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Vol.:(0123456789)
Environment, Development and Sustainability
https://doi.org/10.1007/s10668-023-03315-9
1 3
Exploring thelinkage betweenglobalization
andenvironmental degradation: adisaggregate analysis
ofIndonesia
YasirRasool1· DuJianguo1· KishwarAli1
Received: 26 March 2023 / Accepted: 29 April 2023
© The Author(s), under exclusive licence to Springer Nature B.V. 2023
Abstract
In recent years, there has been a significant increase in global interaction between nations,
which has positive and negative impacts on environmental health. To better understand the
effects of globalization on carbon emissions in Indonesia, the study adopted auto-regres-
sive distributed lag approach to analyze the long-term and short-term relationships among
variables and the vector error correction method to find the causal interaction among varia-
bles from 1990 to 2020. The study revealed that economic globalization positively impacts
on CO2 emissions, while social globalization mitigates environmental degradation. How-
ever, political globalization and human capital does not appear to affect CO2 emissions
significantly. Furthermore, we found that energy consumption and economic growth con-
tribute to increased emissions. The study also observed the environmental Kuznet curve
decreases environmental deterioration and increases environmental sustainability in the
region. Therefore, our results suggest that Indonesia should carefully regulate trade open-
ness and foreign direct investment to minimize its negative environmental impact. Addi-
tionally, we recommend encouraging the social aspects of globalization to help mitigate
CO2 emissions.
Keywords CO2 emissions· Globalization index· Human capital index· Indonesia
Abbreviations
CO2 emission Carbon emission
VECM Vector error correction term
FDI Foreign direct investment
GHGs Greenhouse gases
ECO GLOBAL Economic globalization
* Kishwar Ali
kishwarali@ujs.edu.cn; Kishwar.mcb@yahoo.com
Yasir Rasool
yasirrasool3@gmail.com; 1000006284@ujs.edu.cn
Du Jianguo
djg@ujs.edu.cn
1 School ofManagement, Jiangsu University, Zhenjiang, China
Y.Rasool et al.
1 3
POLGLOBAL Political globalization
SOCGLOABL Social globalization
ARDL Auto-regressive distributed lag model
EKC Environment Kuznets curve
CEEC Central and Eastern European countries
HI Human capital index
EG2 Economic growth square
GDP Gross domestic product
EU Energy use
NAFTA North America, Mexico, Canada, United States
ICT Information Communication Technology
DW Durbin Watson
ECT Error correction term
1 Introduction
One of the most extreme intimidations to quality of life in the modern era is the catastrophe
of the environment, primarily attributed to the emission of greenhouse gases. The emission
from these gases is creating an alarm in global situation, producing continuous hazards for
the sustainability of the environment and life on earth. According to Ahmad etal. (2018)
and Jianguo etal. (2022), the most prominent gas among GHGs is carbon dioxide. They
further explained that human activities play a focal role in stemming carbon emissions,
destroying the quality of life (Ali et al., 2022; Wang et al., 2019a, 2019b, 2019c). The
steady increase in energy consumption usage for production purposes is threatening cir-
cumstances for deteriorating environmental quality (Destek, 2019). CO2 is the main culprit
for GHGs, global warming daily and continuously damaging the climatic system (Zhang
etal., 2018). To that end, the adverse effects of carbon emissions on growth and humans
cannot be disregarded.
Our research work aimed to empirically probe the impact of globalization on carbon
dioxide emissions in the case of Indonesia. Globalization is the name of shifting from a
national level beyond the boundaries worldwide to strengthen global and national econo-
mies in the presence of cultural and regulatory barriers (Mehmood, 2021). The effects of
globalization with the traits of social, political and economic globalization of the environ-
ment have mixed insights from the last few decades. The economic way of globalization
via trade and foreign investments can mitigate environmental issues by introducing innova-
tive and sustainable technologies. Traditional technology and energy usage are a manda-
tory part of damaging the environs. The effects are considered noteworthy for improving
the environment, including scale, composition and technique effects directly linked with
innovative and sustainable technology (Shahbaz etal., 2018; Zhang etal., 2017). Besides,
Apergis et al. (2021) contend that globalization inspires human demand by dovetail-
ing far-flung areas, causing an expansion in resource depletion, consumption, and waste
generation.
Incorporating the multidimensional concept of globalization into the existing body of
literature is crucial, as highlighted by Destek (2019). The study identifies three primary
factors that contribute to globalization, namely social, political, and economic factors.
Prior empirical research has investigated the relationship between carbon emissions and
various aspects of globalization. To enhance our understanding of this relationship, it is
Exploring thelinkage betweenglobalization andenvironmental…
1 3
essential to consider the multiple dimensions of globalization and their potential impact
on carbon emissions. Therefore, further research is needed to explore the nexus between
carbon emissions and the diverse facets of globalization, including social, political, and
economic factors. Such research could shed light on the complex interplay between glo-
balization and environmental sustainability and inform policy interventions to mitigate the
adverse effects of carbon emissions on the planet. For instance, Apergis etal. (2021), Sola-
rin and Al-mulali (2018) and Xiaoman etal. (2021) have plaid the relationship and effects
of international investment (FDI) on carbon emission. The influence of liberalization trade
on carbon emission was observed in the research work of Muhammad and Khan (2021).
Similarly, the study of Kamran et al. (2019) for international agreements, like the Paris
climate agreement, and emission (Akhtar etal., 2021; Ozcan & Apergis, 2017) for using
internet effects and emission (Adebayo etal., 2023a, 2023b, 2023c, 2023d) for FDI and
CO2 emission have deliberately measured the connection among the segments of globaliza-
tion and carbon emissions.
Based on the above solid reasoning from past studies, the impact of globalization with
the main three proxies on carbon emission in Indonesia’s framework cannot be disregarded.
The good and bad association between global and GHG emissions has been observed in
numerous studies (Ahmed etal., 2019). For example, in the study of Ozcan and Apergis
(2017), while discussing the social aspect of globalization with the variables of internet
usage and carbon emission, they concluded internet usage plays a vital role in reducing
carbon emissions. Similarly, contradictory findings were observed with the same variables
in the research work (Salahuddin etal., 2016). Likewise, the impact of tourism on carbon
emission has also contradictions in results in studies of de Vita etal. (2015) and Azam
etal. (2018) for Turkey and the panel of Malaysia, Singapore and Thai- land, respectively.
These findings motivate us to observe this relationship in Indonesia for impressive results
for adding to the previous literature. Our focus on the country-specific study will build
thoughtful literature on this bond of association among the variables. Furthermore, many
scholars have observed the correlation between GHG emissions and economic growth in
recent and past studies. For example, the research work of Chen etal. (2019) in China,
Mikayilov etal. (2018) in Azerbaijan, Cherni and Essaber Jouini (2017) in Tunisia and
Acheampong (2018) in MENA countries found a bilateral casualty relationship between
growth and emission. While Adebayo (2023) found the causality among the financial risk,
economic collaboration, disintegrated energy, and economic complexity to load capacity
element. Previous experiential works are accessible on the carbon and growth nexus with
inconsistent consequences and judgments (Wang etal. 2019a).
Similarly, some scholars have examined the association between energy emissions
and growth in different time-spanning and regions with different findings. The studies of
Churchill etal. (2023) and Ali etal. (2023a, 2023b, 2023c) found positive bilateral causal
between energy consumption and economic growth in their panel studies. Furthermore,
the empirical work of Ali etal. (2023a, 2023b, 2023c) in the USA found no connection
between energy consumption and economic growth. Based on this past research work, we
can say that the results for each study are different for each country with other policies.
Therefore, conducting this study in Indonesia is essential for some exciting consequences
and suitable policy suggestions.
The main contribution of this document to the present empirical and theoretical litera-
ture is by three dimensions; of globalization, such as economic globalization, social glo-
balization and political globalization.) To our knowledge, this is the first manuscript that
investigates the influence of different dimensions of globalization on environmental deg-
radation in Indonesia. The impact of energy utilization and economic growth influence on
Y.Rasool et al.
1 3
the environment is also examined as well as we also checked the validity of the Environ-
ment Kuznets Curve hypothesis. This study also adds the human capital index as a control
variable. We utilized Auto-Regressive Distributed Lag Model for all these process analyses
to explore both short and long-run impacts of variables on the environment. For causality
direction among the parameters, we used the VECM technique.
The remaining part of our research work is arranged in the following sequence. Segment
(II) of this study provides a detailed literature review based on past and current empirical
studies. Segment (III) represents methodology and data collection with econometric proce-
dures. Segment (IV) reflects a discussion of results, while Segment (V) regards conclusion
and policy implications.
1.1 Case ofIndonesia
Indonesia has adopted the 2022 UN Climate Agreement COP27. COP27 took place in
2022 in Sharm el-Sheikh, Egypt. The main goal of COP27 is to continue the global effort
to address climate change and to strengthen the implementation of the Paris Agreement,
which aims to limit global temperatures to below 2°C, preferably 1.5°C. Countries are
expected to discuss and agree on specific measures to reduce GHGs, increase climate adap-
tation and mitigation funding, and ensure transparency.
Indonesia is a country located in Southeast Asia, and it contains more than 17,000
Islands. Indonesia has more than 270 million population and it is the fourth largest country
in the world regarding population. It has more than 270 million population. Indonesia is a
country in which about 300 ethnic groups and more than 700 languages are spoken. Indo-
nesia is famous for its natural resources, such as natural gas and oil. Indonesia is important
in the global energy market based on its natural resources. Furthermore, it is the largest
country in the world that produces crude palm oil and the second largest coal producer.
Indonesia comes under the umbrella of oil-based economies, which entirely depend on fos-
sil fuels for multiple sectors, including transportation, industries, production and household
sectors (Azam etal., 2015), creating severe types of natural damages. Rasool etal. (2019)
discovered that in the economies of Indonesia gas and oil sector has a big contribution.
The study of Farabi etal. (2019) argued that Indonesia is experiencing a rapid upsurge
in energy usage, which is contributing a lot to carbon emissions in that specific territory
from past decades with increasing economic growth trends. Moreover, as energy consump-
tion from fossil fuels is the main culprit of carbon emission (Ali & Bu, 2022), negative
externalities of carbon emissions upon human capital cannot be disregarded (Wang etal.,
2019b). Globalization plays an essential part in any country’s economic development
(Zafar etal., 2019), and at the same stage, it also causes a sustainable environment with
dangerous impacts on human health (Sasana etal., 2018). Globalization, as measured by
the index of KOF and its three dimensions, namely ECO-GLOBAL, SOC-GLOBAL, and
POL-GLOBAL, has significantly increased over the last few decades, particularly in the
case of Indonesia. As per our knowledge, there is no country-level study that examines the
connection between global and carbon emissions in the instance of Indonesia. This work
will be considered the first contribution in the literature for Indonesia, which reflects this
complex relationship of variables.
Indonesia has taken steps to address these environmental challenges, including setting
targets for reducing greenhouse gas emissions, promoting renewable energy sources, and
implementing conservation programs to protect its natural resources. However, much work
Exploring thelinkage betweenglobalization andenvironmental…
1 3
still needs to be done to ensure sustainable development and protect the environment in
Indonesia.
2 Historical literature
Environmental instability is a fundamental and challenging global issue in the current
era. The concern of economic activities at the worldwide level is creating a threatening
situation for environmental sustainability and anthropogenic health. Many scholars have
observed the nexuses of air pollution, mainly CO2 with different variables with different
results and time-span (Adebayo etal., 2023; Ramzan etal., 2023; Zang et al., 2021), but
the environmental issue still needs full concentration for making sustainable policies for
different regions and scenarios. For this reason, we have selected economic growth, the
square of economic growth, energy use, human capital, and the sub-indices of globaliza-
tion and their impact on environmental degradation and its severe impact on human beings.
To follow our objective, we reviewed the literature and made linkages between our con-
cerned variables and carbon emissions.
2.1 EKC and CO2 emissions
In the 1990s, the EKC hypothesis concept was of great importance in exploring the link
between the sustainability of the environment and EKC (Gill etal., 2017). Simon Kuznets
first presented the theory of the EKC hypothesis. He discussed the relationship between
economic growth and income inequality. He documented that as income (per capita)
enhances, the equitableness of income enhances originally and after an equilibrium point
of income (per capita), income equitableness reduces (Kuznets, 1955). Moreover, Gross-
man and Krueger (1991), following the concept of the Environmental Kuznets curve
and found an inverted U-shaped relation between income and emissions. This inverted
U-shaped curve identifies the Environment Kuznets hypothesis.
Many researchers have explored the EKC concept in their studies. For instance, Shah-
baz etal. (2017b) found that global indicators increase environmental sustainability in the
presence of EKC from 1970 to 2010 in the case of China. The same consequences are
sorted out by Rasool etal. (2019) from the time 1980–2010 in the case of Romania and
disclose that the global index enhances the ecological degradation while the growth of the
economic and intensity of energy mitigate environmental pollution Furthermore, EKC has
found in this empirical research. Akadiri etal. (2019), examined the influence of global
on the environment in tourist destination countries, moreover, they discovered that glo-
balization depletes environmental sustainability in the existence of the Environmental
Kuznets curve. Xu etal. (2022), retrieved data from 2001 to 2017 to analyze 19 African
nations. They identified the effect of global and energy intensity on carbon dioxide emis-
sions and found mixed results. Additionally, they found the EKC assumption in the respec-
tive nations, such as Zambia, Cameroon, Congo Republic, Morocco, Tunisia, and Algeria.
Haseeb et al. (2018) explored the relation between energy usage, economic growth and
globalization with emit in panel BRICS realms over 1995–2014. They examined that in the
presence of EKC, globalization has no relation with carbon emission. On the other side,
Destek (2019) investigated the relationship between the overall globalization index, sub-
branches of globalization (Eco global, Soc global and Pol global), and carbon emissions by
using (AMG) approach. They probed that in the existence of EKC, energy consumption,
Y.Rasool et al.
1 3
overall global index, Eco global and social global degrade the environmental sustainability
in the Central and Eastern European realms (CEECs).
2.2 Globalization Indicators and CO2 emissions
Many scholars have explored much literature regarding economic globalization with
the environment and documented feedback of positive, negative, and mixed results. For
instance, Kalaycı and Hayaloğlu (2019) expressed that trade openness as a segment of glo-
balization has no significant association with emissions in NAFTA countries. On the other
hand, Muhammad and Khan (2021) and Amri (2018), in 12 Middle East regions and EU
regimes. They concluded that trade increases carbon emissions, respectively. Bakirtas and
Cetin (2017) used foreign direct investment as a proxy to measure economic globalization.
They confirmed that foreign investment reduces the effect of CO2 emissions in Mexico,
Indonesia, South Korea, Turkey, and Australia, respectively. Likewise, Behera and Dash
(2017) and Solarin and Al-mulali (2018) described that international investment causes to
mitigate environmental quality. Moreover, financial development decreases the influence
of CO2 emissions on the environment (Ali etal., 2016; Park etal., 2018) and Amri (2018)
found that financial development enhances environmental pollution.
Haseeb etal. (2019) examined the link between social globalization (Internet users and
mobile cellular subscriptions) with carbon emissions by applying the dynamic, seemingly
unrelated regression model. They revealed that the usage of the internet and mobiles pro-
vide support for environmental sustainability. Similar findings are also supported by Ozcan
and Apergis (2017). Besides these, Salahuddin etal. (2016) and Park etal. (2018) high-
lighted the negative association between social globalization with carbon emissions. Fur-
thermore, some scholars like Irfan etal. (2023), also observed the dynamic linking between
the tourism industry and environment sustainability with control factors such as energy
consumption and economic output in the case of China. And they found that the tourism
industry enhances carbon mission and destroys environmental quality. Nevertheless, Wang
etal. (2019a, 2019b, 2019c) and Azam et al. (2018) found a positive role of tourism on
environmental quality in the case of OECD states, Malaysia, Singapore, and Thailand,
respectively. Similar findings are documented by Katircioglu etal. (2018). It is worth men-
tioning that this study also explored the relationship between political globalization with
carbon emissions. Political globalization can be measured using the Kyoto protocol proxy
to observe the political agreement’s relations with the environment (Wang et al., 2019a,
2019b, 2019c) argued; that the Kyoto protocol reduces CO2 emissions in 170 Countries.
2.3 Human capital and CO2 emissions
Human capital is the sum of a stock of habits, experience, skill and knowledge, compe-
tencies, and societal talents that contribute to a person’s capability to execute work in a
manner that produces economic value. In this present era, it is challenging to maintain eco-
nomic growth without compromising environmental sustainability, especially for under-
developed countries. CO2 emission growth is connected with human economic goings-on
(Wang, etal., 2019a, 2019b, 2019c). Ali (2017) found that economic growth depends on
human development, which is directly linked to human capital. The trained, well-informed,
expert labor is an input element in the manufacturing process and can be used in the
anthropologic capital context. Several developed nations have shifted their economies from
unskilled and uneducated forces to knowledge-based ones. Therefore, if underdeveloped
Exploring thelinkage betweenglobalization andenvironmental…
1 3
counties also desire to sustain their economic growth, they should shift their economies
from labor-based to knowledge-based.
Li etal. (2022) examined that human capital enhances economic growth and mitigates
Emissions. Khan etal. (2022) disclose that anthropogenic also assists the rise in the usage
of renewable energy due to knowledge, education and public awareness about energy secu-
rity. Human capital, in the beginning, was introduced by Raymond and Smulders (1993),
as the main element of economic growth and disclosed as the side effect of increasing
physical capital. They also evaluated that consciousness about the atmosphere smog by
knowledge, trained and skilled labor enhances the utilization of fleshly capital in manu-
facturing. Dasgupta etal. (1998) revealed that skilled and higher-knowledge labor gener-
ally use advanced and energy-efficient technology, ultimately raising environmental qual-
ity. Hence, anthropological is not only beneficial for persons, but it is also profitable for
humanity (Sianesi & Reenen, 2003). Wang etal. (2019a, 2019b, 2019c), disclose that if
the industry employees are knowledgeable, trained and skilled (human capital), it produces
less pollution. Ali etal. (2022), surveyed fifteen provinces in China to analyze the influenc-
ing factor of industrialized pollution. They revealed that the province with higher anthropo-
genic values, calculated by experiences and skill, is more likely to follow environmentally
friendly rules. Bano etal. (2018) identified the association between human capital, FDI and
CO2 emit. They documented that international investment has a negative relationship with
the environment in those regions of China where anthropogenic is higher. In contrast, for-
eign investment has a posited relationship with air pollution in regions with lower human
capital.
The above studies did not examine the linkages between interested indicators, particu-
larly in Indonesia. Hence our study contributes to existing literature to improve environ-
mental sustainability in the region.
3 Methodological framework
3.1 Data
For this research, we collected data from different web sites, such as economic growth and
energy consumption retrieved from the World Development Indicators (WDI). Human
capital index collected from the web page of www. ggdc. net/ pwt. Economic globalization
(Trade, Foreign Investment, Porto polio Investment, Payment to foreign nationals) Social
globalization (Phone Rate, transfer (per to GDP), international tourism, internet user (per
1000 people), newspaper trading, the MC Donald) and Political globalization (Ambassa-
dor, member of the international institute, participation of Security Council, PBB interna-
tional agreement) retrieve from KOF web page. This data are collected from 1990 to 2020
as the base of availability. (See details in Table1).
3.2 Theoretical framework andmodel specification
This segment shows off the theoretical background to examine the cause of economic,
social, political globalization and carbon dioxide emission interactions in Indonesia.
First, we explore the pairwise connection among the variables, and then we check the
combined causal effects of the variables. There are diverse outcomes, likewise politi-
cal globalization and human capital with carbon emit in the long and short run. Scant
Y.Rasool et al.
1 3
Table 1 Variables indicators and definition
Data of all the parameters sought from the World Development indicator, KOF and PWT10. The data ranges from 1990 to 2020
Variables Symptom Definition
Carbon emission CO2The carbon emissions exclude from conventional energy sources, likewise oil, coal,
gas and other liquid
Gross domestic product GDP Nominal GDP divided by the total population
Gross domestic product2EG2Gross domestic product square = log economic growth multiple log economic growth
Energy use EU It is calculated as energy use per capita kg of oil equivalent
Economic globalization ECO-GLOB Economic globalization KOF
Social globalization SOC-GLOB Social globalization KOF
Political globalization POL-GLOB Political globalization KOF
Human capital index HI Based on schooling years and return to education
Exploring thelinkage betweenglobalization andenvironmental…
1 3
literature on this nexus describes the nexus of these variables. Prior studies in the litera-
ture have mentioned the relation between globalization and its proxies and environmen-
tal degradation by applying different techniques and presenting different insights. How-
ever, their results are unconvincing and subject to further investigations. For instance,
Ahmed etal. (2019) in Malaysia, Wang etal. (2019a, 2019b, 2019c) OECD regions.
According to Shahbaz etal. (2017), the ARDL and Bayer and Hancke methods were
implied and found the relation among coal utilization, GDP2, globalization proxies and
emission in China. They unveiled that coal consumption enhances air pollution, but
globalization proxies reduce pollution. Moreover, these types of globalization and CO2
emissions have a single-directional relation in China. These results, express that, if eco-
nomic, social, and political globalization trend increase and decrease, it will not affect
the environmental sustainability in China.
Destek (2019) employed the second-generation panel data method to explore the rela-
tionship between globalization indexes and pollution. He discloses that overall globaliza-
tion, particularly economic and social globalization, are very dangerous for environmental
sustainability in Central and Eastern countries. However, political influence enhances the
environment image and reduces air pollution. Ahmed etal. (2019) applied the ADRL bound
test and Bayer and Hancke co-integration methodology to unveil globalization’s influence on
the ecological and carbon footprint. They discovered that these proxies have an insignificant
influence on the ecological footprint, but it significantly surges the ecological carbon foot-
print. Khan and Ullah (2019) in Pakistan, using ADRL bound test and modern Johansen co-
integration approaches, taking data from 1975 to 2014 to conclude that three types of globali-
zation enhance atmospheric degradation. On the other though of school, Sasana etal. (2018)
described that economic globalization increases the sustainability of the environment, while
POL-Global and SOC-Global influence degrades the environmental image. In the end, Axel
(2006) first time introduced ECO-Global, POL-Global and SOC-Global in his study.
Using these shards of information from the above literature, the expected indications
rely on the given theoretical perspective, which includes (EG, EG2, EU, ECOGlobal, POL-
Global, SOCGlobal and HI on CO2 emissions). EG-EG2 affects CO2 emissions; examining
economic growth (EG) study has discovered that a rise in EG significantly boosts demand
for energy consumption. Thus, increasing demand expands energy consumption, growing
CO2 emissions (Wang etal., 2020). As a result, economic growth destroys Indonesia’s
environmental sustainability. Hence, economic growth could encourage CO2 emissions;
𝛽
1=
𝛼CO
2,it
𝛼EG
it
>
0
. But Akadiri etal. (2019) express that economic growth square mitigates
CO2 emit and enhances environmental sustainability by shifting the old technology to
advance technology. Moreover, minimize the dependency on non-renewable energy use.
Therefore, the influence on CO2 emissions could be negative,
𝛽
2=
𝛼CO
2,it
𝛼EG2
it
<
0
. Further-
more, EU impact on CO2 emissions; This is due to the fact that fuels are utilized in trans-
portation, processing, exploration, manufacturing, and storage, all of which lead to the gen-
eration and consumption of energy-related pollutants (Wang etal., 2020), therefore, the
influence on CO2 emissions could be positive
𝛽
3=
𝛼CO
2,it
𝛼EUit
>
0
. Economic globalization
increased carbon emissions by industrialization, importantly in emerging nations such as
Indonesia seeking to attract foreign investment and increase their export markets. Industrial
procedures and energy use are major foundations of carbon emissions, importantly if they
are based on fossil fuels. As a consequence, upward industrialization excludes higher levels
of carbon emit. In this study, we anticipate an adverse connection between economic glo-
balization and CO2 emissions in Indonesia,
𝛽
4=
𝛼CO
2,it
𝛼EcoGlobal
it
>
0.
Political globalization led
Y.Rasool et al.
1 3
to greater economic integration and upsurge trade, which enhance CO2 emission due to
greater transportation and energy use. Therefore, the influence of PolGlobal could be nega-
tive;
𝛽
5=
𝛼CO
2,it
𝛼PolGlobal
it
>
0.
Additionally, social globalization mitigates CO2 emissions in
Indonesia by different channels, such as, information, education, and technology, leading to
more well-organized and supportable practices. Social globalization promotes the sharing
of best practices and the adoption of international environmental standards. Moreover,
social globalization increased political pressure and involvement for environmental protec-
tion. Therefore, the influence on CO2 emissions could be negative;
𝛽
6=
𝛼CO
2,it
𝛼SocGlobal
it
>
0.
Finally, Human index raise awareness about environmental issues and promote sustainable
practices such as Educated individuals are more likely to understand the importance of sus-
tainable development and to take actions to reduce their environmental impact. Addition-
ally, education can help to promote research and development of new technologies and
practices that can mitigates CO2 emissions in the region; Hence, the expected
sign;
𝛽
7=
𝛼CO
2,it
𝛼Hiit
<
0
.
3.3 Econometrics model
To examine the econometrically effect of CO2 emission, we followed the model suggested
by Rasool etal. (2019) and rewritten Eq. (1) as:
where i, Indonesia, t, 1990–2020.
LogCO2
is denoting the carbon dioxide emissions,
LogEG
is the (economic growth),
LogEG2
is the (economic growth square), and, energy
conumsption,
LogECOGLOB
is the (economic globalization),
LogSOCGLOB
is the
(social globalization),
LogPOLGLOB
is (political globalization), and
LogHI
is the human
capital index,
𝜇t
is the error correction term.
In this research work, we applied (ARDL) method for the long-run and short-run
investigations. This method was developed (Pesaran etal., 2001) for the study of the co-
integration between different connections of the variables. Numerous researchers have
applied different methods for co-integration analysis from the previous research literature.
For example, Engle and Granger (1987), Phillips and Hansen (1990), Johansen and Julius
(1990), and Stock and Watson (1993). Though these approaches have a particular require-
ment. For example, these methodologies demand that all variables be significant in the
same order. If all the variables are insignificant at the same level, then we cannot apply
the approaches mentioned above. For this reason, we prefer to select the most appropri-
ate technique of ADRL for this study by comparing the upper mentioned methodologies.
This method has many benefits; also, it is relevant if the variables have co-integration at l
(0) level, at l (1) different and or both levels (Im & Pesaran, 1997). Although the ARDL
is not valid in this condition when the variables are stationary at level (2) (Charfeddine
etal., 2018), this method is also worth full for the small sample size (Ahmed etal., 2019).
This approach also offers the unprejudiced outcome of long- and short-term relationships
(Wang etal., 2019a). Furthermore, the ARDL methodology is free from autocorrelation,
and suitable lag length collection removes the endogeneity problem (Rasool etal., 2019).
The ARDL is defined by the below Eqs.(2, 3, and 4);
(1)
LogCO
2it
=
𝛼0
+
𝛽1
(LogEG
it
)+
𝛽2
(LogEG2
it
)+
𝛽3
(LogEU
it
)+
𝛽
4(LogEcoGlobal
it
)
+𝛽5(LogPolGlobalit)+𝛽6(LogSocGlobalit )+𝛽7(LogHIit )+𝜇it
Exploring thelinkage betweenglobalization andenvironmental…
1 3
where in Eqs. (2, 3, and 4),
Δ
is the first difference operator. The null hypothesis of no
co-integration (H0:
𝛾1
=
𝛾2
=
𝛾3
=
𝛾4
=
𝛾5
=
𝛾6
= 0) is checked against the alternative hypothesis
(H0:
H0∶𝛾1≠𝛾2≠𝛾3≠𝛾4≠𝛾5≠𝛾6≠0
.
We discovered the bound F-value to check the co-integration, as that if the targeted
value is beyond the upper bound, it confirms the co-integration but if it below the lower
critical bound value, it confirms the no co-integration among the variables. If co-integra-
tion is confirmed, we can run further techniques for the long and short-run connection. To
investigate the robustness and steady of the data, we check the autocorrelation, heterosce-
dasticity, and stability in the model using numerous diagnostic methods.
4 Results anddiscussion
4.1 Descriptive statistics summary
Table2 revealed the summary of the descriptive statistics after converting the variables’
values from absolute digits to logarithms from 1990 to 2020. The summary of the statistics
comprises information about standard deviation, maximum, mean, median, and minimum.
The Carbon emission per capita is from 0.42 to 0.89. The economic globalization range is
3.99, 3.59, and 4.25; social globalization ranges from 3.66, 3.69, and 3.99; political glo-
balization is 4.36, 4.364, and 4.47.
Furthermore, box plots depict the statistical parameters of key, explanatory, and control
variables of interest (see Fig.1), where 25, 50, and 75 percent are represented across all
(2)
Δ
LogCO2=c+
P
∑
i=1
𝜕1iΔLogCO2i−1+
P
∑
i=0
𝜕2iΔLogEGt−i+
P
∑
i=1
𝜕3iΔLogEG2
t−i
+
P
∑
i=1
𝜕4iΔLogEUt−i+
P
∑
i=1
𝜕5iΔLogECOGLOBt−i+
P
∑
i=1
𝜕6iΔLogHIt−i
+𝛾1CO2t−1+𝛾2EGt−1+𝛾3EG
2
+𝛾4EUt−1+𝛾5ECOGLOBt−1+𝛾6HIt−1+𝜇t
(3)
Δ
LogCO2=c+
P
∑
i=1
𝜕1iΔLogCO2i−1+
P
∑
i=0
𝜕2iΔLogEGt−i+
P
∑
i=1
𝜕3iΔLogEG2
t−
i
+
P
∑
i=1
𝜕4iΔLogEUt−i+
P
∑
i=1
𝜕5iΔLogSocGlobt−i+
P
∑
i=1
𝜕6iΔLogHIt−i
+𝛾1CO
2t−1
+𝛾2 EG
t−1
+𝛾3EG2+𝛾4EU
t−1
+𝛾5SocGlob
t−1
+𝛾6HI
t−1
+𝜇
t
(4)
Δ
LogCO2 =c+
P
∑
i=1
𝜕1iΔLogCO2i−1+
P
∑
i=0
𝜕2iΔLogEGt−i+
P
∑
i=1
𝜕3iΔLogEG2
t−i
+
P
∑
i=1
𝜕4iΔLogEUt−i+
P
∑
i=1
𝜕5iΔLogPOLGLOBt−i+
P
∑
i=1
𝜕6iΔLogHIt−i
+𝛾
1CO2t−1
−
1
+𝛾
2EGt−1
+𝛾
3EG
2+𝛾
4EUt−1
+𝛾
5POLGLOBt−1
+𝛾
6HIt−1
+𝜇
t
Y.Rasool et al.
1 3
graphs. The circle represents the median, while the squares represent the mean values. The
top and bottom lines reflect the maximum and minimum values, respectively.
We also examined the coefficient correlation among the selected variables; the conse-
quences are shown in (Fig.2); the closer the data point is to a straight line, the more indi-
cators show positive correlations while the data point progresses Furthermore, Pearson’s
coefficient of correlation (r) quantifies the linear relationship between parameters. The cor-
relate coefficient values range from –1 to + 1. Positive correlation coefficients suggest one
parameter increases or decreases with another.
4.2 Unit root test
Before applying the ARDL approach, it is mandatory to identify that all variables are inte-
grated at level (0), at first different (1). For this aim, we used many units of root tests. The
unit root test is essential to research the time series and panel data. Because the unit root
test provides information about data that it is relevant for analysis or not. For this reason,
we implied Augmented Ducky Fuller and Philips Pearson unit root tests. At first, findings
obtained using unit root tests for each data series are analyzed and mentioned in Table 3,
unveiling that all variables are integrated at first different I (1) but not at level l (0). We
found null co-integration at 2nd level l (2); therefore, the null hypothesis of no stationary at
l (0) is accepted but overruled at l (1) for all series.
4.3 Bounds test
After examining the results of the conventional unit root, the ARDL Bounds test was
employed to verify co-integration in variables. The outcomes of the bound test are pre-
sented in Table4, which suggests that the F statistics value is higher limits of critical values
of Narayan (2005) at 1 percent significant level for three models, indicating the rejection
of the null hypothesis. The bounds test results demonstrate the existence of co-integration
among three model variables. The coefficient of lagged residual correction terms are nega-
tive and signs indicating the validity of co-integration among all variables in all (3) models.
The elasticity of coefficients 0.89, 0.83 and 0.53 indicates the quick adjustment to achieve
a prolonged period equilibrium in the first to third models, respectively. We also applied
some diagnostic techniques to verify the sanctity of our models from serial correlation, het-
eroskedasticity, and miss-spiced functional problems. Table4 shows the probability values
for the LM test, wherein none of the models has a value beneath 0.10. Simply the serial
correlation problem does not exist in our models. Additionally, findings from the ARCH
test displayed no heteroskedasticity in the models, and outcomes of the Ramsey RESET
Table 2 Descriptive statistics
LCO2LGDP LGDP2LEU LECO-
GLOB
LSOC-
GLOB
LPOL-
GLOB
LHI
Mean 0.41798 7.76978 60.44944 8.64388 3.99119 3.66597 4.36566 0.79526
Median 0.42217 7.6958 59.22535 8.70358 3.95923 3.69147 4.36475 0.82494
Maximum 0.89451 8.26292 68.27578 9.02433 4.24904 3.9918 4.47261 0.88243
Minimum − 0.22956 7.30508 53.36426 8.06207 3.80744 3.106 4.14301 0.63938
Std. Dev 0.29391 0.28747 4.4896 0.26913 0.11564 0.27649 0.09019 0.07387
Exploring thelinkage betweenglobalization andenvironmental…
1 3
test also endorsed the accuracy of all models. The DW statistics results are presented in
Table5, confirming the freeness of all models from error residual correlation. Additionally,
CUSUM and CUSUMsq tests were performed to confirm the stability of all models. The
figures obtained from CUSUM and CUSUMsq tests disclose the presence of stability in
all models. The Figs.3, 4, and 5 obtained from CUSUM and CUSUMsq tests validate the
obstacle of parameters.
Fig. 1 The normal box plots
Y.Rasool et al.
1 3
4.4 Benchmark estimation
In this study, we employed Bound test for confirmation although the co-integration exists
or not among the parameters. The outcomes of the Bound test show the strong co-move-
ment between the variables. After finding the co-integration among variables, we used the
ARDL benchmark analysis technique. The benchmark outcomes are composed in Table5
and Fig.6, graphical interpretations of the study’s empirical analysis. With the help of the
ARDL technique, we have inspected the long-term and short-term connection between the
Fig. 2 Correlation matrix box plots
Table 3 Units root test
*, **, and *** are used for 1%, 5%, and 10% significance level. Source, Authors estimations
Variable Augmented Dickey–Fuller test statistic Phillips Perron test statistic
At level First different At level First different
t-statistic pro t-statistic pro t-statistic pro t-statistic Pro
LCO2 − 2.894955 0.0542** − 5.606044* 0.0000 − 2.963154 0.0466** − 5.606044* 0.0000
LE − 1.030099 0.7340 − 6.575627* 0.0000 − 1.072631 0.7180 − 6.584412* 0.0000
LEG − 0.981981 0.7513 − 4.804016* 0.0003 − 0.893555 0.7808 − 4.544318* 0.0007
LGDP − 0.806910 0.8070 − 4.841858* 0.0003 0.806910 0.8070 − 4.809656* 0.0003
LGDP2 − 0.353233 0.9080 − 4.863903* 0.0003 − 0.353233 0.9080 − 4.834088* 0.0003
LHCI − 2.145732 0.2286 − 6.223840* 0.0000 − 2.171430 0.2193 − 6.223813* 0.0000
LPG − 2.103103 0.2447 − 5.175939* 0.0002 − 2.960424 0.0469** − 5.442502* 0.0000
LSG − 0.597260 0.8606 − 5.919742* 0.0000 − 0.597260 0.8606 − 5.919841* 0.0000
Exploring thelinkage betweenglobalization andenvironmental…
1 3
Table 4 ARDL bound test
*, **, and *** are used for 1%, 5%, and 10% significance level. Source, Authors estimations
Model under observation Bounds tets Diagnostic statistics tests
F-stat Lags Decision Ramsey test ARCH Test LM test
(LCO/LGDP, LGDP2, LEU, LECOGLOB, LHI) 5.9393* (1,2,2,2,2,0) yes 0.0363
[0.8504]
0.7361
[0.3835]
0.7372
[0.2965]
(LCO/LGDP, LGDP2, LEU, LSOCGLOB, LHI) 8.3432* (1,2,2,2,1,0) yes 0.1128
[0.7396]
0.0508
[0.8173]
0.3117
[0.5999]
(LCO/LGDP, LGDP2, LEU, LPOLGLOB, LHI) 5.6719* (2,2,2,0,0,0) yes 1.5865
[0.2055]
1.5865
[0.2055]
0.9761
[0.2427]
Critical values LCB 1(0) UCB 1(1) Note:* ratify 1% significant. AIC optimum lag length 2 is used to com-
pute the results. For detail about critical values see Narayan (2005)
1% critical values 3.657 5.256
5% critical values 2.734 3.920
10% critical values 2.306 3.535
Y.Rasool et al.
1 3
globalization sub-sectors (economic globalization, social globalization, political globali-
zation) and environment sustainability, other control variables likewise economic growth,
economic square, energy consumption, and human capital index. We found strong positive
Table 5 ARDL model result
*, **, and *** are used for 1%, 5%, and 10% significance level. AIC optimum lag length two is used to
compute results in all models
Variables Model (1) Model (2) Model (3)
Dependent variable CO2 (long-term)
LGDP 6.1445*
[0.0000]
4.4918*
[0.0000]
5.5031*
[0.0004]
LGDP2 − 0.3682*
[0.0000]
− 0.2802*
[0.0000]
− 0.3214*
[0.0007]
LEU 1.0895*
[0.0001]
1.9112*
[0.0000]
1.4147*
[0.0001]
LECO-GLOB 0.2353**
[0.0399]
– –
LSOC-GLOB – − 0.2107*
[0.0034]
–
LPOL-GLOB – – − 0.0528
[0.6816]
LHI − 0.2476
[0.3825]
− 0.2234
[0.3875]
− 0.4024
[0.4006]
C − 46.9548*
[0.0000]
− 43.2142*
[0.0000]
− 45.6319*
[0.0000]
R-square 0.9984 0.9986 0.9982
Adjusted R20.9976 0.9980 0.9974
DW Stat 2.2884 2.1406 2.1946
F-statistics 1148.662
[0.0000]
1480.972
[0.0000]
1352.667
[0.0000]
Jarque–Bera (Normality) 0.09401
[0.9540]
0.6228
[0.7324]
0.26619
[0.8753]
Variables Model (1) Model (2) Model (3)
LGDP 7.4414**
[0.0461]
9.7039*
[0.0076]
12.8090*
[0.0222]
LGDP2 − 0.4648***
[0.0564]
− 0.6553*
[0.0063]
− 0.9341**
[0.0165]
LE 0.3391**
[0.0247]
0.7434*
[0.0000]
− 0.0006
[0.9875]
LECO-GLOB 0.2401*
[0.0024]
– –
LSOC-GLOB – − 0.0473
[0.2614]
–
LPOL-GLOB – – 0.0022
[0.9701]
LHI 0.0006
[0.9497]
− 0.0008
[0.9300]
0.0021
[0.9845]
cointEq(− 1) − 0.8967*
[0.0000]
− 0.8330*
[0.0000]
− 0.5252*
[0.0000]
Exploring thelinkage betweenglobalization andenvironmental…
1 3
and significant co-movement between economic growth and environmental sustainability.
For instance, the 1% increase in economic growth per capita causes the 6.1445%, 4.4918%,
and 5.5031% rises in CO2 emissions stemming from nonrenewable energy as the pri-
mary source of energy production in Indonesia country. Owing to this, economic growth
upsurges energy utilization, which results in the escalation of CO2 emissions, which
unfortunately harms the environment quality. For Indonesia, an upsurge in economic
growth turns into increased energy use and pollution. Indonesia is an emerging country
actively participating in world trade and adding to the Globe’s growth, which unfortunately
upsurges CO2 emit. Further, an expansion in the economic activities for instant purchase,
investment and consumption is likely to increase pollution in the air. Another reasonable
reason behind the surge of CO2 emitted in Indonesia may be related to using non-renew-
able energy appliances in production. Regarding the above outcome, it is our suggestion
-12
-8
-4
0
4
8
12
16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
CUSUM5% Significance
-0.4
0.0
0.4
0.8
1.2
1.6
16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
CUSUM of Squa res5% Significance
Fig. 3 Results of CUSUM residual and CUSUMsq of residual
-12
-8
-4
0
4
8
12
18 19 20 21 22 23 24 25 26 27 28 29 30
CUSUM5% Significance
-0.4
0.0
0.4
0.8
1.2
1.6
18 19 20 21 22 23 24 25 26 27 28 29 30
CUSUM of Squares5% Significance
Fig. 4 Results of CUSUM residual and CUSUMsq of residual
-12
-8
-4
0
4
8
12
17 18 19 20 21 22 23 24 25 26 27 28 29 30
CUSUM5% Significance
-0.4
0.0
0.4
0.8
1.2
1.6
17 18 19 20 21 22 23 24 25 26 27 28 29 30
CUSUM of Squares5% Significance
Fig. 5 Results of CUSUM residual and CUSUMsq of residual
Y.Rasool et al.
1 3
to the government of Indonesia and policymakers to consider this study in their discus-
sion and implement the environment tax on businessmen and industrialists as well as the
transport sector. Moreover, Indonesia’s government should force the industrialist to shift
their outdate technology to green technology because these sectors are the main culprit
excluding environmental pollution by using nonrenewable energy materials. Finally, here
we want to mention that our analysis is consistent with these respected authors (Adebayo
etal., 2023; Wang etal., 2019a, 2019b, 2019c; Ullah etal., 2023). Our dynamic analysis
also displays the correlation between economic growth square and Indonesia’s environ-
ment. The study’s outcome confirms the solid and reliable connection between the vari-
ables. For instance, a 1% increase in economic growth square overcomes −0.3682, 0.2802,
and −0.3214 percent carbon emissions, respectively. This document outcome denotes that
when the income level comes to the high stage, the per capita income starts to enhance
environmental sustainability after this equilibrium point. People started to skip the non-
renewable energy appliance and adopt energy-saving appliances. Furthermore, the industri-
alist and businessmen also remove their outdated and fixed green technology that does not
harm the environment. At the end of this discussion, we want to mention that ours outcome
is consistent with these respected authors (Akadiri etal., 2019; Kalaycı & Hayaloğlu, 2019;
Khan & Ullah, 2019). All these respected researchers investigated the relationship between
the environment Kuznets curve and CO2 emitted in their work in the following states, i.e.,
NAFTA countries and the Middle East and North Africa stats, forty Europen Countries.
The coefficient of economic globalization displays a 1% increase in economic globaliza-
tion resulting in a 0.235% escalation in CO2 emissions. The outcomes indicate that eco-
nomic globalization can affect the sustainability of the environment. This can be described
via well-known phenomenon due to economic globalization, e.g., global free trade,
import & export, international investment, financial development, etc., all harming the
Fig. 6 Graphical interpretations of empirical analysis of the study
Exploring thelinkage betweenglobalization andenvironmental…
1 3
environment. Moreover, global free trade can cause environmental degradation in water,
slow renewable resources, soil and air pollution, exhaustion of conventional energy, and
worldwide atmosphere and climate changes. In addition to these, other elements which can
cause environmental degradation are rapid industrial growth, the surge in agricultural man-
ufacturing, and energy use in transporting raw and finished goods. Our findings affirm the
research of Muhammad and Khan (2021), Kalaycı and Hayaloğlu (2019), Shahbaz et al.
(2017a, 2017b), Xu etal. (2018) and Destek (2019).
The coefficient results express that social globalization decreases CO2 emissions and
improves environmental quality. The 1% increase in social globalization proportionally
improved the environmental quality by 0.0034%. Social globalization is the sum of artistes
at multi-continental distances, mediated by various flows, including people, knowledge and
ideas, merchandise and services. Social globalization enhances the sustainability of the envi-
ronment by sharing information related to the green environment. For example, social globali-
zation can help promote the organization of energy-saving workshops in different countries.
Moreover, the rapid expansion in technology innovation, natural resources and
green finance in recent years has helped to save fossil fuel. The surging trend of online
shopping and advance ICT technology played a vital role in minimizing the use of fos-
sil fuel for energy generation; this happened due to the fast internet services, secure
online transactions, and high downloading speed of shopping for goods (Lanre Ibrahim
etal., 2022). Social globalization’s status can be further optimized by establishing a
safe financial system that contributes to maintaining environmental sustainability. Ulti-
mately, we conclude that the abovementioned factors help develop a sustainable envi-
ronment and limit CO2 emissions. Our findings align with Haseeb etal. (2018, 2019),
Shahbaz etal. (2017a, 2017b) and Shahbaz etal. (2017b).
The result of political globalization indicates that it has no significant relation-
ship with carbon emissions in the long term. The results show that political glo-
balization did not enhance CO2 emissions in Indonesia. Our study is also following
(Destek, 2019) for Central and Eastern European Countries (CEECs) and Shahbaz
et al. (2017b). The outcomes of Table 5 show that the human capital index has no
significant relation with carbon emissions from the Indonesian perspective. This study
describes that human capital does not affect environmental sustainability. In all three
models, human capital displayed an insignificant association with CO2 emissions. The
consequences of energy consumption shows that the over use of energy rise the pollu-
tion in Indonesia; therefore 1 per cent increase in energy consumption enhanced the
carbon dioxide emissions by 1.089%, 1.911%, and 1.415%, respectively, which shows
that increasing demand for energy inflicted deteriorating impact on the sanctity of the
environment of Indonesia. It might be due to taking in to account the old technologies,
which use a high amount of energy and consequently emit more CO2 into the atmos-
phere compared with state-of-the-art technologies. Our findings corroborate the results
obtained by Muhammad and Khan (2021), Shahbaz et al. (2015a), Javid and Sharif
(2016), Dar etal. (2017), Mesagan and Nwachukwu (2018), Shahbaz etal. (2017a) for
Turkey’ 170 countries around the global world, Malaysia, Pakistan, India, Nigeria, and
eight ASEAN countries. Hence, Indonesian stakeholders must design energy preserva-
tion policies to ensure the prudent use of energy in Indonesia that should limit energy
consumption, and increase economic growth while minimizing globalization’s malign
effect on the environment. Renewable energy is a best alternative source that should be
benefited to pursue aforementioned goals.
The findings of the short-run analysis are mentioned in Table5. The coefficient outcome of
per capita (economic growth) positively influences CO2, while the per capita (square of economic
Y.Rasool et al.
1 3
growth) does not upset the ecological health. The results of this document unveil the existence of
Environment Kuznets in Indonesia. The result Table 5 unveils the correlation between energy
usage and CO2 emit, wherein a 1% increase in energy consumption enhanced 0.339%, 0.743%
carbon emission in the air. Therefore, the Indonesian government and policymakers should
deeply contemplate and revisit their present policies. The short-term analysis results show no
significant associations between environmental sustainability and human capital. The results of
the short-term analysis demonstrate that human capital does not influence environmental quality,
indicating that the Indonesian government is not working on the human development index.
Economic globalization displays a positive and significant relation with carbon
emit (CO2) during a short period. The analysis of outcomes of the present study
displayed that 1% per rising in economic globalization added 0.240% per carbon
emissions into the air. The dynamic analysis portraits the insignificant relationship
between social globalization and carbon emit. The short-run analysis results highlight
the Indonesian government’s absence of interest in short-term projects. Therefore,
social globalization has an insignificant effect on environmental sustainability. The
consequence of political globalization has a positive and insignificant link with envi-
ronmental sustainability in portion 3.
4.5 VECM long andshort‑run results
After confirming the existence of a one-way association among the variables. We found the
trend of two-way correlation in Table6; for this purpose, we used VECM. Toda and Phil-
lips (1993) confirmed that when there is a long-term relation between variables, VECM is
an excellent option to check the causality among variables. Vector error correction term
differentiates the causality outcomes from long and short term. We examined the Granger
causation using the Wald test, which determined the figures of all variables for difference
and difference lags. Table6 presents the results of Granger causality.
We found causality results of long-term by error correction term (ECT) and short-
run granger via Wald test (F-statistic). Previously, Rasool etal. (2019) described that the
ECTt-1 value should be significant and negative. If it is not significant and negative, then
the long-run causality does not exist among independent and dependent variables. The
VECM equation of the three models is written below:
Model (01), Economic globalization
Model (02), Social globalization
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
Δlog CO2
Δlog GDP
ΔlGDP2
Δlog EU
Δlog ECOGLOB
Δlog HI
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
=
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
𝜑1
𝜑2
𝜑3
𝜑4
𝜑5
𝜑
6
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
+
p
�
𝜎=1
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
𝜑11𝜎𝜑12𝜎𝜑13𝜎𝜑14𝜎𝜑15𝜎𝜑16𝜎
𝜑21𝜎𝜑22𝜎𝜑23𝜎𝜑24𝜎𝜑25𝜎𝜑26𝜎
𝜑31𝜎𝜑32𝜎𝜑33𝜎𝜑34𝜎𝜑35𝜎𝜑36𝜎
𝜑41𝜎𝜑42𝜎𝜑43𝜎𝜑44𝜎𝜑45𝜎𝜑46𝜎
𝜑51𝜎𝜑52𝜎𝜑53𝜎𝜑54𝜎𝜑55𝜎𝜑56𝜎
𝜑
61𝜎
𝜑
62𝜎
𝜑
63𝜎
𝜑
64𝜎
𝜑
65𝜎
𝜑
66𝜎
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
×
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
ΔLogCO2it−𝜎
ΔLogGDPit−𝜎
ΔLogGDP2
it−𝜎
ΔLogEUit−𝜎
ΔLogECOGLOBit−𝜎
ΔLogHI
it
−
𝜎
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
+
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
𝛾1
𝛾2
𝛾3
𝛾4
𝛾5
𝛾
6
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
ECTit−1
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
𝜇1it
𝜇2it
𝜇3it
𝜇4it
𝜇5it
𝜇
6it
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
Exploring thelinkage betweenglobalization andenvironmental…
1 3
Table 6 VECM model result
LCO2LGDP LGDP2LEU LECOGLOB LHI ECT (− 1)
Model 1 Economic globalization
LCO2– 3.7425* [0.0000] − 3.7203* [0.0002] 0.1041 [0.9170] − 1.3865 [0.1656] − 0.6310 [0.5280] − 0.2369** [0.0218]
LGDP − 0.7469 [0.4602] – 0.2725 [0.7852] 0.8888 [0.3741] − 0.5786 [0.5628] 0.8264 [0.4086] − 0.6772 [0.3348]
LGDP2 − 0.7363 [0.4615] − 0.2913 [0.7708] – 0.8737 [0.3822] − 0.5949 [0.5519] − 1.3429 [0.1793] 0.52638 [0.3951]
LEU 0.0468 [0.9626] 1.2859 [0.1985] − 1.2904 [0.1969] – − 1.1357 [0.2561] 0.4399 [0.6600] − 0.4761* [0.0023]
LECO-GLOB 1.7209*** [0.0853] − 2.0107** [0.0444] 2.0461* [0.0407] − 0.2507 [0.8020] – − 0.1620 [0.8713] − 0.1026 [0.1207]
LHI 0.8194 [0.4125] 1.6804*** [0.0929] − 1.6505 [0.0988] 1.9913** [0.0464] 0.2397 [0.8105] – − 0.0023 [0.3586]
Model 2 Social globalization
LCO2– 1.4188
(0.1559)
− 1.4518
(0.1465)
− 0.9437
(0.3453)
− 0.1030
(0.9180)
0.4910
(0.6234)
− 0.37821** [0.0312]
LGDP − 0.3639
(0.7159)
– − 0.2145
(0.8302)
− 0.3424
(0.7321)
0.0192
(0.9847)
− 0.3001
(0.7641)
0.2244 [0.2463]
LGDP2 − 0.3870
(0.6987)
0.0336
(0.9732)
– − 0.2883
(0.7731)
0.0405
(0.9677)
− 0.3454
(0.7298)
− 0.2165 [0.2689]
LEU − 0.4807
(0.6307)
1.5845
(0.1131)
− 1.5552
(0.1199)
– − 0.3788
(0.7048)
− 0.6023
(0.5470)
− 0.5945*** [0.0965]
LSOC-GLOB 1.7715****
(0.0765)
− 1.7770***
(0.0756)
1.7563***
(0.0790)
− 0.7479
(0.4545)
– 1.5127
(0.1303)
− 0.16244 [0.1910]
LHI 0.1780
(0.8580)
3.3361*
(0.0008)
− 3.2694*
(0.0011)
0.5156
(0.6061)
− 0.3898
(0.6967)
– 0.0077* [0.0071]
Model 3 Political globalization
LCO2– 1.9108**
(0.0560)
− 1.8937**
(0.0583)
0.6641
(0.5067)
− 0.5834
(0.5596)
− 0.8748
(0.3817)
− 0.4411* [0.0003]
LGDP − 0.4098
(0.6819)
– − 0.1815
(0.8560)
0.4769
(0.6334)
0.0685
(0.9454)
− 1.0291
(0.3034)
− 0.07390 [0.5004]
LGDP2 − 0.4398
(0.6601)
0.0829
(0.9339)
– 0.5129
(0.6080)
0.0688
(0.9451)
− 1.0525
(0.2925)
0.05073 [0.5495]
LEC − 0.3608
(0.7182)
1.1169
(0.2640)
− 1.1123
(0.2660)
– 0.0035
(0.9972)
0.2050
(0.8375)
− 0.4577* [0.0014]
Y.Rasool et al.
1 3
*, **, and *** are used for 1%, 5%, and 10% significance level. Source, Authors estimations. ECT (− 1) indicating long-run causality
Table 6 (continued)
LCO2LGDP LGDP2LEU LECOGLOB LHI ECT (− 1)
LPOL-GLOB 0.9034
(0.3663)
− 3.3307*
(0.0009)
3.3116*
(0.0009)
− 1.5979
(0.1100)
– 0.3815
(0.7028)
− 0.2317** [0.0000]
LHI 0.2247
(0.8222)
3.3949*
(0.0007)
− 3.3622*
(0.0008)
− 1.6404
(0.1009)
− 1.9105**
(0.0561)
– − 0.0445 [0.3099]
Exploring thelinkage betweenglobalization andenvironmental…
1 3
Model (03), Political globalization
The ECT (−1) represents the long-run causality results. Wang etal. (2019) highlighted
that long-run causality exists among variables if the error correction term shows a negative
sign. Similarly, the Wald test is applied to calculate the F-value useful to determine the
short-term causality among variables. Subsequent results are revealed in Table6.
Model (1): The consequences indicate the presence of a two-way connection between
carbon emission and energy use. We discovered the unidirectional causality among the
human development index and economic, social and political globalization regarding CO2
emit. Per capita (economic growth) granger caused CO2 to emit. These outcomes further
support the presence of the EKC in Indonesia (Shahbaz etal., 2017b). Energy use enhances
per capita (economic growth) concerning the causality perspective.
The causality from energy use and globalization (KFOI) to CO2 emissions demonstrates
the enhanced ECO-Global, SOC-Global, and POL-Global, which then aggrandized the
carbon dioxide emission due to the increased energy consumption.
Model (2): The results also express the feedback relationship among energy use, CO2
emissions and HI (human capital index). However, per capita (economic growth) and car-
bon emissions have a one-side causality relationship. CO2 emissions and per capita (eco-
nomic growth square) has a unit-direction cause connection. Globalization indexes have a
unidirectional granger causality connection with emit.
Model (3): The results suggest the presence of a feedback hypothesis between carbon
dioxide and energy use and political globalization and CO2. The one-way causality found
running from ECO Global, social globalization and growth, economic square to carbon
emission.
Model (1): In short-term causality results, per capita (economic growth) granger trig-
gered the CO2 emissions, but inversely CO2 did not promote per capita (economic growth).
Similarly, economic growth square stimulated carbon dioxide emissions. The one-way
causality runs from economic globalization to economic growth and carbon dioxide emis-
sions. The human capital index granger also aggrandized per capita (economic growth) and
energy use. These consequences indicate that if the activity of human capital is enhanced,
it increases economic growth and energy utilization.
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
ΔlogCO2
ΔlogGDP
ΔlogGDP2
ΔlogEU
ΔlogSOCGLOB
ΔlogHI
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
=
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
𝜑1
𝜑2
𝜑3
𝜑4
𝜑5
𝜑
6
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
+
p
�
𝜎=1
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
𝜑11𝜎𝜑12𝜎𝜑13𝜎𝜑14𝜎𝜑15𝜎𝜑16𝜎
𝜑31𝜎𝜑32𝜎𝜑33𝜎𝜑34𝜎𝜑35𝜎𝜑36𝜎
𝜑41𝜎𝜑42𝜎𝜑43𝜎𝜑44𝜎𝜑45𝜎𝜑46𝜎
𝜑51𝜎𝜑52𝜎𝜑53𝜎𝜑54𝜎𝜑55𝜎𝜑56𝜎
𝜑61𝜎𝜑62𝜎𝜑63𝜎𝜑64𝜎𝜑65𝜎𝜑66𝜎
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
×
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
ΔLogCO2it−𝜎
ΔLogGDPit−𝜎
ΔLogGDP2
it−𝜎
ΔLogEUit−𝜎
ΔLogSOCGLOBit−𝜎
ΔLogHI
it
−𝜎
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
+
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
𝛾1
𝛾2
𝛾3
𝛾4
𝛾5
𝛾
6
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
ECTit−1
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
𝜇1it
𝜇2it
𝜇3it
𝜇4it
𝜇5it
𝜇
6it
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
Δlog CO2
Δlog EG
Δlog EG2
Δlog EU
Δlog POLGLOB
Δlog HI
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
=
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
𝜑1
𝜑2
𝜑3
𝜑4
𝜑5
𝜑6
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
+
p
�
𝜎=1
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
𝜑11𝜎𝜑12𝜎𝜑13𝜎𝜑14𝜎𝜑15𝜎𝜑16𝜎
𝜑31𝜎𝜑32𝜎𝜑33𝜎𝜑34𝜎𝜑35𝜎𝜑36𝜎
𝜑41𝜎𝜑42𝜎𝜑43𝜎𝜑44𝜎𝜑45𝜎𝜑46𝜎
𝜑51𝜎𝜑52𝜎𝜑53𝜎𝜑54𝜎𝜑55𝜎𝜑56𝜎
𝜑61𝜎𝜑62𝜎𝜑63𝜎𝜑64𝜎𝜑65𝜎𝜑66𝜎
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
×
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
ΔLogCO2it−𝜎
ΔLogEGit−𝜎
ΔLogEG2
it−𝜎
ΔLogEUit−𝜎
ΔLogPOLGLOBit−𝜎
ΔLogHIit−𝜎
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
+
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
𝛾1
𝛾2
𝛾3
𝛾4
𝛾5
𝛾6
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
ECTit−1
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
𝜇1it
𝜇2it
𝜇3it
𝜇4it
𝜇5it
𝜇6it
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
Y.Rasool et al.
1 3
Model (2): The result confirms the presence of unidirectional causality association
among social globalization, CO2 emit and per capita (economic growth). Social globaliza-
tion influences the rise in economic growth and CO2 emit in Indonesia. Human capital also
promotes economic growth.
Model (3): Our result explains the one-way causality relationship between economic
growth per capita and CO2 emit. Economic growth and carbon dioxide emissions Granger
cause each other. Additionally, Political globalization granger cause economic growth. And
Human capital, granger cause per capita (economic growth) and political globalization. At
the same time, economic growth increases the living standards of the human being. Moreo-
ver, political globalization influences anthropogenic living style, education standards and
per capita income.
5 Conclusion andpolicy implication
This study used co-integration and vector error correction model (VECM) to probe the
long and short-run associations and adopted the ARDL approach for benchmark estima-
tion between carbon emissions, economic growth, the square of economic growth, human
capital index, energy consumption, economic globalization, social globalization, and polit-
ical globalization using data on Indonesia from the period 1990 to 2020. This work aimed
to evaluate the effect of different globalization index proxies and we analyzed the long
and short-run influence of per capita (economic growth), per capita (square of economic
growth), human capital index and energy use on CO2 emissions in the context of Indonesia.
The study outcomes revealed that the bounds test confirmed that the carbon emissions and
independent variables have co-integration in the long term. Furthermore, due to globaliza-
tion, economic growth, energy consumption, and human capital, regarding the benchmark
estimation, the ARDL approach showed that economic globalization and economic growth
and energy consumption increases environmental degradation and decreases environmen-
tal quality in Indonesia. In contrast, the influence of social globalization was recorded as
significantly negative on CO2 emissions, and political globalization’s and human capital
impacts were negative but insignificant on CO2 emissions to mitigate Indonesia’s environ-
mental health.
5.1 Policy implication
This research reflects the perpetual kinship of economic globalization, per capita (eco-
nomic growth) and energy utilization with the environment. Meanwhile, for the betterment
of the environment, we recommend some policies for the scenario of the Indonesian econ-
omy, which might be useful to create a functional and healthy environment for anthropo-
genic. Our results suggest that the government of Indonesia should reconsider and mini-
mize the harmful influence of economic globalization on their atmosphere. They should
devise stringent atmosphere protection rules forcing domestic and international investors to
form environment-friendly manufacturing structures.
In particular, Indonesia’s stakeholders should encourage export industries to imple-
ment efficient and renewable energy consumption technologies. Even though social glo-
balization impounded a significant and robust influence on the environmental sustain-
ability of Indonesia but still, the Indonesian government should announce more projects
via print and electronic media to propagate awareness about environment protection. The
Exploring thelinkage betweenglobalization andenvironmental…
1 3
Indonesian government should encourage filmmakers and drama-makers to include public
service messages regarding environmental protection and its health benefits at the start of
the movie/drama. In addition, the Indonesian government should motivate tourist firms and
hotel owners to use renewable energy or mixed energy rather than fossil fuel energy for a
healthy environment. Although political globalization impounded insignificant to affect the
environment, the Indonesian government should sign international environment protection
agreements and invite international experts to reduce environmental pollution.
In Indonesia, the human capital index does not affect environmental sustainabil-
ity; therefore, the Indonesian government should focus on developing human awareness.
Because educated and knowledgeable labor can prevent extra energy loss during manu-
facturing processes. Moreover, informative and train labor know the worth of a healthy
environment. To that end, capacity-building programs for labor and management should be
a priority for Indonesian institutes. The Indonesian government should open a state-of-the-
art technical and social institutes equipped with a modern syllabus for human development.
Funding Authors received no funding.
Data availability The dataset used and analyzed during the current study are available from the correspond-
ing author on reasonable request.
Declarations
Conflict of interest The authors reported no potential conflict of interest with respect to the research, author-
ship, and/or publication of this article.
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