Asymmetric effect of financial development and energy consumption
on environmental degradation in South Asia? New evidence from
non-linear ARDL analysisORIGINAL ARTICLE
Asymmetric effect of financial development and energy
consumption on environmental degradation in South Asia?
New evidence from nonlinear ARDL analysis
Md. Golam Kibria1 · Ismay Jahan1 ·
Jannatul Mawa1
Received: 4 September 2020 / Accepted: 25 February 2021 / Published
online: 31 March 2021 © The Author(s), under exclusive licence to
Springer Nature Switzerland AG part of Springer Nature 2021
Abstract This paper looks at the causes of environmental
degradation by regarding the asym- metric effect of financial
development and energy consumption in the presence of urbanization
and economic growth for South Asia. Using a yearly dataset from
1974 to 2014, this study employs the non-linear autoregressive
distributive lag (NARDL) approach to investigate the asymmetry that
emerges from positive or negative shocks of financial development
and energy consumption. The results of NARDL model assert that the
shocks in energy consumption, both positive and negative, sig-
nificantly contribute to rise the environmental degradation in the
long run and also cut down the density of CO
2 emissions in the short run. In contrast, only a negative
shock in financial development has an adverse and significant
impact on CO 2 emis-
sions in the long run. Besides, the results of the ARDL model
indicate that finan- cial development declines environmental
degradation, while energy consumption evolves the CO
2 emissions in the long run. This paper suggests that
policymakers
may strive to attain high economic success using environmental
favorable energy consumption and financial development.
Keywords Financial development · Energy consumption ·
Urbanization · Environmental degradation · South
Asia
JEL Classification F36 · Q43 · O18 · Q53
* Md. Golam Kibria
[email protected];
[email protected]
Ismay Jahan
[email protected]
Jannatul Mawa
[email protected]
1 Department of Economics, Noakhali Science
and Technology University, Noakhali 3814,
Bangladesh
Introduction
Environment helpful economic progress have increased in place of
only focus- ing on growth since the commencement of the industrial
era (Teodorescu 2012; Muhyidin et al. 2015; Manzoor
et al. 2018). The concept of sustainable develop- ment
receives more attention from both developed and developing
countries due to environmental degradation such as global warming
and climate change. How- ever, Uchiyama (2016) emphasized that
there is a symphony among scholars that different economic
approaches tempt environmental damages. Consequently, an ultimate
discussion is about how to mitigate the effect of CO2 emissions
without disturbing the economic activities. Collins and Zheng
(2015) argued that to detect a quick solution for CO2 emissions is
a difficult task. In the recent Paris agree- ment (12 December
2015), all countries stand into a universal motive to fight global
warming and climate change. The prime aim of this agreement is to
retain the global temperature increase of 2 °C till
2100.
Additionally, the objective is to enhance the capability of all
nations to con- front the effect of environmental degradations.
Therefore, 20 nations including the United States, the United
Kingdom, China, Australia, and India, were agreed to improve global
assistance to overcome the threat of CO2 emissions. But a ques-
tion emerges that how the developing economies make this hypothesis
of agree- ment true. While most of the economy has achieved
remarkable economic growth during the last decade, CO2 emissions
also increase through multiple effects. Therefore, it is crucial to
perceive how to reduce CO2 emissions by continuing the growth
trend. Frankel and Romer (1999) described that we cannot avoid
finan- cial development, while the study deals with increasing CO2
emissions, since it enhances a country’s national income that
ultimately raises CO2 emissions. Moreover, Sadorsky (2010) revealed
that augmentative and proficient financial institutions appear with
compatible consumer lending approaches, increasing the purchasing
power of consumers such as houses, refrigerators, automobiles, etc.
which produces more CO2 emissions. To clarify this solution for
South Asia, this current study examines the asymmetric impact of
financial development and energy consumption on CO2 emissions
during 1974–2014 in the presence of eco- nomic growth and
urbanization.
In recent years, several theoretical and empirical studies for
different countries have expressed the conjunction between
financial development and CO2 emis- sions. Academic scholars
predominantly focus on this connection during the international
financial crisis of 2007–2008. For instance: Ayeche et al.
(2016) for European Countries; Tamazian and Rao (2010) for
transitional economies; Hao et al. (2016) for China; Ozturk
and Acaravci (2013) for Turkey; Yuxiang and Chen (2011) for China;
Lee, Chen, and Cho (2015) for OECD economies; Mugableh (2015) for
Jordan; Farhani and Ozturk (2015) for Tunisia; Tamazian et al.
(2009) for BRIC economies; Shahbaz et al. (2013) for Malaysian
economy; Boutabba (2014) for Indian economy; Charfeddine and
Khediri (2016) for UAE; Dar and Asif (2018) for Turkey; Jalil and
Feridun (2011) for China; Sehrawat et al. (2015) for India;
Zhang (2011) for China; Dar and Asif (2017) for India;
SN Bus Econ (2021) 1:56 Page 3 of 18 56
Siddique (2017) for Pakistan; Al-Muali et al. (2015) for 129
economies; Abbasi and Riaz (2016) for emerging countries; Dogan and
Seker (2016) for top renew- able energy economies; Godli et
al. (2020) for Pakistan; Ahmad et al. (2018) for China; and
Godli et al. (2020) for Turkey. In the theoretical work,
Yuxiang and Chen (2011) explained that financial development has
four different effects on environmental performance such as
capitalization effect, technology effect, income effect, and
finally, the regulation effect. Numerous empirical studies reveal
the mixed impact of financial development on CO2 emissions.
Addressing the positive impact of financial development, Ma and
Stern (2008), Cole, Elli- ott, and Shimamoto (2005), Lundgren
(2003), and Yuxiang and Chen (2011) con- cluded that not only
financial development accelerates the economic condition
ameliorating excellent manufacturing tools but also reduces
environmental dam- ages by declining pollution and loss in
production.
Moreover, as mentioned by Lundgren (2003), financial development
accounts as an investment effect for the economy by which it
produces modern production equipment and update technology to
alleviate environmental degradation. Also, financial development
imposes several restrictions on product strategy and man- ages
attractive funds for the company to be benefitted economically plus
alleviat- ing environmental degradation in production (Cole
et al. 2005). In addition, Ma and Stern (2008) identify
financial development as a technological benefit for both the
economy and environment.1 Conversely, several scholars find an
inconsistent rela- tionship between financial development and the
environment. In general, financial development degrades
environmental quality through producing more CO2 emis- sions
(Ayeche et al. 2016; Tamaziana and Rao 2010; Ozturk and
Acaravci 2013; Lee et al. 2015; Mugableh 2015; Farhani and
Ozturk 2015; Basarir and Cakir 2015). As noted by Cole et al.
(2005), financial development arises questionable signals for
sustainable development by introducing new heavy industries.
Additionally, Ozturk and Acaravci (2013) stated that there is no
long-term significant impact of finan- cial development on CO2
emissions for Turkey. Therefore, the relationship between financial
development and CO2 emissions is ambiguous.
To the best of our know-how, the asymmetric combined association
between financial development, energy consumption, and CO2
emissions is not explored for South Asia. Our paper contributes to
the relevant body of work in the field by esti- mating a non-linear
autoregressive distributed lag (NARDL) model to examine the impacts
of the shocks, positive and negative, in financial development and
energy consumption with the existence of urbanization and economic
growth. Moreover, this study analyses the linear ARDL approach to
explore the symmetric scenario among the variables and to compare
with the non-linear model.
The rest of the paper is arranged as follows: “Literature review
and hypothesis” reports the literature review and hypothesis. “Data
and methodology” discusses the
1 As mentioned by Brännlund et al. (2007), the effects of
technology are suspicious for the environment, because it can
thrive the production of the companies which ultimately increases
the waste and pollution in the environment.
SN Bus Econ (2021) 1:5656 Page 4 of 18
data and methodology of the study. The results and discussion are
in “Methodol- ogy”, while Sect. 5 concludes the paper.
Literature review and hypothesis
In general, previous researches use several control variables to
examine the relation- ship between financial development and
environmental quality. Table 1 reports the causality between
financial development–CO2 emissions.
On the contrary, there are several findings on the relationship
between energy consumption and environmental quality. For instance:
Siwar et al. (2009) for Malay- sia; Akbostanci et al.
(2009) for Turkey; Akin (2014) for 85 countries; Sharif et al.
(2020a, b) for Turkey; Zafar et al. (2020) for OECD
countries; and Sharif et al. (2020a, b) for top-10 polluted
countries. Munir and Riaz (2019) showed that there is an asymmetric
association between electricity and coal consumption and CO2 emis-
sions in the long run for South Asian economies. Following the
previous studies, for instance: Ahmad et al. (2018), Lahiani
(2019), Dar and Asif (2017), Mohiuddin et al. (2016), Rayhan
et al. (2018), Islam et al. (2017), Sarkodie (2018),
Munir and Riaz (2019), Sarkodie and Strezov (2019), Destek and
Sarkodie (2019) and Bekun et al. (2019a, b), we see that
economic growth and urbanization also affect the envi- ronmental
performance besides financial development and energy
consumption.
Numerous studies have investigated the relationship between
economic growth and environmental performance. Apart from ambiguous
findings, a great num- ber of studies have explored an inverted
U-shaped connection between economic growth and environmental
degradation. This hypothesis of the U-shaped relation- ship is
known as ‘Environmental Kuznets Curve (EKC)’ which is first
theoretically introduced by Grossman and Krueger (1991) and
empirically heralded by Shafik and Bandyopadhyay (1992) and Shafik
(1994). Thereafter, several scholars of different
Table 1 Literature review on financial development–CO 2 emissions
nexus
FD financial development, H-JTC Hatemi-J threshold cointegration,
DCPS domestic credit to private sector, ESB endogenous structural
breaks, count. country, DOLS dynamic ordinary least squares, GCT
granger causality test, FDI foreign direct investment, GMM
generalized method of moments, ARDL autoregressive distributed lag
model, VECM vector error correction model, VDA variance
decomposition analysis, TI total investment, RDCPS real domestic
credit to private sector, FD↑CO
2 financial develop-
2 financial development negatively affects CO
2 emissions
Author(s) Country Methodology Proxy for FD Impact
Dar and Asif (2017) India ARDL and H-JTC DCPS FD ↑ CO 2
Gokmenoglu et al. (2015) Turkey ESB cointegration DCPS FD ↓ CO
2
Al-Muali et al. (2015) 129 count DOLS and GCT DCPS FD ↓ CO
2
Bello and Abimbola (2010) Nigeria Linear regression FDI FD ↓ CO
2
Boutabba (2014) India ARDL, GCT DCPS FD ↑ CO 2
Shahbaz et al. (2013) Malaysia ARDL bounds test, VECM RDCPS FD
↓ CO 2
Zhang (2011) China GCT, JCT, VECM, VDA FDI FD ↑ CO 2
Komal and Abbas (2015) Pakistan GMM TI FD ↑ CO 2
SN Bus Econ (2021) 1:56 Page 5 of 18 56
countries and regions investigate the presence of EKC. For
instance: Moomaw and Unruh (1997), Friedl and Getzner (2003),
Martinez-Zarzoso and Bengochea-Moran- cho (2004), Dinda (2004),
Dinda and Coondoo (2006), Galeotti et al. (2006), Kan- jilal
and Ghosh (2013), Managi and Jena (2008), Akbostanci et al.
(2009), He and Wang (2012), Ozturk et al. (2016) and Dogan and
Turkekul (2016).
Since the asymmetric relationships between financial development,
energy con- sumption, and CO2 emissions are not investigated in the
context of South Asia. This current study fulfills this research
gap by empirically analyzing the association between considered
variables whether it is symmetric or asymmetric. Therefore, the
proposed hypothesis is as follows:
Null (H0) : There is a symmetric association between financial
development, energy consumption, and CO2 emissions. Alternative
(HA) : There is an asymmetric association between financial
develop- ment, energy consumption, and CO2 emissions.
Data and methodology
Data
Since the primary sign of climate change and global warming is CO2
emissions, this study uses Carbon dioxide emissions (metric tons
per capita) as a proxy of the environmental indicator. Besides, the
study uses domestic credit to the private sec- tor (% of GDP) for
financial development, GDP per capita (constant 2010 US$) for
economic growth, the urban population for urbanization, and energy
consumption is taken in kg of oil equivalent per capita. All data,
over 1974–2014, are collected from the World Development Indicator
(WDI).
Methodology
The existing literature investigated symmetric analysis employing
Autoregressive Distributive Lag (ARDL) model associated with the
error correction model and granger causality test through which it
solely presents the existence of sort run and long run
relationships. That is why an asymmetric relationship among
variables is not possible for the previous studies. The NARDL
modeling technique is employed, because it generates an asymmetric
and non-linear cointegration among the variables and also captures
short run and long run effects. Moreover, the NARDL approach
relaxes the integration order restrictions where the order should
be the same for the error correction model. This facility is
supported by Hoang et al. (2016).
The NARDL model has some other benefits for ascertaining the
cointegration association in a small sample (Romilly et al.
2001). Also, it can be applied regard- less of whether the
regressors are integrated of order one, zero, or both (Pesa- ran
et al. 2001), thus avoiding the prior problems connected with
conventional cointegration techniques such as the Engle and Granger
(1987) and Johansen and
SN Bus Econ (2021) 1:5656 Page 6 of 18
Juselius (1990). Besides, it discovers not only to assess the short
run and long run asymmetries but also to unroll concealed
cointegration (Shin et al. 2014). Finally, any endogeneity and
multicollinearity problems are eschewed with the appropri- ate
interchange of lag lengths in the model (Pesaran et al. 2001;
Shin et al. 2014), which makes the approach more flexible than
the other techniques.
Shin et al. (2014) developed new modeling called NARDL model,
followed by asymmetric error correction model:
Similarly,
Following Eqs. (1) and (2), Δ reports the first difference
term; and are the
,
, ,
, and show the short run effects, while
i and i denote the long-term effects, and t and t
are the white noise error term. A long run estimation contains the
speed of adjustment and response time towards an equilibrium point,
while short term estimation provides the quick reac- tion of the
exogenous variables. To examine the long run asymmetry ( = + =
−
) and short run asymmetry ( = + = −), this study uses the Wald
test. However, FD+ and FD− are attained by a decomposition of
financial develop-
ment into partial sum of positive and negative changes (FDt = FD0 +
FD+ t + FD−
t )
as follows:
Likewise, energy consumption follows the same decomposition of
partial posi- tive and negative changes. Shin et al. (2014)
proposed a bounds test procedure to explore an asymmetric long-term
cointegrating relationship among the variables.
(1)
2 URt−1 +
SN Bus Econ (2021) 1:56 Page 7 of 18 56
From two procedures of the bound test, this study uses F test of
Pesaran et al. (2001).
A long run relationship presents among the variables if the null
hypoth- esis is rejected. The estimation of long run asymmetric
effect is based on Lmi+ = 40 and Lmi− = 50 . This study also
examines the asymmetric granger causality test (1969) among
financial development, energy consumption, and CO2 emissions.
There are some scholars who employed the NARDL modeling approach to
exhibit an asymmetric relationship. For instance: Aftab et al.
(2017) for emerging financial markets; Bahmani-Oskooee and Aftab
(2017); Aftab et al. (2019) for Asian emerging economies;
Ahmad et al. (2018) for Pakistan; Godil et al. (2020a, b)
for Turkey; and Zafar et al. (2020) for OECD countries.
Results and discussion
Prior to check cointegration analysis for ensuring long run and
short run relation- ships among variables, it is necessary to
examine the stationary properties for every single variable. As
mentioned by Gujarati and Porter (1999), non-stationary of time
series generates spurious outcomes. The ARDL bound test can be
carried out if every single time series variable is stationary at
I(0) or I(1). Additionally, F-statistics
The null hypothesis of F − statistic test + = − = = 0 (no
cointegration).
Table 2 Augmented Dicky–Fuller (ADF) and Phillips–Perron (PP) unit
root tests
Notes: (a) This table reports the unit root tests of Augmented
Dickey and Fuller (1979) and Phillips and Perron (1998). (b)
Akaike’s information criterion (AIC) is used to choose optimal lag
length for ADF and the Newey-West automatic bandwidth selection
criterion is used for PP. (c) ***, **, and * express 1%, 5%, and
10% significance level, respectively.
Augmented Dicky–Fuller (ADF) test
Variable Level First difference Order of integra- tion
Carbon dioxide emissions (CO 2 ) 0.401 − 6.477 *** I (1)
Financial development (FD) − 0.986 − 5.182*** I (1) Energy
consumption (EC) 1.206 − 5.854*** I (1) Urbanization (UR) − 0.675 −
3.536** I (1) Economic growth (EG) 2.536 − 5.668*** I (1)
Phillips–Perron (PP) test Carbon dioxide emissions (CO
2 ) 0.508 − 6.533*** I (1)
Financial development (FD) − 1.309 − 5.333*** I (1)
Energy consumption (EC) 1.069 − 5.996*** I (1)
Urbanization (UR) − 1.394 − 4.206** I (1) Economic
growth (EG) 3.604** – I (0)
SN Bus Econ (2021) 1:5656 Page 8 of 18
Ta bl
e 3
A sy
m m
et ric
e ffe
ct o
SN Bus Econ (2021) 1:56 Page 9 of 18 56
Table 4 Asymmetric effect of energy consumption on environmental
degradation
Notes: Notes: (1) Akaike’s information criterion (AIC) is used to
choose optimal lag length. (2) ***, **, and * express 1%, 5%, and
10% significance level, respectively. (3) Std. Err.-values are in
the parenthesis. (4) Lag specification: 4, 4, 4, 4, 3. (5) [3.74,
5.06] = [lower, upper bound]. (6) ECM
t−1 is known as the speed of adjustment
ECM t−1 EC
0.0037*** (0.0005)
0.0394*** (0.0104)
0.0199*** (0.0058)
− 0.00019 (0.00014)
Part B: short run coefficient estimates Lag order 0 1 2 3 Variables
ΔCO
2 – 0.9985***
(0.3078) 0.6277***
− 0.0033** (0.0012)
− 0.0044*** (0.0012)
− 0.0031** (0.0011)
ΔEC− − 0.064** (0.0255) − 0.0271 (0.0271) − 0.0333 (0.0242) −
0.054*** (0.0162) ΔUR − 0.082 (0.2019) − 0.1121 (0.1461) − 0.0203
(0.1413) 0.12072 (0.1088) ΔEG − 0.0005* (0.0003) − 0.00006
(0.00031) 0.0009**
W L
2 Root MSE
Part D: Wald test 10.83*** (0.0049) 5.83** (0.0290) 0.9471 0.8661
0.0106
Table 5 Diagnostic tests
Model (1) Kurtosis 1.23 (0.2682) Financial development
Heteroskedasticity test:
Breusch–Pagan test 0.76 (0.3837) Constant variance White’s test
39.00 (0.4246) Homoskedasticity Normality test: Skewness 16.20
(0.8468) Normally distrusted
Model (2) Kurtosis 1.00 (0.3172) Energy Consumption
Heteroskedasticity test:
Breusch-Pagan test 0.10 (0.7549) Constant variance White’s test
39.00 (0.4246) Homoskedasticity
SN Bus Econ (2021) 1:5656 Page 10 of 18
(bound test) of Pesaran et al. (2001) becomes ineffective if
the integrated order of any single variable is two or more
(Ouattara 2004). This study employs the Aug- mented Dicky–Fuller
(ADF) test and Phillips–Perron (PP) test.
Table 2 reports the results of the unit root test. The
findings clearly show that CO2 emissions, financial development,
energy consumption, urbanization, and economic growth are
integrated at I(1) for both ADF and PP test except economic growth
is stationary at the level for PP test. Thereby, bound tests may
continue. Tables 3 and 4 reports the results of Shin et
al. (2014) non-linear ARDL approach for Eqs. (1) and (2). The
table contains four segments. Part A and B indicate long run and
short run coefficient estimates, while part C and D exhibit ARDL
bounds test and Wald test. However, Table 5 reports the
findings of the diagnostic test. However, Fig. 1 discloses the
positive and negative trend of financial development and energy
con- sumption. Following the results of Table 4, the
calculated F-statistic value is 7.614 which lies over the lower and
upper bound critical value at a 1% significance level. Therefore,
the NARDL bound test explicitly reject the hypothesis of no
cointegra- tion relationship among the variables, connoting long
run connection among them.
The short run results of model (1) reveal that partial positive sum
of financial development has significant and negative impact on CO2
emissions, while in the long run, it has no significant effect on
South Asian economies. Contrariwise, there is a positive and highly
significant effect of the partial negative sum of financial
development on CO2 emissions but a negative and significant impact
in the long run.
Fig. 1 Positive and negative trend of financial development and
energy consumption
SN Bus Econ (2021) 1:56 Page 11 of 18 56
Accordingly, 1% increase in partial positive changes of financial
development leads to decrease CO2 emissions by 0.79 or 0.92 percent
in the short run and 1% decrease in partial negative changes of
financial development proves to raise environmental degradation by
1.71% uplifting CO2 emissions in the short run, while reduces envi-
ronmental erosion by 1.77% in the long run for South Asian
economy.
However, urbanization only has a negative and significant effect on
CO2 emis- sions in the short run, while economic growth has both
short and long run impacts. In the short run estimation, economic
growth has a significant and negative relation- ship with CO2
emissions where has a positive association with CO2 emissions in
the long run. The error correction term measures the speed of
adjustment that explains how shortly variables respond to the long
run equilibrium. Accordingly, the coeffi- cient of ECMt−1 is
negative and significant at 1% confidence level which also means
that there exists a static long-term relationship (Banerjee
et al. 1998). The adjusted R2 value (0.6895) exhibits the
goodness of fit of the models.
However, the results of the Wald test reject the null hypothesis in
the long run that financial development asserts a symmetric impact
on CO2 emissions long run, explaining that positive and negative
variation of financial development has a dif- ferent significant
effect on CO2 emissions in the long run. Besides, the diagnostic
test results of Table 6 validate that the model is free from
heteroscedasticity. Also, Skewness and kurtosis sign ensure that
the residuals of the model practice normal distribution.
Now, according to the estimated results of Table 4, a partial
positive sum of energy consumption has a significant and negative
effect on CO2 emissions in the short period regarding different lag
orders, but contrariwise, it has a positive and significant
relationship with CO2 emissions in the long period for South Asia.
Also, partial negative changes in energy consumption has a negative
significant impact on CO2 emissions in the short run considering
various lag order, while as per expec- tation, it positively
affects the CO2 emissions in the long run. Accordingly, 1% increase
in the partial positive sum of energy consumption minimizes
environmental degradation by 0.44% in the short period taking lag
order two and over against pro- duces environmental deterioration
by 0.37% in the long period for South Asia. How- ever, as expected,
1% decrease in partial negative changes in energy consumption
declines environmental wasting by 5.4% in the short run regarding
lag order three, while it pollutes the environmental quality by
3.94% in the long run.
Besides, urbanization worsens the environment of South Asia by
1.99% in the long period. Also, economic growth has a significant
and negative impact on CO2 emissions in the short run and it has no
significant effect in the long run. The speed of adjustment (
ECMt−1 ) shows that Eq. (2) is stable as it is negative and
highly sig- nificant at 1% level of significance. The value of
F-statistic is 6.345 which lies over the lower and upper bound
critical value at 1% significance level. Thus, the NARDL bound test
clearly reject the hypothesis of no cointegration relationship
among the variables, indicating long-term association among
them.
However, the outcomes of the Wald test reject the null hypothesis
mean that energy consumption validates symmetric impact on CO2
emissions in the short run and long run, interpreting that positive
and negative changes of energy consumption have a different
significant impact on CO2 emissions (long run: 10.83; short
run:
SN Bus Econ (2021) 1:5656 Page 12 of 18
Ta bl
e 6
S ym
m et
ric e
ffe ct
SN Bus Econ (2021) 1:56 Page 13 of 18 56
5.43). The adjusted R2 value (0.8661) describes the goodness of fit
of the models. Additionally, the estimated results of the
diagnostic test in Table 5 proves that the model is free from
heteroscedasticity. Also, Skewness and kurtosis sign prove that the
residuals of the model practice normal distribution.
Now, moving to Table 6, the ARDL model is calculated to match
with NARDL model. Following the estimations of the model (1), the
long run elasticities of CO2 emissions are positive and highly
significant for urbanization and economic growth, while negative
for financial development. The outcome suggests that 1% increase in
financial development reduces CO2 emissions by 0.8%. In contrast,
relative to eco- nomic growth and urbanization, the short run
elasticities are negative and significant for the model (1).
Turning to the model (2), energy consumption and urbanization badly
affect the environment increasing CO2 emissions in the long run and
economic growth has a negative association. This result explains
that 1% rise in energy con- sumption enhances CO2 emissions by
0.49%. The short run results exert that energy consumption has an
adverse relationship with CO2 emissions, while economic growth
positively affects the CO2 emissions.
The error correction term ( ECTt−1 ) is statistically significant
and negative for models (1) and (2), thus confirming the presence
of long-run dynamics in these models. According to the findings,
the ARDL bound test rejects the null hypothesis of no cointegration
relationship for both models as the F-statistic values lie over the
lower and upper bound critical value at 1% significance level. Both
models are well defined due to the characteristics of constant
variance and homoscedasticity.
Table 7 reports the asymmetric granger causality test. The
results exhibit that there is a unidirectional causal relationship
from partial positive sum of financial development to CO2
emissions, while CO2 emissions also have a unidirectional causal
connection with partial negative sum of financial development. On
the other hand, partial negative sum of energy consumption
generates unidirectional causal relationship with CO2 emissions,
while there is also a unidirectional causality from CO2 emissions
to partial positive sum of energy consumption.
Table 7 Asymmetric granger causality test
Note: ***, **, and * express 1%, 5%, and 10% significance level
respectively
Model Statistics Causality
Conclusions and remarks
Based on the yearly data from 1972 to 2014, this study explores the
relationships among CO2 emissions, financial development, energy
consumption, economic growth, and urbanization for South Asia. With
the help of non-linear ARDL model, the empirical results validate
the asymmetric connection between energy con- sumption, financial
development and environment as the CO2 emissions are highly
affected by both positive and negative shocks in energy consumption
and financial development. The outcomes of the ARDL approach
express that energy consump- tion has a positive impact on CO2
emissions in the long run, while financial devel- opment has an
adverse effect. Comparing the analysis of the ARDL and NARDL model,
this study confirms that energy consumption vastly contributes to
rise the CO2 emissions in South Asia than financial development. To
prevent the environ- mental wasting of South Asia, these factors
can be used as important techniques for policy makers and
governments.
This paper offers some policy recommendation which is consistent
with the results. First, the positive connection between CO2
emissions and financial development pro- pounds that the
policymakers of South Asia should concentrate on financial develop-
ment while making policy to decline the greenhouse gases. They
should adopt differ- ent policies for the different order of
economic development. For example: when the economic development is
at an early period, the ratio of financial development should be
exhorted. Contrariwise, when the economy thrives enough, the
adverse effects of financial development on the atmosphere should
be cautiously governed. To minimize the deleterious effects of
financial development on the environment, the banking sector of
South Asia should aware of the misallocation of financial funds.
The bank authority should be provided useful financial resources to
the proficient and productive industry in place of issuing cheap
loans to inefficient and consumptive enterprises. Then, there will
be environment friendly technology with high production. Second,
the positive relation- ship between CO2 emissions and energy
consumption in the findings proposes that the government can
enhance the environment quality by imposing restrictions on
inefficient energy consumption machines and the use of fossil fuels
and should grant subsidies on low carbon use technologies such as
renewable energy.
This paper suggests further study using other determinants of
environmental degra- dation such as globalization, trade balance,
global value chain, and total employment. Moreover, similar
econometric tools can be employed considering both renewable and
non-renewable energy consumption.
Author contributions The authors have equal contribution.
Data availability The data can be made available upon reasonable
request.
Material availability The authors will follow the journal
policy.
Conflict of interest The authors declare no conflict of
interest.
SN Bus Econ (2021) 1:56 Page 15 of 18 56
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Asymmetric effect of financial development and energy
consumption on environmental degradation in South Asia?
New evidence from non-linear ARDL analysis
Abstract
Introduction