World Review of Business Research
Vol. 5. No. 2. April 2015 Issue. Pp. 39 – 59
Impact of Agriculture, Industry and Service Sector’s Value
Added in the GDP on CO2 Emissions of Selected South Asian
Countries
Janifar Alam*
The study will determine the impact of Agriculture,
manufacturing Industry and Service sector’s value added in
the GDP on CO2 emissions of selected south Asian countries;
Bangladesh, India, Nepal and Sri Lanka. Moreover, the study
empirically analyze the factors that affects CO2 emissions and
examine the Environmental Kuznets Curve for GDP per capita
and CO2 emissions in metric tons per capita of mentioned
countries by using World Bank data for 39 years from 19722010. The finding of this study suggests that value added of
agriculture in the GDP has negative significant impact on CO2
emissions where industrial and services value added in the
GDP has positive significant impact on CO2 emissions. All
measured associating factors are significantly affects the
growth of CO2 emissions. The existence of EKC “U” shape
does not hold because CO2 emissions increase with income.
Field of Research: Environment and National Economy
1. Introduction
Climate change and global warming are the greatest and most significant environmental
issues for the last two decades. Scientists claim that the increasing carbon dioxide (CO2)
emissions produce a substantial increase of greenhouse gases, which have given rise to
recent occurrences of warm temperatures. Elevated CO2 emissions from industries,
factories, vehicles have contributed to the greenhouse effect, causing warmer weather that
lasted long after the atmosphere.CO2 emissions have grown radically in the past century
because of human activities, mainly by the use of fossil fuels as well as energy production
that are directly linked with economic growth and development. Environmental Kuznets
curve (EKC) analysis is an econometric methodology which assumes that environmental
quality or pollutant emissions are correlated with economic growth. The growth of Gross
Domestic Product (GDP) is specifically a good indication of economic growth. The EKC
relationship states that environmental degradation increases when GDP per capita
increases up to a certain threshold level and beyond a threshold level (the turning point)
pollution decreases and environmental quality improves.
The EKC discusses “Stages of economic growth” that execute in three phases; it
discusses, Stage 1: Society concentrates resources in the primary sector (i.e. extraction,
agriculture) to satisfy necessary consumption; Stage 2: As basic needs are satisfied and
further consumption is concentrated on consumption goods so resources are switched to
the secondary sector (i.e. manufacturing) and environmental pollution increases and Stage
3: Society moves from the secondary to the tertiary sector (i.e. Services) characterized by
much lower levels of pollution.
Janifar Alam, Lecturer, School of Business, University of Information Technology and Sciences, GA-37/1
Progoti Sarani, Baridhara J-Block, Dhaka 1212, Bangladesh. e-mail: sumi_janifar@yahoo.com
Alam
In 1991, the EKC hypothesis was first introduced by Grossman and Krueger for different
environmental indicators, including the CO2 emissions as well. The EKC hypothesis stated
an inverted U-shape relation between various indicators of environmental quality and per
capita income. Under this hypothesis, CO2 emissions were usually explained by linear,
quadratic or cubic polynomial functions of income per capita.CO2 accounts for the largest
portion of greenhouse gas emissions and a major source of environmental problems. Thus
it is meaningful to examine the EKC relationship between CO2 emissions and GDP per
capita.
Several effects of global warming are projected to affect the South Asian region including
steady sea level rise, increased cyclonic activity and changes in ambient temperature and
precipitation patterns. As per the IPCC, the projected for the mean annual increase in
temperature by the end of the century in South Asia is 3.3 °C with the min-max range as
2.7 and 4.7°C. The mean value for Tibet would be higher with mean increase of 3.8°C and
min-max figures of 2.6 and 6.1 °C respectively which implies harsher warming conditions
for the Himalayan watersheds. The corresponding sea level rise at the end of the 21st
Century relative to the end of the 20th Century ranges from 0.18 to 0.59 m. Ongoing sea
level rises have already submerged several low-lying islands in the Sundarbans, displacing
thousands of people. Temperature rises on the Tibetan Plateau, which are causing
Himalayan glaciers to retreat. So the study is conducted to examine the EKC relationship
between CO2 emissions and GDP per capita on South Asian region for some selected
countries like Bangladesh, India, Nepal and Sri Lanka.
The study has analyzed the factors affecting on the annual growth of GDP and CO2
emissions. Numerous factors are responsible for CO2 emissions such as Rate of
deforestation, Trade, Population density, Urbanization, Climate, Population growth, Number
of Vehicles on road, Education level, Employment level and country‟s investment. Here the
study observe robustness of some associating factorsofCO2 emissions which are GDP,
Fossil fuel energy consumption, Exports and Imports of goods and services, Energy
production, Energy use and Electric power consumption. There are only three variables
selected as major contributors of the annual GDP growth which are Agriculture, Industry
and services.
GDP is the monetary value of all final goods and services produced within a country in a
given time period, though GDP is usually calculated on an annual basis. GDP is the sum of
gross value added by all resident producers in the economy plus any product taxes and
minus any subsidies not included in the value of the products. It is calculated without
making deductions for depreciation of fabricated assets or for depletion and degradation of
natural resources. Traditional macroeconomic accounting divides GDP into three principal
categories: Agriculture, Industry, and Services. Agriculture includes agricultural and
livestock production and services; fishing; hunting; and forestry. Industry includes mining
and quarrying; manufacturing; construction; and electricity, gas, and water. Finally,
Services includes transport, storage, and communications; wholesale and retail trade;
banking, insurance, and real estate; ownership of dwellings; public administration and
defense and other services.
Now this study concentrates on average contribution of agriculture, manufacturing and
service sector in GDP contribution of 39 years; from the year of 1972 to 2010. In the GDP
of Bangladesh, the average contribution of agriculture sector is 32.22%, manufacturing
sector is 21.61% and service sector is 46.17%.In the GDP of India, the average
contribution of agriculture sector is 28.45%, manufacturing sector is 25.58% and service
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sector is 45.97%.In the GDP of Nepal, the average contribution of agriculture sector is
49.58%, manufacturing sector is 16.07% and service sector is 34.35%. In the GDP of Sri
Lanka, the average contribution of agriculture sector is 22.99%, manufacturing sector is
27.30% and service sector is 49.71%. It is noticeable that value addition of service sector in
GDP is higher than manufacturing sector. So our society is in service economy as service
sector generates more wealth than the manufacturing sector of the economy. According to
the EKC hypothesis, our economy moves from the secondary (manufacturing) to the
tertiary sector (Services) and environmental pollution should be decrease. To justify the
hypothesis, the study wants to investigate the association of CO2 emissions with value
added of agriculture, value added of industry and value added of services in the GDP (% of
GDP) which was not addressed by the past studies.
The main objective of the study is to analyze the impact of agriculture,
industry and service sectors value added in the GDP on CO2 emissions of
Asian countries which are Bangladesh, India, Nepal and Sri Lanka.
empirically analyze the factors that affects the CO2 emissions and
Environmental Kuznets Curve for GDP per capita and CO2 emissions
countries from the year of 1972 to 2010.
manufacturing
selected south
In addition to
examines the
for mentioned
The rest of the paper is organized as follows namely section 2 reviews the theoretical
background, section 3 discusses literature review, section 4 lays out the methodology,
section 5 discusses the results from the analysis and the last section concludes.
2. Theoretical Background
EKC is an inverted-U-shaped relationship between economic growth and measured
pollution indicators or environmental qualityas economic growth is linked to continuous
structural transformation and change. In fact EKCmodel represents the structural change;
as income of an economy grows over time, emission level grows first, reaches a peak and
then starts declining after a threshold level of income has been achieved.
Figure 1: Environmental Kuznets Curve
Thus the EKC has another term “stages of economic growth” as economies pass through a
transition from agriculture based economies to industrial economies results in increasing
environmental degradation and then post-industrial service based economies consequently
begin to demonstrate decreases in pollution and environmental degradation. The transition
from agricultural to industrial economies results in increasing environmental degradation
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due to greater use of natural resources, more emission of pollutants, emphasize to
increase in material output. The transition from industrial to service based economy is
assumed to result in leveling off and a steady decline of environmental degradation
because of increased environmental awareness, higher environmental expenditures,
efficient technologies and increased demand for environmental quality. As income moves
away from the EKC turning point, it is assumed that transition to improving environmental
quality starts.
Figure 2: Stages of Economic Development
3. Literature Review
Akbostanci et al. (2009) investigated the relationship between income and environmental
degradation in Turkey. By using a time series model spanning from 1968 to 2003, they
found that CO2 emissions and income tend to have a monotonically increasing relationship
in the long run. This monotonically increasing relationship implies that the EKC hypothesis
does not hold in this case.
Ang (2008) found positive link between GDP per capita, energy consumption, CO2
emissions for Malaysia. Causality runs from output to energy consumption not only in the
short, but also in the long run.
Galeotti et al. (2009) explained that EKC is not found at all the times relating to CO2.
Halicioglu (2009) examine the relationship between income per capita, carbon emissions,
energy use and trade openness for Turkey. Results from ARDL bounds testing approach
support cointegration among the series. In addition to EKC relation, he also found that
energy consumption, trade and CO2 emissions are the main contributors to economic
growth in the long run. Halicioglu (2009) applied ARDL approach of cointegration in a loglinear quadratic equation among CO2 emission, energy consumption, economic growth in
order to test the validity of EKC for Turkey. Results suggested that the most significant
variable in explaining the carbon emissions in Turkey is income followed by energy
consumption and foreign trade.
Jalil and Mahmud (2009) found uni-directional causality running from economic growth to
CO2 emissions in China. The results of the study also indicate that the carbon emissions
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are mainly determined by income and energy consumption in the long-run. Moreover trade
has a positive but statistically insignificant impact on CO2 emissions.
Lean and Smyth (2009, 2010) examined the relation between electricity consumption, CO2
emissions and output for ASEAN countries using a panel vector error correction model.
They found a positive and significant long run relation between electricity consumption and
CO2 emissions. The CO2 emissions and GDP per capita relation supports the existence of
EKC.
Saboori and Soleymani (2011) examines the dynamic relationship among carbon dioxide
(CO2) emissions, economic growth and energy consumption based on the environmental
Kuznets curve (EKC) hypothesis for Iran during the period 1971 to 2007. Auto regressive
distributed lag (ARDL) results suggest that the existence of three forms of long-run
relationship among variables when CO2 emissions, economic growth and energy
consumption are the dependent variables. The results do not support the EKC hypothesis
which assumes an inverted U-shaped relationship between income and environmental
degradation. The long-run results indicate energy consumption has a positive and
significant impact on CO2 emissions.
4. Methodology
The data has been collected from the secondary source known as world development
indicators from the year 1972 till 2010 consecutive years. There are basically four variables
included in the model that are agriculture, manufacturing, services and GDP Growth. At first
this study tracing the relationship between variables and to check the effect of independent
variables (agriculture, manufacturing and services) on dependent variable (GDP Growth).
GDP = α+ β1 (Agriculture) + β2 (manufacturing) + β3 (services) +е ………….model 1
Now we consider model 2 to analyse the effects of different determinants on the carbon
dioxide (CO2) emissions.
CO2 = f (GDP, export, Import, fossil fuel energy consumption, energy production, energy
use, electricity power consumption) …………… model 2
From model2, CO2 = f (GDP); Hence, CO2 = f (α+ β1Value added of Agriculture in the GDP
+ β2 Value added of manufacturing industry in the GDP + β3value added of services in the
GDP+е)……..........................model 3
The Econometric Model;
CO2 = α+ β1(Value added of Agriculture in the GDP) + β2(Value added of manufacturing
industry in the GDP) + β3(value added of services in the GDP) + β4(export) +β5(Import)
+β6(fossilfuel energy consumption)+ β7(energy production) + β8 (energy use) + β9
(electricity power consumption)+е
Hypothesis test
: When share of the Services sector‟s value added (VA) in the GDP is higher, CO2
emissions decrease.
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: When share of the Services sector‟s value added (VA) in the GDP is higher, CO2
emissions does not decrease.
The basic empirical analysis of the EKC starts from an estimate of (a variation of)
CO2 = α+ β1GDP + β2 GDP2 + β3 GDP3 +е… … … … … … … … … … … (1)
The EKC hypothesis requires that β1> 0, β2< 0 and β3 = 0.
The income level at which environmental degradation begins to decline is called income
turning point (ITP). The ITP of an EKC is obtained by setting the first derivation (with
respect to income) of equation (1) equal to zero and solved for income; this yields –
β1/2β2.With β1> 0, β2< 0 and β3> 0 an N-shaped pattern is obtained, i.e. there is a second
turning point, after which the environmental degradation rises again with increasing income.
5. Empirical Results
5.1 Effects of GDP Growth on CO2 Emissions
In the regression analysis of Bangladesh India, Nepal and Sri Lanka, it is observed that
GDP growth has strong positive influence on CO2 emissions that is highly significant in the
regression model. Value added of agriculture in the GDP is negatively associated with CO2
emissions and Value added of industry and services in the GDP are positively correlated
with CO2 emissions that are all highly significant.
5.2 Effects of Determinants on CO2 Emissions
For above mentioned south Asian countries, The explanatory variables; Exports and
Imports of goods and services, Fossil fuel energy consumption, Energy Production, Energy
Use and Electric power consumption are positively correlated with CO2 emissions which
are all statistically significant.
5.3 Effects of Value Added of GDP on CO2 Emissions
Value added of industry and services in GDP is positively correlated with Exports, Imports
of goods and services, Fossil fuel energy consumption, Energy Production, Energy Use
and Electric power consumption but Value added of agriculture in GDP is negatively
correlated with these factors that are also highly significant for all selected south Asian
countries.
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5.4 Description of Variables
Table1: Definition of study variables
Description
CO2 emissions (metric tons per capita)
GDP per capita (current US$)
Square of GDP per capita
Cube of GDP per capita
Share of the Agriculture sector‟s value added (VA) in
theGDP (% of GDP)
Ind_%GDP
Share of the Industry sector‟s value added (VA) in
theGDP (% of GDP)
Serv_%GDP
Share of the Services sector‟s value added (VA) in
theGDP (% of GDP)
Export
Exports of goods and services (% of GDP)
Import
Imports of goods and services (% of GDP)
Fossil
Fossil fuel energy consumption (% of total)
Energy Production Energy production (kt of oil equivalent)
Energy Use
Energy use (kt of oil equivalent)
Electricity power
Electric power consumption (kWh per capita)
consumption
Variable
CO2
GDP
GDP2
GDP3
Agri_%GDP
5.5 Regression Analysis
5.5.1 Regression Analysis of Bangladesh
5.5.1.1 Effects of Determinants on CO2 Emissions in Bangladesh
From the econometric model of Bangladesh, it shows the following relation;
CO2 = α -.006 (Value added of Agriculture in the GDP) + .014 (Value added of
manufacturing industry in the GDP) + .009 (value added of services in the GDP) +
.018(export) +.018 (Import) + .006(fossil) + 0.000015 (energy production) +
0.0000126(energy use) + .001(electricity power consumption)+е
5.5.1.2 Effects of Value Added of GDP on CO2 Emissions in Bangladesh
Agri_%GDP = α – 1.947(export)- 2.077 (Import) -.738 (fossil) -.002 (energy production) .001(energy use) – .140 (electricity power consumption)+е
Ind_%GDP =α + .785(export) +.811 (Import) + .292(fossil) + .001 (energy production) +
.001(energy use) + .053(electricity power consumption) +е
Serv_%GDP =α + 1.162 (export) +1.266 (Import) + .446 (fossil) + .001 (energy production)
+ .001(energy use) + .087(electricity power consumption) +е
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The table of regression analysis of Bangladesh is given below;
Table 2: Regression analysis of Bangladesh (Panel A)
Country: Bangladesh
Estimators Model
GDP
Agri_% Ind_% Serv_% Export
Import Fossil
Summary
GDP
GDP
GDP
CO2
R
.947
.801
.785
.786
.939
.845
.958
R-Square
.896
.642
.616
.617
.881
.714
.919
B
.001
-.006
.014
.009
.018
.018
.006
Sig
.000
.000
.000
.000
.000
.000
.000
Agri_%
R
.728
.722
.670
.888
GDP
R-Square
.530
.521
.450
.789
B
-8.467
-1.947 -2.077 -.738
Sig
.000
.000
.000
.000
Ind_%
R
.702
.738
.664
.892
GDP
R-Square
.493
.545
.441
.796
B
20.706
.785
.811
.292
Sig
.000
.000
.000
.000
Serv_%
R
.721
.688
.653
.857
GDP
R-Square
.519
.474
.426
.734
B
13.388
1.162
1.266
.446
Sig
.000
.000
.000
.000
Table 2: Regression analysis of Bangladesh (Panel B)
Country: Bangladesh
Estimators Model
Energy
Energy Use Electricity
Summary Production
power
consumption
CO2
R
.994
.995
.984
R-Square
.987
.989
.968
B
1.528E-005
1.260E-005
.001
Sig
.000
.000
.000
Agri_%
R
.803
.804
.729
GDP
R-Square
.644
.646
.532
B
-.002
-.001
-.140
Sig
.000
.000
.000
Ind_%
R
.782
.790
.705
GDP
R-Square
.611
.624
.496
B
.001
.001
.053
Sig
.000
.000
.000
Serv_%
R
.790
.786
.721
GDP
R-Square
.624
.619
.520
B
.001
.001
.087
Sig
.000
.000
.000
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5.5.1.3 Environment Kuznets Curve for CO2 Emissions and GDP in Bangladesh
The graphical representation of the relation between GDP per capita and CO2 emissions
(metric tons per capita) in figure 1 shows that an inverted “U” shape of the EKC does not
hold since emissions rather have an upward trend. It is seen that N-shaped pattern is
obtained as CO2 emissions increases again with increasing income.
Figure 3: Carbon dioxide emissions and GDP in Bangladesh, 1972-2010
CO2 emissions and GDP per capita in
Bangladesh, 1972-2010
0.4
0.35
0.3
CO2
0.25
Emissions
0.2
(metric tons
per capita) 0.15
0.1
0.05
0
CO2
0
200000000
400000000
600000000
GDP per capita (current US$)
The above graphical representation of CO2 emissions and GDP per capita in Bangladesh
for the year of 1972 to 2010 reveals thatCO2 emissions accelerate as value added of
industry and services in GDP expansively rising at the same time.
5.5.2 Regression Analysis of India
5.5.2.1 Effects of Determinants on CO2 Emissions in India
From the econometric model of India, it shows the following relation;
CO2 = α -.050 (Value added of Agriculture in the GDP) + .149 (Value added of
manufacturing industry in the GDP) + .067 (value added of services in the GDP) +
.061(export) +.052 (Import) + .031(fossil) + 0.000003434 (energy production) +
0.000002348(energy use) + .002(electricity power consumption) +е
5.5.2.2 Effects of Value Added of GDP on CO2 Emissions in India
Agri_%GDP = α – 1.119(export)- .935 (Import) -.583 (fossil) -0.0000633 (energy
production) - 0.00004253 (energy use) – .044 (electricity power consumption)+е
Ind_%GDP =α + .258(export) +.223 (Import) + .140(fossil) + 0.00001512 (energy
production) + 0.000009787(energy use) + .010(electricity power consumption) +е
Serv_%GDP =α + .861 (export) +.712 (Import) + .443 (fossil) + 0.00004818 (energy
production) + 0.00003275(energy use) + .033(electricity power consumption) +е
.
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The table of regression analysis of India is given below;
Table 3: Regression analysis of India (Panel A)
Country: India
Estimators Model
Summary
CO2
R
R-Square
B
Sig
Agri_%
R
GDP
R-Square
B
Sig
Ind_%
R
GDP
R-Square
B
Sig
Serv_%
R
GDP
R-Square
B
Sig
GDP
.909
.827
.001
.000
.840
.705
-35.385
.000
.721
.520
108.081
.000
.837
.700
46.340
.000
Agri_%
GDP
.957
.915
-.050
.000
Ind_%
GDP
.793
.629
.149
.000
Serv_%
GDP
.964
.930
.067
.000
Export
Import Fossil
.937
.878
.061
.000
.901
.811
-1.119
.000
.739
.546
.258
.000
.911
.829
.861
.000
.926
.857
.052
.000
.884
.782
-.935
.000
.750
.563
.223
.032
.885
.783
.712
.000
.970
.941
.031
.000
.966
.932
-.583
.000
.823
.678
.140
.000
.964
.930
.443
.000
Table 3: Regression analysis of India (Panel B)
Country: India
Estimators
Model
Energy
Energy Use
Electricity
Summary Production
power
consumption
CO2
R
.996
.996
.997
R-Square .992
.992
.995
B
3.434E-006
2.348E-006
.002
Sig
.000
.000
.000
Agri_%
R
.969
.952
.953
GDP
R-Square .939
.906
.909
B
-6.330E-005
-4.253E-005 -.044
Sig
.000
.000
.000
Ind_%
R
.823
.779
.796
GDP
R-Square .677
.607
.633
B
1.512E-005
9.787E-006
.010
Sig
.000
.000
.000
Serv_%
R
.969
.963
.959
GDP
R-Square .939
.927
.919
B
4.818E-005
3.275E-005
.033
Sig
.000
.000
.000
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5.5.2.3 Environment Kuznets Curve for CO2 Emissions and GDP in India
The graphical representation of the relation between GDP per capita and CO2 emissions
(metric tons per capita) in figure 2 shows that an inverted “U” shapes of the EKC does not
hold since emissions rather have a rising trend.
Figure 4: Carbon dioxide emissions and GDP in India, 1972-2010
CO2 emissions and GDP in India,
1972-2010
2
1.5
CO2
emissions
1
(metric tons
per capita)
CO2
0.5
0
0
1E+09
2E+09
3E+09
GDP per capita (current US$)
The above graphical representation of CO2 emissions and GDP per capita in India for the
year of 1972 to 2010 shows that CO2 emissions rises as value added of industry and
services have positive correlation with CO2 emissions and increases with increasing
income.
5.5.3 Regression Analysis of Nepal
5.5.3.1 Effects of Determinants on CO2 Emissions in Nepal
From the econometric model of Nepal, it shows the following relation;
CO2 = α -.003 (Value added of Agriculture in the GDP) + .007 (Value added of
manufacturing industry in the GDP) + .004 (value added of services in the GDP) +
.006(export) +.005 (Import) + .012(fossil) + 0.00002367 (energy production) +
0.00001979(energy use) + .001(electricity power consumption) +е
5.5.3.2 Effects of Value Added of GDP on CO2 Emissions in Nepal
Agri_%GDP = α – 1.423(export) – 1.391 (Import) - 3.107 (fossil) - .007 (energy production)
- .006 (energy use) – .398 (electricity power consumption) +е
Ind_%GDP =α + .699(export) +.459 (Import) + .922(fossil) + .002 (energy production) +
.001(energy use) + .092 (electricity power consumption) +е
Serv_%GDP =α + .724 (export) +.932 (Import) + 2.185 (fossil) + .005 (energy production) +
.004(energy use) + .306(electricity power consumption) +е
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The table of regression analysis of Nepal is given below;
Table 4: Regression analysis of Nepal (Panel A)
Country: Nepal
Estimators Model
Summary
CO2
R
R-Square
B
Sig
Agri_%
R
GDP
R-Square
B
Sig
Ind_%
R
%GDP
R-Square
B
Sig
Serv_%
R
%GDP
R-Square
B
Sig
GDP
.737
.543
.000
.000
.787
.620
-7.254
.000
.404
.163
10.537
.011
.878
.772
11.030
.000
Agri_%
GDP
.891
.794
-.003
.000
Ind_%
GDP
.753
.567
.007
.000
Serv_%
GDP
.852
.726
.004
.000
Export
Import Fossil
.700
.490
.006
.000
.624
.389
-1.423
.000
.868
.754
.699
.000
.432
.187
.724
.006
.902
.813
.005
.000
.931
.867
-1.391
.000
.871
.758
.459
.000
.850
.722
.932
.000
.981
.962
.012
.000
.895
.801
-3.107
.000
.753
.567
.922
.000
.858
.736
2.185
.000
Table 4: Regression analysis of Nepal (Panel B)
Country: Nepal
Estimators Model
Energy
Energy Use Electricity
Summary Production
power
consumption
CO2
R
.897
.916
.889
R-Square .805
.839
.790
B
2.367E-005 1.979E-005 .001
Sig
.000
.000
.000
Agri_%
R
.938
.935
.927
GDP
R-Square .880
.873
.860
B
-.007
-.006
-.398
Sig
.000
.000
.000
Ind_%
R
.625
.631
.604
GDP
R-Square .390
.398
.365
B
.002
.001
.092
Sig
.000
.000
.000
Serv_%
R
.978
.970
.973
GDP
R-Square .956
.941
.946
B
.005
.004
.306
Sig
.000
.000
.000
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5.5.3.3 Environment Kuznets Curve for CO2 Emissions and GDP in Nepal
The graphical representation of the relation between GDP per capita and CO2 emissions
(metric tons per capita) in figure3 shows that an inverted “U” shapes of the EKC does not
hold since emissions going up.
Figure 5: Carbon dioxide emissions and GDP in Nepal, 1972-2010
CO2 emissions and GDP in Nepal,
1972-2010
0.16
0.14
0.12
CO2
0.1
emissions
0.08
(metric tons
per capita) 0.06
0.04
0.02
0
CO2
0
100000000
200000000
300000000
GDP per capita (current US $)
It is seen that CO2 emissions increases with increasing value added of industry and
services in the GDP of Nepal. The trend shows that CO2 emissions sharply increase.
5.5.4 Regression Analysis of Sri Lanka
5.5.4.1 Effects of Determinants on CO2 Emissions in Sri Lanka
From the econometric model of Sri Lanka, it shows the following relation;
CO2 = α -.023 (Value added of Agriculture in the GDP) + .066 (Value added of
manufacturing industry in the GDP) + .027 (value added of services in the GDP) +
.006(export) +.003 (Import) + .019(fossil) + .000 (energy production) + 0.00008009(energy
use) + .001(electricity power consumption) +е
5.5.4.2 Effects of Value Added of GDP on CO2 Emissions in Sri Lanka
Agri_%GDP = α – .181(export) – .122 (Import) - .724 (fossil) - .008 (energy production) .003 (energy use) – .053 (electricity power consumption) +е
Ind_%GDP =α + .030(export) +.047 (Import) + .116(fossil) + .001 (energy production) +
.001(energy use) + .001 (electricity power consumption) +е
Serv_%GDP =α + .151 (export) +.075 (Import) + .608 (fossil) + .007 (energy production) +
.003(energy use) + .044(electricity power consumption) +е
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The table of regression analysis ofSri Lanka is given below;
Table 5: Regression analysis of Sri Lanka (Panel A)
Country: Sri Lanka
Estimators Model
GDP
Agri_% Ind_% Serv_% Export
Summary
GDP
GDP
GDP
CO2
R
.865
.957
.656
.938
.192
R-Square .748
.915
.430
.880
.037
B
.000
-.023
.066
.027
.006
Sig
.000
.000
.000
.000
.242
Agri_%
R
.878
.134
GDP
R-Square .771
.018
B
-74.661
-.181
Sig
.000
.416
Ind_%
R
.664
.094
GDP
R-Square .441
.009
B
235.423
.030
Sig
.000
.571
Serv_%
R
.843
.131
GDP
R-Square .711
.017
B
84.154
.151
Sig
.000
.427
Import Fossil
.133
.018
.003
.419
.124
.015
-.122
.453
.198
.039
.047
.226
.089
.008
.075
.589
.973
.947
.019
.000
.920
.846
-.724
.000
.614
.378
.116
.000
.906
.821
.608
.000
Table 5: Regression analysis of Sri Lanka (Panel B)
Country: Sri Lanka
Estimators Model
Energy
Energy Use
Electricity power
Summary Production
consumption
CO2
R
.822
.956
.949
R-Square .676
.914
.900
B
.000
8.009E-005
.001
Sig
.000
.000
.000
Agri_%
R
.879
.965
.968
GDP
R-Square .772
.931
.938
B
-.008
-.003
-.053
Sig
.000
.000
.000
Ind_%
R
.553
.647
.647
GDP
R-Square .306
.418
.418
B
.001
.001
.001
Sig
.000
.000
.000
Serv_%
R
.875
.950
.944
GDP
R-Square .766
.903
.892
B
.007
.003
.044
Sig
.000
.000
.000
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5.5.4.3 Environment Kuznets Curve for CO2 Emissions and GDP in Sri Lanka
The graphical representation of the relation between GDP per capita and CO2 emissions
(metric tons per capita) in figure 4 shows that an inverted “U” shapes of the EKC does not
hold since emissions have a growing trend.
Figure 6: Carbon dioxide emissions and GDP in Sri Lanka, 1972-2010
CO2 emissions and GDP in Sri Lanka,
1972-2010
0.7
0.6
0.5
CO2
emissions 0.4
(metric tons 0.3
per capita)
CO2
0.2
0.1
0
0
1E+10
2E+10
GDP per capita (current US$)
It is seen that CO2 emissions increases stridently because of growing share of Industry and
services in the total GDP.
From the above regression analysis of Bangladesh, India, Nepal and Sri Lanka, it is
noticeable that value added of services in GDP is positively correlated with CO2 emissions
because export, import, fossil fuel energy consumption, energy production, energy use and
electricity power consumption which are also positively correlated with both value added of
services in GDP and CO2 emissions and statistically significant also. For this interrelation
among the factors ofCO2 emissions and value added of services in GDP, CO2 emissions
increases with the growth of value added of services in GDP. So the null hypothesis is
rejected. Now the study attain the findings that- When share of the Services sector‟s value
added (VA) in the GDP is higher than manufacturing sector, CO2 emissions does not
decrease rather increase. The study do not get any inverted- U shape relation
betweenGDP per capita and CO2 emissions metric tons per capita asCO2 emissions
increases again with rising income.
6. Conclusion
In this study, The EKC hypothesis contradicts in the sense that our south Asian society‟s
moves from the secondary to the tertiary sector (Services) but CO2 emissions does not
decrease rather increases with rising income that is the new findings of the research. This
study finds out that, growth of CO2 emissions linked with not only value added of industry
in GDP but also value added of services in GDP. So the implication of the research is that
staying service economy is not the only way of reducing CO2 emissions because value
added of services in GDP also associated with CO2 emissions.
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There are certain limitations of the study which be taken into account for further studies in
the future, like sample size is small which is only for 39 years and the study cannot include
the data of CO2 emissions from mining and quarrying; manufacturing; construction and
electricity of industrial sector and the data of CO2 emissions by transport, trade, real estate
and other services because of unavailability of data. Besides this study do not include all
associating factors of CO2 emissions. However, this paper basically investigates the impact
of principal categories of GDP (Agriculture, Industry and Services) on CO2 emissions.
References
Akbostanci, E.S, Turut-Asik, G.I and Tunc2009, „The relationship between income and
environment in Turkey: Is there an environmental Kuznets curve?‟ , Energy Policy,
Vol. 37, PP. 861-867.
Ang, JB 2008, „Economic development, pollutant emissions and energy consumption in
Malaysia‟, Journal of Policy Modeling, Vol.30, PP. 271-278.
Galeotti,Manera and Lanza 2009,„On the Robustness of Robustness Checks of the
Environmental Kuznets curve Hypothesis‟, Environmental and Resource
Economics,Vol. 42, PP. 551-574.
Halicioglu, F2009, „An econometric study of CO2 emissions, energy consumption, income
and foreign trade in Turkey‟, Energy Policy, Vol. 37, PP. 1156-1164.
Jalil, A and Mahmud, S. F 2009, „Environment Kuznets curve for CO2 emissions: a
cointegrationanalysis‟,Energy Policy, Vol. 37, PP. 5167-5172.
Lean, H. H and Smyth, R 2010, „CO2 emissions, electricity consumption and output in
ASEAN‟,Applied Energy, Vol. 87, PP. 1858-1864.
Saboori, B and Soleymani, A 2011, „CO2 emissions, economic growth and energy
consumption in Iran: A co-integration approach‟, International Journal of
Environmental Sciences, Vol. 2, no.1, PP. 44-53.
54
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Appendix
Table 6: Descriptive Statistics of Study Variables
Variable
CO2 emissions
(metric tons
per capita)
Country
Bangladesh
India
Nepal
Sri Lanka
GDP per capita Bangladesh
(current US$)
India
Nepal
Sri Lanka
Agriculture,
Bangladesh
value added
India
(% of GDP)
Nepal
Sri Lanka
Industry, value Bangladesh
added (% of
India
GDP)
Nepal
Sri Lanka
Services, etc.,
Bangladesh
value added
India
(% of GDP)
Nepal
Sri Lanka
Exports of
Bangladesh
goods and
India
services (% of
Nepal
GDP)
Sri Lanka
Imports of
Bangladesh
goods and
India
services (% of
Nepal
GDP)
Sri Lanka
Fossil fuel
Bangladesh
energy
India
consumption
Nepal
(% of total)
Sri Lanka
Energy
Bangladesh
production (kt
India
of oil
Nepal
equivalent)
Sri Lanka
Energy use (kt Bangladesh
of oil
India
equivalent)
Nepal
Sri Lanka
Electric power
Bangladesh
consumption
India
(kWh per
Nepal
capita)
Sri Lanka
Obs.
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
39
Mean
.1682
.8659
.0744
.3608
307.6577
439.1145
218.6309
710.5923
32.2254
28.4534
49.5813
22.9917
21.6090
25.5797
16.0717
27.2965
46.1656
45.9669
34.3470
49.7116
9.4212
10.1775
14.1742
30.3559
16.4242
11.7622
24.5446
39.3807
46.7251
54.9306
6.5676
33.2703
12122.6854
300714.0291
5910.4471
4046.5752
14694.5403
356102.1973
6399.7412
6255.9507
77.1416
297.0791
41.9043
202.5330
Std.Dev
.09031
.37102
.04288
.15610
148.99130
296.41993
113.35224
548.11269
12.80760
7.03266
12.29963
6.44410
5.05005
1.97753
4.34140
1.54648
8.01884
5.35216
9.02690
5.49111
4.75033
5.65944
5.39465
4.76382
4.13485
6.65287
8.23481
6.50946
15.40714
11.64290
3.54356
8.18179
5872.95707
107639.9367
1624.84785
738.23688
7130.89562
157369.4019
1984.10564
1863.54282
66.51648
154.03289
28.66711
118.47195
Minimum
.05
.38
.02
.20
91.49
125.20
78.88
192.61
17.81
17.74
32.73
11.34
6.06
20.16
8.18
24.20
26.43
36.53
20.07
40.64
2.90
3.96
5.45
21.33
8.10
3.64
7.92
23.90
20.71
37.17
1.92
23.54
5034.91
145862.11
3676.67
2811.89
5872.83
160128.83
3758.48
3927.61
10.33
100.35
6.00
64.92
Maximum
.37
1.67
.15
.63
762.80
1417.07
595.77
2400.02
61.95
43.31
71.76
33.16
27.22
29.03
22.92
30.64
56.05
54.64
49.94
58.84
17.66
23.60
26.33
39.02
24.96
28.67
37.71
54.80
71.41
72.47
12.65
46.37
25759.94
531303.82
8877.77
5543.98
30755.83
723743.18
10218.36
9844.50
247.44
641.27
103.44
449.23
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Table 7: Correlations among study variables of Bangladesh
Country: Bangladesh
Correlations
CO2
CO2
GDP
Agri_%GDP
Ind_%GDP
Serv_%GDP
Exports
Imports
Fossil
Energy
production
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
GDP Agri_% Ind_% Serv_% Exports Imports Fossil
GDP
GDP GDP
Energy
production
Energy
use
Electric
power
consumption
1
.947**
1
.000
-.801** -.728** 1
.000
.000
.785** .702** -.968**
1
.000
.000
.000
.786** .721** -.987** .917**
.000
.000
.000 .000
.939** .835** -.722** .738**
.000
.000
.000 .000
.845** .762** -.670** .664**
.000
.000
.000 .000
.958** .880** -.888** .892**
.000
.000
.000 .000
.994** .956** -.803** .782**
.000
.000
.000 .000
.995** .950** -.804** .790**
.000
.000
.000 .000
.984** .958** -.729** .705**
Energy use
Electric
power
Sig. (2-tailed)
.000
.000
consumption
**. Correlation is significant at the 0.01 level (2-tailed).
.000
.000
1
.688**
.000
.653**
.000
.857**
.000
.790**
.000
.786**
.000
.721**
.899**
.000
.923**
.000
.935**
.000
.947**
.000
.924**
.000
.000
1
1
.802**
1
.000
.839** .957**
.000 .000
.855** .963**
.000 .000
.846** .911**
.000
.000
1
.999**
.000
.988**
.986**
.000
.000
1
1
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Table 8: Correlations among study variables of India
Country: India
Correlations
CO2
CO2
GDP
Agri_%GDP
Ind_%GDP
Serv_%GDP
Exports
Imports
Fossil
Energy
production
Energy use
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
GDP Agri_% Ind_% Serv_% Exports Imports Fossil
GDP
GDP GDP
Energy
production
Energy
use
Electric
power
consumption
1
.909**
.000
-.957**
.000
.793**
.000
.964**
.000
.937**
.000
.926**
.000
.970**
.000
.996**
.000
.996**
.000
.997**
1
-.840**
.000
.721**
.000
.837**
.000
.925**
.000
.954**
.000
.801**
.000
.904**
.000
.933**
.000
.913**
Electric
power
Sig. (2-tailed)
.000
.000
consumption
**. Correlation is significant at the 0.01 level (2-tailed).
1
-.889**
.000
-.986**
.000
-.901**
.000
-.884**
.000
-.966**
.000
-.969**
.000
-.952**
.000
-.953**
.798**
.000
.739**
.000
.750**
.000
.823**
.000
.823**
.000
.779**
.000
.796**
.911**
.000
.885**
.000
.964**
.000
.969**
.000
.963**
.000
.959**
.987**
.000
.865**
.000
.930**
.000
.957**
.000
.944**
.000
.000
.000
.000
1
1
1
1
.836**
1
.000
.917** .979**
.000 .000
.947** .954**
.000 .000
.930** .969**
.000
.000
1
.993**
.000
.996**
.996**
.000
.000
1
1
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Table 9: Correlations among study variables of Nepal
Country: Nepal
Correlations
CO2
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
GDP
Sig. (2-tailed)
Agri_%GDP Pearson Correlation
CO2
Sig. (2-tailed)
GDP
1
.737** 1
.000
-.891** -.787** 1
.000
.000
Pearson Correlation
.753** .404*
Sig. (2-tailed)
.000
.011
Pearson Correlation
.852** .878**
Serv_%GDP
Sig. (2-tailed)
.000
.000
Pearson Correlation
.700** .179
Exports
Sig. (2-tailed)
.000
.276
Pearson Correlation
.902** .742**
Imports
Sig. (2-tailed)
.000
.000
Pearson Correlation
.981** .747**
Fossil
Sig. (2-tailed)
.000
.000
Pearson Correlation
.897** .908**
Energy
production Sig. (2-tailed)
.000
.000
Pearson Correlation
.916** .904**
Energy use Sig. (2-tailed)
.000
.000
Electric
Pearson Correlation
.889** .926**
power
Sig. (2-tailed)
.000
.000
consumption
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Ind_%GDP
Agri_% Ind_% Serv_% Exports Imports Fossil Energy
Energy Electric
GDP
GDP GDP
production use
power
consumption
-.830**
.000
-.963**
.000
-.624**
.000
-.931**
.000
-.895**
.000
-.938**
.000
-.935**
.000
-.927**
1
.650**
.000
.868**
.000
.871**
.000
.753**
.000
.625**
.000
.631**
.000
.604**
1
.432**
.006
.850**
.000
.858**
.000
.978**
.000
.970**
.000
.973**
1
.765**
.000
.680**
.000
.447**
.004
.467**
.003
.411**
1
.899**
.000
.862**
.000
.868**
.000
.850**
1
.904**
.000
.924**
.000
.897**
1
.998**
.000
.996**
1
.000
.000
.000
.009
.000
.000
.000
.000
.995**
1
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Table 10: Correlations among study variables of Sri Lanka
Country: Sri Lanka
Correlations
CO2
GDP Agri_%
GDP
Pearson Correlation
1
Sig. (2-tailed)
Pearson Correlation
.865**
1
GDP
Sig. (2-tailed)
.000
Pearson Correlation
-.957** -.878**
Agri_%GDP
Sig. (2-tailed)
.000
.000
Pearson Correlation
.656**
.664**
Ind_%GDP
Sig. (2-tailed)
.000
.000
Pearson Correlation
.938**
.843**
Serv_%GDP
Sig. (2-tailed)
.000
.000
Pearson Correlation
.192
-.106
Exports
Sig. (2-tailed)
.242
.522
Pearson Correlation
.133
-.057
Imports
Sig. (2-tailed)
.419
.729
Pearson Correlation
.973**
.785**
Fossil
Sig. (2-tailed)
.000
.000
Pearson Correlation
.822**
.867**
Energy
production
Sig. (2-tailed)
.000
.000
Pearson Correlation
.956**
.901**
Energy use
Sig. (2-tailed)
.000
.000
Electric
Pearson Correlation
.949**
.936**
power
Sig. (2-tailed)
.000
.000
consumption
**. Correlation is significant at the 0.01 level (2-tailed).
Ind_% Serv_% Exports Imports Fossil
GDP GDP
Energy
production
Energy
use
Electric
power
consumption
CO2
1
-.691**
.000
-.979**
.000
-.134
.416
-.124
.453
-.920**
.000
-.879**
.000
-.965**
.000
-.968**
.529**
.001
.094
.571
.198
.226
.614**
.000
.553**
.000
.647**
.000
.682**
.131
.427
.089
.589
.906**
.000
.875**
.000
.950**
.000
.944**
.693**
.000
.299
.065
.139
.398
.199
.225
.128
.000
.000
.000
.436
1
1
1
1
.208
1
.204
.177 .740**
.280 .000
.173 .911**
.293 .000
.128 .890**
.437
.000
1
.948**
.000
.940**
.989**
.000
.000
1
1
59