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Decoupling
CO2 emission per capita from GDP per capita is an essential property
in indicating Environmental Kuznets Curve hypothesis which is empirically
verified in Indian economy during 1970-2016 and in addition to that this
phenomenon is also satisfied if population density, energy use, electricity
consumption, foreign direct investment inflows and degree of openness are
assumed to be other determinants of per capita CO2 emission in which
N shaped EKC hypothesis was verified in both cases. To examine it, the paper
used semi-log and double log regression analysis, Bai-Perron Model for
structural breaks, Johansen co-integration test and vector error correction
model including Wald test for short run causality. The paper concludes that
India’s per capita CO2 emission has been increasing at the rate of
3.49% per year during 1970-2016 significantly with four upward structural
breaks. There are six co-integrating equations among those variables so that
long run association exists among them. Long run causalities were found from
GDP per capita, electricity consumption, FDI inflows and trade openness to the
CO2 emission per capita. There are short run causalities running
from population density, energy use, electricity consumption respectively to
the CO2 emission per capita. There is bidirectional short run
causality between FDI and population density. But short run causality was found
from energy use to trade openness. The vector error correction model is
unstable, non-stationary and non-normal.
Keywords: CO2
Emission per Capita, GDP per Capita, Energy Consumption, Foreign Direct
Investment, Trade Openness, Environmental Kuznets Curve, Co-integration, Short
Run Causality, Long Run Causality.
INTRODUCTION
The relationship between economic
studies of the trade openness and CO2 emissions has undoubtedly
become a major concern for economists, policymakers and the general public. The
relation may be positive or negative. In economic theory, the fact is that increasing
trade enhances economic growth. Similarly, increasing growth adversely affects
the environment by releasing emissions into the atmosphere. It is expected that
the countries affected to implement more environment friendly production
techniques to enhance the quality of the environment.
A number of studies have examined
the environmental consequences of trade liberalization and economic growth in
recent decades. According to the EKC concept, CO2 emissions are
expected to have a positive relationship with the level of income or trade
liberalization before the EKC threshold and then a negative relationship beyond
the threshold. Similarly, if there is a positive relationship between CO2
emissions and free trade, then the country is not likely to have experienced
its optimal level of trade liberalization.
The
view that the foreign direct investment enhances CO2 emissions is
called Pollution Haven Hypothesis. The Pollution Haven Hypothesis can occur in
three ways (Aliyu, 2005). Firstly, pollution industries arise through polluting
industries to countries with more loose regulations than countries with strict
environmental regulations. Secondly, developed countries throw away hazardous
wastes related to industrial and nuclear energy production into developing
countries. Thirdly, multinational corporations should obtain unlimited sources
of renewable resources such as oil and petroleum products, lumber and other
forest resources, etc., in developing countries. Foreign investments can cause
positive or negative environmental impacts in host countries in the form of two
conditions called pollution haven and pollution hale effects. If the
environmental impact of foreign direct investments is positive, it is a
pollution haven hypothesis. If it is negative, it becomes pollution hale
effect.
The relationship between energy
consumption and economic growth has important implications for energy policy
and there are four types of causalities between them such as neutral,
conservative, growth and feedback hypothesis. However, rapid economic growth is usually accompanied by
increased energy consumption and may cause unexpected effects on energy
resources and the environment. (Shiu & Lam, 2004; Mozumder &
Marathe, 2007; Ozturk, 2010).Other
authors such as Keppler and Mansanet-Bataller (2010), Narayan (2010) and Pao & Tsai (2010) stated that economic growth and energy
consumption are accompanied with environmental degradation in both developed
and developing countries. These studies have generated an inverted U-shaped
curve. Toda-Yamamoto
(1995) examined that the results of causality test imply that carbon emissions
and FDI, energy consumption and CO2 emissions have bidirectional
causal relationships. On the other hand, there are unidirectional causal
relationships running from economic growth and energy consumption to FDI and
from economic growth to energy consumption.
The contribution of
globalisation, particularly trade openness, towards greenhouse gas emissions
have been an important issue within the context of human induced climate
change. As individual countries vary according to income levels as well as the
composition of traded commodities, which have different emission intensities,
the relationship between trade openness and greenhouse gas emissions can be
considered as an empirical question which depends on country specific
variables. Baek et al. (2009)
have examined the dynamic relationship among trade, income and environmental
quality, i.e., emission of sulphur dioxide (SO2) using co-integration
analysis for a sample of developed and developing countries. The results
suggest that trade and income growth increase environmental quality in
developed countries and the reverse is evident in most developing countries.
According to economic theories, there is a positive
relationship between trade liberalization and economic growth. Since trade
openness could increase production and income, it affects the emissions. But,
by virtue of trade theories there is no clear relationship between
environmental quality and trade openness. The similar hypothesis of EKC is
applicable to the conditions of trade openness and environmental effects, and
also towards the experimentation for measuring scale effect of the open
economic policies.
Lastly, economic studies concluded that income per
capita and population growth are the main two factors increasing carbon
emissions in OECD countries, whereas the decrease in energy intensity is the
main factor reducing them. In the EKC hypothesis, these studies indicate that
the elasticity of CO2 emissions and energy use with respect to
population are close to unity. A few of them considered population density as
an additional explanatory variable. Emissions are typically decomposed into
scale, composition and technique effects. Scale effects are measured with
income and population variables, composition effects refer to changes in the
input or output mix and technique effects are proxied by energy intensity (the
effect of productivity on emissions) and global technical progress.
In those above perspectives, the paper seeks to
explore EKC hypothesis with respect to GDP per capita to consider population
density, energy use, electricity consumption, foreign direct investment inflows
and trade openness as other explanatory variables of CO2 emission
per capita in India from 1970 to 2016.
LITERATURE REVIEW
Zhang & Liu (2017) examined 10 newly
industrialised countries during 1971-2013 and found that the results support Environmental
Kuznets Curve hypothesis where trade openness negatively affects the emissions
and real GDP and energy positively affects the emissions. The vector error
correction model indicates long run causality from energy to emission and from
trade openness to energy. Ave, Edoja & Charfedding (2017) studied 31
developing countries from 1970 to 2013 in panel data and showed that economic
growth has negative effect in CO2 emission under low growth regime
but it is positive in high growth regime. The validity of EKC is inverted U
shaped. Energy consumption and population exert positive and significant effect
on CO2 emission. Ameyaw & Yao (2018) studied five West African
countries during 2007-2014 in panel data. The research found that the causality
revealed that there exists a unidirectional causality running from GDP to CO2
emission and from labor force to CO2 emission and no causality was
found from CO2 emission and gross fixed capital formation.
Bidirectional Long Short-term Memory Algorithm Formulation process showed that
the upward surge in African’s total CO2 emissions is a threat to
human and ecological safety if past and current intricate paths in data are
transmitted into future.
Pazienza (2015) examined that FDI inflows in
agriculture and fishing sector of 30 OECD countries from 1981 to 2005 affected
emissions negatively in panel data analysis. Peng et al. (2016) studied
province level panel data in China using cross sectional dependence and
homogeneity and found that there was bidirectional causality between FDI and CO2
emissions in Neimenggu and there was unidirectional causality from FDI to CO2
emissions in Beijing, Henan, Guizhou and Shanxi during 1985-2012. Albiman, Suleiman
& Baka (2015) used causality and non-causality test, impulse response
functions and variance decomposition, ADF and P.P. unit root test in Tanzania
during 1975-2013 to investigate environment pollution and per capita economic
growth. Economic growth rate and energy consumption per capita both being
unidirectional, cause environment pollution through CO2 emission in
Tanzania. Sulaiman & Abdul-Rahaman (2017) verified empirically in Malaysia
during 1975-2015 using Autoregressive Distributed Lag approach, Vector Error Correction,
variance decomposition and impulse response functions respectively where energy
consumption is revealed to be an increasing function of CO2 emission
which increases with energy consumption and economic growth. Kais & Mbarek (2017)
used panel unit root and co-integration, vector error correction and Granger
causality test during 1980-2012 in three North African countries and found that
there were unidirectional causalities running from economic growth to CO2
emission and from energy consumption to CO2 emission. A high level
of economic growth leads to high level of energy consumption. Lean & Smyth (2010)
showed in 5 ASEAN countries during 1980-2006 that there is positive association
between electricity consumption and emissions and a nonlinear relationship
between emissions and real output consistent with EKC. There is unidirectional
causality from emissions to electricity consumption in the short run. Sohag et
al. (2017) investigated the impacts of real income, trade, population increase
and energy consumption on CO2 emissions using data from 82
developing nations between 1980 and 2012 using various Mean Group (MG)
approaches (cross-correlated and augmented). The results showed that a
percentage increase in trade (holding all of the other explanatory variables
constant) reduces CO2 pollution by 0.3%. Meanwhile, the results were
inconclusive for low-income, middle-income and full sample countries.
Sun et al. (2019) studied in 49 Belt and Road high
emission countries from 1991 to 2014 in panel data using panel co-integration
under three income panels: high, medium and low respectively. The paper found
that trade openness had both positive and negative impacts on environmental
pollution but varied on different groups of countries. VEC model showed that in
the long run there are causal effects between trade, economic growth, energy
consumption and environmental pollution in Belt and Road countries. EKC results
indicated the existence of an inverted U-shaped relationship between trade
openness and carbon emissions. Naranpanawa (2011) empirically verified in Sri
Lanka during 1960-2006 through ARDL approach and Johansen-Juselius co-integration
test and found that there is long run relation and there exists both short run
and long run causalities between trade openness and carbon emissions. Choi,
Heshmati & Cho (2010) studied in China, Korea, Japan from 1971-2006 in
which China showed an N shaped while Japan had U shaped curve. In Korea, it is
inverted U shaped. VEC model showed that in China openness is negatively
related with emission and openness square is positively related with emission.
GDP and GDP square are positively related with emission significantly. In
Korea, openness is positively related with emission and openness square is
negatively related with emission significantly. GDP affects negatively and GDP
square affects negatively in emission significantly. In Japan GDP is negatively
related with emission and GDP square positively affects emission significantly
while openness has positive impact with emission and negative impact with openness
square insignificantly during the specified period. Stern & Common (2001)
investigated the presence of an EKC for emissions of SO2 using a
panel of 73 countries from 1960 to 1990. The results of their analysis provide
evidences of a global inverted-U shaped EKC. Random effects estimation produces
consistent results and again reveals an inverted-U shaped EKC for OECD
countries. Many empirical studies have verified the EKC
hypothesis. Bhowmik (2019) examined in Nordic countries during 1970-2016 and
found the validity of EKC hypothesis where relative and absolute decoupling
from GDP per capita to the CO2 emission per capita was observed. Hettige
et al. (1992); Cropper & Griffiths (1994); Grossman & Krueger (1995);
Martinez-Zarzoso & Bengochea-Morancho (2004); Apergis (2016) and Bae (2018)
all support the EKC hypothesis.
Dietz & Rosa (1997) and York, Rosa & Dietz
(2003) studied the impact of population on carbon dioxide emissions and energy
use within the framework of the IPAT1 model. The results from these studies
indicate that the elasticity of CO2 emissions and energy use with
respect to population are close to unity. In a panel data context, Shi (2003)
found a direct relationship between population changes and carbon dioxide emissions
in 93 countries over the period 1975-1996. A similar result was obtained by
Cole & Neumayer (2004). These authors considered 86 countries during the
period 1975-1998 and they found a positive link between CO2
emissions and a set of explanatory variables including population, urbanization
rate, energy intensity and smaller household sizes.
OBJECTIVES OF THE PAPER
The paper endeavors to search out the absolute and
relative decoupling of CO2 emissions per capita from GDP per capita
of India during 1970-2016 along with other determinants like population
density, energy use, electricity consumption, FDI inflows and degree of
openness and tries to verify Environmental Kuznets Curve hypothesis. Even, the
paper attempts to find out the long run association between those variables and
short causalities among them by applying econometric models of co-integration
and vector error correction model. The paper also examined to show the
structural behavior of CO2 emission per capita of India.
METHODOLOGY
AND SOURCE OF DATA
Semi-log linear regression model is used to show
trend value. Bai-Perron (2003) model is applied to find out the structural
breaks of emission. Double log multi-variable regression model is used to examine
the relation among the variables studied here. Johansen (1988) co-integration
test is applied to indicate long run association and vector error correction
model is utilised to find out short and long run causalities with the help of
co-integrating equations. Wald test (1943) is done for acceptance or rejection
of short run causality. Hansen-Doornik (1994) VEC normality test verified the
multivariate normality. The data of CO2 emission per capita in
million ton, GDP per capita in US dollar at current prices, population density
per square km of land, energy use in kg of oil equivalent per capita,
electricity consumption in kwh per capita of India from 1970 to 2016 were
collected from the World Bank. The data of foreign direct investment inflows in
million dollar and trade openness of India from 1970 to 2016 were collected
from UNCTAD (trade openness=sum of export and import of goods and
services/2/GDP/2).
SOME
OBSERVATIONS FROM THE ECONOMETRIC MODELS
India is the third largest CO2 emitter in
this world. In India CO2 emission per capita has been increasing at
the rate of 3.49% per year from 1970 to 2016 significantly.
Log(y)=-1.1397+0.03498t
(-76.30) * (66.36)*
R2=0.989,
F=4404.42*, DW=0.66, *=significant at 5% level, y=CO2 emission per
capita in India, t=period of time.
India’s CO2 emission per capita has four
upward structural breaks in 1983, 1990, 1998 and 2008, respectively, which have
been determined by Bai-Perron test through applying HAC standard errors and
covariance (Bartlett Kernel, Newey-West fixed bandwidth=4.00). These structural
breaks are plotted in the following Figure 1.
Log(y)=28.307 - 16.889 log(x1) + 2.838
log(x1)2 - 0.1524 log(x1)3
(2.26) * (-2.58)* (2.65) * (-2.66) *
+ 2.332 log(x6) - 0.3782 log(x6)2
+ ui
(2.84) * (-2.51)*
R2=0.954,
F=171.20*, DW=0.39, AIC=-1.14, SIC=-1.17, *=significant at 5% level, x1=GDP
per capita in India, x6=trade openness.
The estimated equation states that India’s CO2
emission per capita (y) is absolutely decoupled with x1 and x13
but not relatively decoupled with x12 since δlog(y)/δlog(x1)<0,
δlog(y)/δlog(x1)2>1 and δlog(y)/δlog(x1)3<0.
The decoupling is accelerated with higher trade openness since δlog(y)/δlog(x6)2<0
satisfying absolute decoupling condition. Thus GDP per capita with liberalisation
both satisfied EKC hypothesis showing N shaped curve (Figure 2).
Log(y)=-3.115
- 2.924 log(x1) + 0.5559 log(x1)2 - 0.0347 log(x1)3-
0.3953 log(x2)
(-0.81) (-1.72)* (1.82)* (-1.93)* (-1.03)
+ 1.3115 log(x3)+0.4085log(x4)-0.00055log(x5)
+0.0342log(x6)
(4.15)* (3.85)* (-0.18) (1.04)
R2=0.997,
F=1952.15*, DW=2.44, AIC=-4.22, SIC=-3.86, *=significant at least 10% level, x1=GDP
per capita in current US Dollar, x2=population density per square km
of land, x3=energy use in kg of oil equivalent per capita, x4=electricity
consumption kwh per capita, x5=Foreign direct investment inflows in
million dollar, x6=trade openness (average value of total trade of
goods and services by average of GDP), y=CO2 emission per capita in
million ton.
If population density, energy consumption, foreign
direct investment inflows and trade openness are included with GDP per capita
as the chief determinants of CO2 emission per capita in India during
1970-2016,then the above estimated equation exemplified that EKC hypothesis is
accepted for decoupling CO2 emission per capita where energy
consumption (x3 and x4) are positively revealed with
emission significantly but negatively related with population density and FDI
inflows insignificantly and also positively related with trade openness
insignificantly. GDP per capita and cube of GDP per capita have been decoupling
CO2 emission per capita absolutely (δlog(y)/δlog(x1)<0
and δlog(y)/δlog(x1)3<0) and square of GDP per capita
has been decoupling relatively with CO2 emission per capita
(δlog(y)/δlog(x1)2>0<1) in India significantly with
10% level. In the following Figure 3, it is visible that EKC is partially N
shaped during the period of study.
Johansen
unrestricted co-integration rank test with linear deterministic trend of first
difference series of the CO2 emission per capita, GDP per capita,
GDP per capita square, GDP per capita cube, population density, energy use,
electricity consumption, foreign direct investment inflows and trade openness
of India from 1970 to 2016 indicates that Trace statistic contains seven co-integrating
equations and Max-Eigen statistic contains six co-integration equations which
mean that all the variables have long run association significantly. The values
are shown in the Table 1.
The estimated VEC model states that the positive
change in population density, electricity consumption, and foreign direct
investment inflows induced the negative changes in the CO2 emission
per capita during 1970-2016 significantly but the positive change in energy use
indicated a positive change in CO2 emission per capita along with a
relative decoupling process. The incremental change in FDI is positively
related with increment in population density due to absolute decoupling and
relative decoupling has been associated with the foreign direct investment
inflows. Incremental change in energy use has positive influence on increment
in trade openness but incremental change in CO2 emission per capita
has negative relation with the increment in trade openness significantly during
the specified period. The t values of coefficients of six error corrections and
all other coefficients of the variables of the VEC model are given in the Table
2 shown below.
In the system equations of the VECM, the Wald test
proved that there are short run causalities running from population density, energy
use, electricity consumption and foreign direct investment inflows to the CO2
emission per capita in India. In addition to that there are short run
causalities from foreign direct investment inflows to population density, from
GDP per capita to foreign direct investment inflows and from energy use to
trade openness respectively. Null hypothesis H0 and all the Chi-square values
of the coefficients with their probabilities of the Wald test have been incorporated
in the Table 3.
With the help of the estimated system equations, the
paper found six significant co-integrating equations which are shown in the
Table 4 from which it is clear that the equation 1 has been converging
significantly towards the equilibrium since all the t values of the
coefficients are significant and c(1)<0 and is significant which indicates
that apart from the decoupling there are long run causalities running from
electricity consumption, foreign direct investment inflows and trade openness
to the per capita CO2 emission in India during 1970-2016. Moreover,
the equations 5 and 6 have been approaching to the equilibrium but they are not
significant and other equations are divergent.
In the Figure 4, the co-integrating relationships
have been depicted neatly where part 3 of the figure has reached to the
equilibrium and the part 2 and 5 have been marching towards equilibrium and
others are diverging. Therefore, the equilibrating co-integrating equation
implies that there are long run causalities running from GDP per capita, electricity
consumption, foreign direct investment inflows and trade openness to the per
capita CO2 emission in India during 1970-2016, respectively. In the
long run all the variables are associated which are very clear in the figures.
India’s high emission rate enhances warming in which
policy of renewable energy production is urgent where India’s National Action
Plan for climate policy showed eight objectives and India’s sustainable
development goals relating to goal 13 of UNO SDG have been emphasized for quick
implementation. Moreover, Task Force of NITI Aayog has been adopted all policy
issues relating to SDG and climate change. Other important policies relating to
carbon tax, green investment for renewable energy, protection for environmental
goods, negotiations for WTO laws, long term policy for energy use and trades
and preservation and protection of forests and forests products are necessary.
Waste management, disaster management, rehabilitation of climate refugees,
early warning system of weather are the simultaneous important policy decisions
that a good government can take. Assessment and monitoring are the significant
aspects of the plans which can make long run goals success. A separate climate
fund for India to cope up with adverse impact of climate change should be set
up.
CONCLUDING REMARKS
The paper concludes that per capita CO2
emission of India during 1970-2016 has been catapulting at the rate of 3.49%
per year significantly. It has significant four upward structural breaks in
1983, 1990, 1998 and 2008.India’s CO2 emission per capita has been
decoupled absolutely and relatively with GDP per capita showing N shaped EKC
along with high degree of openness. Same conclusion can be drawn when
population density, energy use, electricity consumption, FDI inflows and trade
openness are being considered as other determinants of CO2 emission
where pollution hale hypothesis with FDI was observed. Trade openness showed
significant positive relation but population density indicated insignificant
negative effect. Johansen test confirmed at least six co-integrating equations
showing long run associations among the variables. Long run causalities were
found from GDP per capita, electricity consumption, FDI inflows and trade
openness to the CO2 emission per capita. There are short run
causalities running from population density, energy use, electricity
consumption to the CO2 emission per capita. There is bidirectional
short run causality between FDI and population density. But short run causality
was found from energy use to trade openness. The vector error correction model
is unstable, non-stationary and non-normal.
Irrespective of that, there are shortcomings too.
The model suffers from autocorrelation problems and that’s why insignificant
relation with FDI and trade openness has been found. Even there are
volatilities of trade openness, GDP per capita and FDI so that perfect N shaped
EKC is not visible. If other data of GHG emissions are included in the model then
it might obtain more clear shape of EKC so that the flawless policy
prescriptions can be formulated.
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