.0382], Test for AR(2)
errors: z =
−
0.589242 [0.5557], Sargan over-identification test: Chi-square(13) = 14.8812 [0.3148], Wald (joint) test:
Chi-square(6) = 1089.05 [0.0000], Wald (time dummies): Chi-square(5) = 213.349 [0.0000].
All variables, including time dummies, are statistically significant at p< 0.01. Only
the variable d_know is significant at the level p< 0.05. The Arellano–Bond test’s null
hypothesis asserts that autocorrelation does not exist in the first differences of the errors.
If the errors are not serially correlated, the test may show a first-order correlation but
not second- and higher-order correlations. The presence of first-order autocorrelation is
frequently overlooked because the parameter estimates are consistent with the presence
of autocorrelation among the first differences of the residuals. The test reveals that the
first-order AR(1) statistic is statistically significant, but not the second-order AR(2) statistic.
The results of the Arellano–Bond test statistics thus reject the existence of second-order
autocorrelation since the AR(2) probability value is above 0.5. We conclude there is no
residual autocorrelation. The results of the Sargan test, 0.3148 > 0.05, indicate the accuracy
of the models, while the results of the Wald test imply that the variables in the model show
an adequate level of explanatory power.
5. Discussion
The empirical findings imply that institutional factors, defined as rules and regulations
that guide human behavior, provide future guidance towards greener production and
are just as important as technological progress in guiding economies toward the green
economy goal.
In other words, the hypothesis testing part of the analysis confirmed the conjectured
research hypotheses. The second part of expected results was concerned in gaining some
new information about the strength of those associations: the coefficient estimation.
For various reasons, ranging from the presence of unit roots in the panel time series to
the differences in the idiosyncratic effects of the cross sections, the optimal hypotheses test
and coefficient estimation method was GMM. This method, combined with the necessary
first differencing of the underlying variables, allowed us to answer the hypotheses about
relationships between the changes in variables. In a model with many variables and the
risk of multicollinearity among variables—i.e., how much does a change in one variable
correlate with a change in another variable? The findings do not contradict predictions
regarding the signs and directions of causality.
Sustainability 2022,14, 12554 19 of 25
CIP (competitive industrial performance) index, a variable used in the model as a
proxy for technological and industrial development, is a sign of technological advancement
and the transformation of an industrial sector in a particular national economy toward
the fourth industrial revolution. The results show that changes in CO
2
emissions are
inversely related to changes in industrial and technological development. It is statistically
significantly (p< 0.05) associated with the CO
2
emissions reduction. Not every industry
is the same in producing CO
2
emissions. Moving upwards on the competitive industrial
performance (CIP) scale significantly reduces CO
2
emissions, since industrialized countries
are able to cherry-pick the industries or their clean sectors and outsource “dirty” industries
or their “dirty” sectors to industrializing countries, not least because of the introduction of
the EU-ETS that monetizes CO2emissions.
Human capital quality, essential for social development and represented by the knowl-
edge output indicator variable, is inversely correlated with CO
2
emissions. The EU must
be focused on creating its own human capital and attracting quality human capital from
different parts of the world. Patents p.c. are a proxy variable for innovation activity. Patents
are one of the results of human capital, and both variables resulted in not being multi-
collinear with one another. The negative sign in the coefficient estimation output confirms
the importance of innovation activity as a factor in pollution reduction. Pollution is of
anthropogenic origin. Its reduction will also necessarily be the consequence of innovative
anthropogenic activity. Innovations drive industrial development and technological im-
provements, including the ones in the field of pollution reduction. This means that a higher
level of innovation activity in combination with institutional arrangements, such as the
EU-ETS, should lead to new green solutions, technological development and industrial
structures that are more environmentally friendly.
Finally, yet importantly, the positive changes in institutional quality represented
by the rule of law and CPI are also inversely correlated to changes in CO
2
emissions.
The higher a country stands on a rule of law ladder, the more it takes care about the
environment, since the rule of law includes the institutionalization of property rights
and rules on environmental protection. This is commensurate with the ordo-liberal idea
and the Coaseian theorem that well-defined property rights and other institutional forms
about resources, also including environmental commons, create incentives for their most
appropriate use. For this incentive structure to work, there is the need to dampen the
negative effects of institutional corruption. The combination of the rule of law index and
the CPI that are not multicollinear, but commensurate, gives us the more complete picture.
6. Conclusions
The paper examines many factors that contribute to CO
2
emissions and estimates
the coefficients with the ultimate objective of reducing environmental pollution. A total
of 27 EU nations are included in the dataset, spanning the years from 2012 to 2019. This
dataset necessitates the use of a dynamic panel inferential statistical approach due to the
dataset’s non-stationarities, heteroscedasticities and autoregression. The dynamic panel
Generalized Method of Moments (GMM)’s two-step estimator has produced coefficients
with the lowest standard errors and greatest stability. Following a detailed description
of the factors that contribute to CO
2
emissions in the literature review, hypothesis testing
and coefficient estimation modelling come next. According to the results, CO
2
emissions
are negatively related to innovation, industrial structure and development, human capital
and institutional framework. The results of the econometric model confirm the intuitively
expected statistical relationships and may provide the EU and other policymakers with
guidelines on the drivers of pollution reduction, while also confirming the fundamental idea
that technology, industry and the economy should be developed in an environmentally
friendly direction. The results of this study support the conjecture that technological
development and lower CO
2
emissions are commensurable. The paper’s most important
theoretical contribution is a thorough study of the effects of several variables in tackling
Sustainability 2022,14, 12554 20 of 25
environmental pollution and providing us with new cognitions about the indicators, factors
and determinants necessary for pollution reduction policies in the EU.
Reduced CO
2
emissions are a result of factors associated with the fourth industrial
revolution, such as human capital, which powers each technical advancement and innova-
tion activity. With these insights, this research adds to the theory and discourse around the
consequences and advantages of the fourth industrial revolution, particularly in the area of
its contribution to reducing climate change and attaining carbon neutrality. Particularly
given that it measures CO
2
emissions, which, in comparison to NO
2
or SO
2
emissions, are,
according to pertinent research, the most significant.
The results of the econometric model are commensurate with the literature review,
providing important guidelines for policy makers, not only in the EU, but also in other
regional and national economies. The fourth industrial revolution was a strategy to increase
the European competitiveness and economic growth, but also to reach sustainable develop-
ment goals and contribute to lower pollution levels. The results of our model may provide
guidelines to the EU and other policy makers, since the results clearly point towards the
variables that are statistically associated with pollution.
The EU industrial policy should be based on innovation and technology and a higher
level of digitization and automation. Focus should be given to green innovations that
contribute to pollution level reduction. The EU policy framework has to be focused on
creating an innovation-enhancing environment that implies the necessity of investments in
R&D by private and public sectors simultaneously.
One of the EU goals is to increase investments in R&D to 3% of GDP. To increase
innovation activity, it is necessary to create macroeconomic environments that encourage
business activity, including starting new domestic businesses, start-ups and FDI. Innovation
activity and technological shifts are driven by high-quality human capital, especially in
today’s era of knowledge economy. Human capital determines the productivity of all
other resources. The results of the model confirm the importance of human capital in
pollution level reduction. It is highly important for EU policymakers to focus on creating
and attracting quality human capital from different fields.
Creating high-quality human capital implies the necessity for investment, as well as
educational system reforms that are adapted to the needs and requests of the job market
in the 21st century. EU policy should continue with the integration between the busi-
ness, scientific, education and public sector, and focus on their previously set goals to
increase higher education levels including lifelong learning. Attracting foreign workforce
is necessary, since the battle for high-quality workers is happening at a global level.
The results of the model confirm the importance of the institutional framework quality,
where lower corruption and higher levels of the rule of law are commensurate with the
reduction in the pollution level. The EU should continue strengthening its institutions that
are on a supranational level, but should also encourage member states on strengthening
institutions on their national, county or municipal level. Regulatory frameworks should
encourage business activity, but should also focus on reducing pollution levels. This is
why it is highly important to continue fighting corruption and strengthening the judicial
system. To achieve these goals, it is necessary to continue with the cohesion policy that
will reduce not only economic, but also technological, institutional and educational gaps
between developed and less-developed member states.
This study has its limitations. Firstly, there is a relatively short time period of analysis
of only 8 years. This is due to the fact that the fourth industrial revolution started only
in 2011. Another limitation is the problem of missing variables. We tried to address this
problem by focusing the study on the correlation between the technological advancement
and human capital as drivers of the fourth industrial revolution, on the institutional
framework encouraging technological shifts, pollution reduction and CO
2
emission levels.
Sustainability 2022,14, 12554 21 of 25
Author Contributions:
Conceptualization, D.B., H.B. and D.M.; methodology, D.B. and D.M.; soft-
ware, D.B. and D.M.; validation, D.B., D.M. and H.B.; formal analysis, D.M.; investigation, D.B.;
resources, H.B.; data curation, D.B.; writing—original draft preparation, D.B. and D.M.; writing—
review and editing, D.B., D.M. and H.B.; supervision, H.B.; project administration, D.B. and H.B.;
funding acquisition, H.B. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the University of Rijeka project “Industry 4.0 and export
competitiveness of European union”, grant number uniridrustv-18-1611431.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
World Bank, World Development Indicators, 2022, Available online:
https://databank.worldbank.org/source/world-development-indicators (accessed on
18 January 2022
).
World Bank, Worldwide Governance Indicators, 2022, Available online: https://databank.world
bank.org/source/worldwide-governance-indicators (accessed on 18 January 2022). Transparency
International, Corruption Perception Index, 2022, Available online: https://www.transparency.org/e
n/cpi/2021 (accessed on 20 December 2021). United Nations Industrial Development organization,
Competitive Industrial Performance Index, 2022, Available online: https://stat.unido.org/content/p
ublications/competitive-industrial-performance-index-2020%253a-country-profiles (accessed on 19
December 2021). WIPO, Global Innovation Index, 2022, Available online: https://www.wipo.int/glo
bal_innovation_index/en/ (accessed on 17 December 2021).
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
Abbreviations
CEE Central and Eastern European
CH4Methane
CO2Carbon dioxide
EKC Environmental Kuznets Curve
EU-ETS European Union – Environmental Trading Scheme (System)
FD First Differences
GMM Generalized Method of Moments
HDI Human Development Index
N2O Nitrous Oxide
ODA Official Development Aid
PPM parts per million
R&D Research and development
ROI Return on investments
WB World Bank
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