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S3H Working Paper Series
Number 08: 2015
Energy Related Carbon Dioxide Emissions in Pakistan:
A Decomposition Analysis Using LMDI
Arslan Khan
Faisal Jamil
September 2015
School of Social Sciences and Humanities (S3H)
National University of Sciences and Technology (NUST)
Sector H-12, Islamabad, Pakistan
S3H Working Paper Series
Faculty Editorial Committee
Dr. Zafar Mahmood (Head)
Dr. Najma Sadiq
Dr. Sehar Un Nisa Hassan
Dr. Lubaba Sadaf
Dr. Samina Naveed
Ms. Nazia Malik
S3H Working Paper Series
Number 08: 2015
Energy Related Carbon Dioxide Emissions in Pakistan:
A Decomposition Analysis Using LMDI
Arslan Khan
Graduate, School of Social Sciences and Humanities, NUST
Faisal Jamil
Assistant Professor, School of Social Sciences and Humanities, NUST
September 2015
School of Social Sciences and Humanities (S3H)
National University of Sciences and Technology (NUST)
Sector H-12, Islamabad, Pakistan
iii
Contents
Abstract…………………………………………………………………………………………...v
1. Introduction…………………………………………………………………………………..1
2. A Survey of Relevant Literature…………………………………………………………….....3
3. Data and Methodology………………………………………………………………………..5
4. Results and Discussion……………………………………………………………………….10
5. Conclusion…………………………………………………………………………………...15
References………………………………………………………………………………………...18
List of Tables
Table 1: Carbon emissions factor of different fuel types. …………………………………….. …..11
Table 2: Results of decomposition analysis of CO2 emissions (Mtons)………………………... …12
Table 3: Results of decomposition analysis in percentages …………………………………...........13
List of Figures
Figure 1: CO2 emissions and energy consumption for 1990-2012. ………………………………..11
Figure 2: CO2 emissions and output level for 1990-2012. …………………………………………11
Figure 3: Results of decomposition analysis. ……………………………………………………...14
v
Abstract
Unprecedented increase in anthropogenic gases in recent decades has led to climatic changes
worldwide. CO2 emissions are the most important factors responsible for greenhouse gases
concentrations. This study decomposes the changes in overall CO2 emissions in Pakistan for the
period 1990-2012 using Log Mean Divisia Index (LMDI). LMDI enables to decompose the changes
in CO2 emissions into five factors namely; activity effect, structural effect, intensity effect, fuel-mix
effect, and emissions factor effect. This paper confirms an upward trend of overall emissions level of
the country during the period. The study finds that activity effect, structural effect and intensity
effect are the three major factors responsible for the changes in overall CO2 emissions in Pakistan
with activity effect as the largest contributor to overall changes in the emissions level. The structure
effect is also adding to CO2 emissions, which indicates that the economic activity is shifting towards
more energy intensive sectors. However, intensity effect has negative sign representing energy
efficiency gains, which indicate good relationship between the economy and environment. The
findings suggest that policy makers should encourage the diversification of the output level towards
more energy efficient sub sectors of the economy.
Key words: Energy consumption, CO2 emissions, decomposition analysis, LMDI, intensity effect.
1
1. Introduction
Climatic changes are one of the most important global issues caused by excessive energy use and
other anthropogenic effects. On one hand, the accumulation of greenhouse gases (GHG) especially
carbon dioxide (CO2) is increasing rapidly because of the process of industrialization and economic
growth. On the other hand, increase in population results in excessive energy use and increasing
demand for food. Therefore, economies are forced to produce more food, which results in
deforestation. Rising deforestation signifies that lesser amounts of GHG will be assimilated by the
ecosystem. Increasing use of fossil fuel and the significant decrease of forests lead to GHG
concentration which in turns leads to greenhouse effect and global warming. Emissions of CO2 have
the highest share (60%) among all the greenhouse gases (Khan et al., 2004).
In February 2005 the first international treaty namely the Kyoto Protocol extends the United
Nations Framework Convention on Climate Change (UNFCCC) commits State Parties to
reduce greenhouse gases emissions with different CO2 reduction responsibilities. Since GHG
emissions are negative externalities, the nations responsible for excessive GHG concentration
impose external costs to other nations. The under-developed countries are more vulnerable because
they cannot fail to adapt and mitigate the external damages. Pakistan is a good example of such
country that contributes only 0.8% in global greenhouse gases and is ranked 135th among all the
countries in terms of its contribution towards emissions, but is facing disproportionately large
consequences of climatic change. Environmental degradation costs about 6% of GDP annually for
Pakistan economy (Khan et al., 2004). The presence of high particulate matters in the air is a serious
problem in most of the urban areas of Pakistan. The environment is deteriorating due to combustion
of fossil fuels and increased motorization.
Although Pakistan contributes only 0.8% to GHG and is ranked 135th among all the countries that
contribute to GHG’s, (Khan et al., 2004) however, climatic changes has affected Pakistan’s economy
quite adversely. Pakistan is considered as the 12th most vulnerable country as far as climatic changes
are concerned. Environmental problems cost about 6% of GDP or about 365 billion rupees for
Pakistan’s economy every given year (Khan et al., 2004). Energy sector is the main factor responsible
for CO2 emissions that contributes 74% in total CO2 emissions globally.
2
Past literature on environmental economics focuses on identifying the relationship between
economic growth and environmental degradation. Many studies develop Environmental Kuznets
Curves (Bryun et al., 1998). In order to identify the nature of environmental problems, the most
relevant approach is the decomposition analysis of total emissions (Ang and Liu, 2007). Various
methods are used for decomposing the CO2 emissions. Most of the early studies decompose the
emissions level using Structural Decomposition Analysis (SDA) technique. However, the residual
term turns out to be significant for the developing countries and the results show biasness which
make it unsuitable for employing SDA for the case of developing countries (Ang and Liu, 2007).
Some recent studies focusing the developing countries use decomposition analyses of CO2 emissions
in order to find out the factors that are responsible for changes in overall emissions level (Paul and
Bhattacharya, 2004). Ang (1997) introduced to index decomposition analysis (IDA), which is a new
method of decomposition analysis called LMDI. The IDA comprises of two techniques i.e.,
Arithmetic Mean Divisia Index (AMDI) and LMDI.
This study focuses on the questions such as; (i) whether increase in CO2 emissions is inevitable as a
result of economic growth? (ii) Can energy intensity be reduced by achieving energy efficiency? (iii)
Will structural change in the economy from traditional to modern sectors affects emissions levels?
The paper attempts to find out the factors responsible for changes in overall CO2 emissions for
Pakistan using LMDI. Decomposition analysis enables us to investigate for different sectors the
factors responsible for changes in overall emissions. It helps in designing policy recommendation in
order to control emissions.
The study finds that emission level shows an overall upward trend during the period 1990-2012. The
most significant factors responsible for this change in the emissions level include the activity effect,
structural effect and intensity effect. Activity and structure effects have a positive sign which shows
that these two effects force the emissions level to increase. While intensity effect has a negative sign
which clearly indicates that this effect decreases the emissions level up to some extent. But the
positive effect of activity and structural effects outweigh the negative intensity effect resulting in an
overall increasing emission level. It suggests that policy makers should encourage the diversification
of the output level towards more energy efficient sub sectors of the economy. It will encourage the
economic activity at the least cost of environmental degradation. Prudent energy pricing policies can
3
help in conservation of energy and environment and also ensure sustainable energy supplies through
energy transition from non-renewable to renewable energy sources.
Rest of the paper is as follows. Section 2 presents a brief review of literature. Section 3 describes the
data and methodology, while the results are discussed in Section 4. Finally, Section 5 concludes the
study and gives some policy implications.
2. A Survey of Relevant Literature
Energy consumption is inevitable for economic growth that is why in developing economies it is
increased rapidly during past few decades. Increase in final energy consumption causes GHG’s
emissions and environmental degradation. There is vast literature that identifies causal relationship
between energy consumption and economic activity with mixed results. Some studies find that
causality runs from energy consumption to economic growth, which implies that energy
conservation may be harmful for economic growth.
Some studies find causality running from economic growth to energy consumption, which suggest
that energy should be conserved (Soytas and Sari, 2007). Very few empirical studies suggest the
neutrality of energy consumption and economic growth. Soytas and Sari (2007) check the causal
relationship between energy consumption and GDP for G7 countries and find that there is causal
relationship between energy consumption and GDP. In countries like Argentina, Italy and Korea the
relationship is running from GDP to energy consumption but for Turkey, France, Germany and
Japan the relationship is opposite and energy consumption is responsible for change in GDP. These
studies assume environment neutrality of energy consumption (Paul and Bhattacharya, 2004).
However, Energy consumption during an economic activity is the main contributor to CO2
emissions.
Stern et al. (1996) study the relationship between economic growth and environmental degradation
and find an inverted U-shaped relationship termed as environment Kuznets curve (EKC). It
suggests that economic growth leads to environmental degradation in the initial stages of economic
growth but in the long run the trend changes and economic growth reduces the environmental
degradation due to efficient energy use. Keeping in view the fact that energy consumption inevitably
results in raising the CO2 emission, decomposition analysis enables to study the reasons for changes
4
in CO2 emissions. Many recent studies use LMDI technique to decompose the changes in CO2
emissions, and conclude that final energy consumption (Activity effect) and energy intensity are the
main factors responsible for changes in CO2 emission level (see for example, Liu et al., 2007;
Akbostanci et al., 2011; Sun et al., 2012; Alves and Mouthinho, 2013).
Nasab et al. (2012), examine factors responsible for changes in CO2 emissions of Iranian industrial
and transport sector and find that the overall activity effect and intensity effect and to some extent
structural effect contribute more significantly to the changes in CO2 emissions. Paul and
Bhattacharya, (2004) decomposes energy related CO2 emissions for Indian economy and shows that
economic growth is the major contributing factor to CO2 emissions for all the major sectors of the
economy. The emission level of the industrial and transport sector in particular, show a rising trend
of emissions level. Although the intensity and fuel-mix affect forces the emissions level to decline
but the activity effect outweighed the intensity and fuel-mix effect and the net result is an overall
increase in emissions level.
Results of studies may be sensitive to the decomposition method employed. Different methods are
used in literature to decompose energy consumption and energy related CO2 emissions during last
two decades. Comparing of LMDI with already existing methods find that LMDI method is
preferred because of its unique properties of holding negative values and zero values and gives a
perfect decomposition (Ang et al., 1998; Ang, 2005). Before 2005, any data having zero values could
not be decomposed using LMDI method. One of the assumptions of the method is that, the data
should not have any zero values. Just by substituting a small positive value in place of zero value we
can use the LMDI method and it will give converging results. As a guide the authors gives a value ∂
of 10-20 for the negative values (Ang and Liu, 2005).
Since the International Energy Agency (IEA) countries uses Laspeyres index method having residual
term. This method cannot be applied to developing countries because the residual term may turn out
to be significant if we used Laspeyres method for developing countries. If in a decomposition
analysis the structure and intensity effect are significant, the residual term may also turns out to be
significant. Hence, many studies suggest that Laspeyres method is not suitable developing
economies (Ang and Liu, 2007).
5
LMDI holds unique property of handling negative and zero values as well as this method is a perfect
decomposition with no residual term. Another important property of this method is that time
reversal test can also be applied on LMDI technique (Ang, 2007). Moreover, LMDI method enables
to decompose the data having negative values such as structural decomposition analysis often has
negative values. But with the passage of time this problem is also solved and now data having
negative values can also be decomposed using LMDI approach (Ang and Liu, 2006). Due to its
enhanced features, LMDI is found most suitable for decomposing energy related CO2 emissions in
Pakistan. Hence we have applied LMDI for decomposing energy related emissions in this study.
3. Data and Methodology
This study attempts to decompose changes in CO2 emissions in Pakistan using LMDI method
developed by Ang (1997). The analysis covers three time periods of different lengths given below:
1. 1990 as base period and 2000 as current period.
2. 2000 as base period and 2012 as current period.
3. 1990 as base period and 2012 as current period.
The purpose of the last step is to see the overall trends of changes in CO2 emissions as well as trends
of different sectors. LMDI method perfectly decomposes emissions and there is no residual term in
this method. The formula for LMDI shows that it is an identity, not an equation which implies that
for a decomposition analysis to be accurate, the left hand side must be equal to the right hand side.
The decomposition analysis of CO2 analysis has five variables on the right hand side that are
responsible for changes in the endogenous variables that are overall CO2 emissions level. Literature
clearly suggests that for developing countries this method is reliable (Ang and Liu, 2007). As
mention in Section 2, LMDI method is simple to formulate and easily interpretable. LMDI method
holds some unique properties of handling negative and zero values and time reversal test can also be
applied on this method.
Log Mean Divisia Index is the weighted sum of relative changes. Ang and Zhang (2000) presented a
survey of index decomposition analysis and explain LMDI decomposition methodology for energy
consumption as well as for environmental issues (CO2). On the other side brief explanation of each
6
and every formula is given in detail for both decomposition analysis of energy consumption as well
as for decomposition analysis of energy related CO2 emissions.
For developing countries the structural effect turns out to be significant and with significant
structural effect the residual term also become significant and hence the results show biasness (Ang
and Liu, 2007). Most of the recent studies are conducted using LMDI technique to decompose the
changes in CO2 emissions as well as to decompose final energy consumption of the economy
(Akbostanci et al., 2011; Nasab et al., 2012).
Since Pakistan is new in this field of study and surprisingly not a single research papers is present on
this topic. We decided to choose the technique which is simple and can easily be understood.
Literature also tells us that this method has a lot of advantages (Handling negative values, Zero
values, no residual term, application of time reversal test and no biasness in results) over the
previous methods and can be preferable over the other methods present in the literature. Changes in
CO2 emissions for Pakistan economy is decomposed into the following five components:
1) Activity effect
2) Structural effect
3) Intensity effect
4) Fuel-mix effect
5) Emission factor effect
Total changes in CO2 emissions are given in Equation (1):
C= ∑ij Q Qi/Q Ei/Qi Eij/Ei Cij/Eij = ∑ij QSi Ii Mij Uij … (1)
Cij is the CO2 emission of sector i from fuel type j. Q is the total activity level of the economy or we
can say Q is the total GDP of the economy. Si= (Qi/Q) is the share of sector i in total economic
activity. Ii = (Ei/Qi) is the intensity effect that is per unit energy consumption of sector i.
Mij = (Eij/Ei) is the fuel mix effect. Fuel mix effect shows that how the economy using the
available fuels. This effect is calculated by dividing the energy consumption of fuel type j of sector i
by overall energy consumption of that sector. Uij = (Cij/Eij) is the CO2 emission effect. This effect
shows that what is the per unit CO2 emission of consuming a specific fuel.
7
LMDI method is further divided into two types. A) Multiplicative Decomposition, B) Additive
Decomposition. In additive LMDI the results show the changes in CO2 emission in absolute
numbers. But in multiplicative LMDI the changes are captured in ratio terms not in absolute
numbers or difference term. In this technique the values of the five effects are multiplied with each
other to get the overall change in CO2 emissions. In this study we use additive decomposition
technique to decompose the changes in CO2 emission level. The general decomposition identity
formula is given as follows:
∑ ∑
… (2)
This is the general form of decomposition identity. In this identity V shows overall change and x1i,
x2i…. xni shows the different effects that are responsible for overall changes. Since this is an identity
not an equation V must be equal to the variables on the right hand side. If we use additive
decomposition analysis, we simply subtract the CO2 emission of base year from current year in order
to get the overall change (V). When we add the variables on the right hand side it will give us exactly
the same amount present on the left hand side of the identity. If the right hand side and left hand
side are not equal it means that there is some problem either in calculation or in data. We are
interested in additive decomposition technique. So we will focus on this specific technique only. The
formula for additive decomposition analysis is given as follows:
∆Vtot = Vt – Vo = ∆Vx1 + ∆Vx2 + … + ∆Vxm … (3)
∆Vtot shows the changes in overall emission level between two time periods. ∆Vx1, ∆Vx2 and so on
represents the various factors that cause changes in total CO2 emission level. In our case of
decomposition of CO2 emissions, there are five variables on the right hand side. The formulas for
each effect are present below. In case of decomposition analysis of final energy consumption, the
variables on the right hand side are only three. The brief explanation of each and every effect and its
formulas are given below.
The general formulae of LMDI decomposition method for the kth term is given by
∆Vxk = ∑i L (Vit, Vi
0) ln (xtkj/xo
kj) = ∑i (Vti – Vo
i/ ln Vti – lnVo
i) ln (xtkj/xokj) … (4)
8
∆Vxk represents the changes in CO2 emission level of sector x from fuel type k. Vit is the emission
level of sector i at time t and Vi0 is the emission level of sector i at time 0. We use the subscript i and
k because we have different types of fuel as well as different types of sectors in an economy.
The general formula for additive decomposition is given by:
∆Ctot = Ct-C0 = ∆Cact + ∆Cstr +∆Cint + ∆Cfuel + ∆Cemf … (5)
∆Cact represents the change in CO2 emission due to economic activity.
∆Cstr represents the change in CO2 emission due to structural changes.
∆Cint represents the change in CO2 emission due to intensity effect.
∆Cfuel represents the change in CO2 emission due to fuel-mix in the economy.
∆Cemf represents the change in CO2 emission due to emission effect.
We calculate each effect on the right hand side of Equation (5) using the formulas given below:
∆Cact =∑ij (Cijt_Cijo / logCijt-logCijo) log (qt/qo) … (5a)
In Equation 5a, Cijt is the CO2 emission arises from fuel type j in sector i and Cijo is the emission
level of same fuel type and of same sector but for time period o. In order to calculate the activity
level, we have to calculate the CO2 emissions arise from different fuel type one by one for all sectors.
Then subtract the emissions of each fuel type from emission level of time t and take logs of both Cijt
and Cijo. Subtract logCijo from logCijt. Divide (Cijt_Cijo by logCijt-logCijo and then multiply the whole term
with log (qt/qo) to get the activity effect. Qt is the Gross domestic product of the economy at time t
and Qo is the gross domestic product at time 0.
∆Cstr = ∑ij (Cijt-Cij
o/logCijt-logCij
0) log (Sit/Si
o) … (5b)
The only difference in Equation 5a and the remaining four equations is the second part of the
equation that is, log (Sit/Si
o). By multiplying log (Sit/Si
o) with (Cijt-Cij
o/logCijt-logCij
0), we will get the
structure effect.
∆Cint = ∑ij (Cijt-Cij
o/logCijt-logCij
0) log (Iit/Ii
0) … (5c)
In Equation 5c, Iit is the energy intensity of sector i at time t. To calculate the Intensity effect we
have to multiply log (Iit/Ii
0) with (Cijt-Cij
o/logCijt-logCij
0).This effect is very important. In this effect we
9
can say that whether the economy is performing well or not. In many countries this effect
contributes more to lower the overall effect. Because Energy intensity for many countries is started
to decline as they move towards better innovations and technologies.
∆Cmix = ∑ij (Cijt-Cij
o/logCijt-logCij
0) log (Mijt/Mij
o) … (5d)
Fuel mix effect is calculated through Equation 5d. Mijt in Equation (5d) is the fuel mix variable and is
calculated by dividing the energy consumption of sector i of fuel type j by energy consumption of
that specific sector (Eij/Ei). (Eij/Ei) shows that how much a specific sector i consumes fuel type j in a
given period. In other words this also shows the share of a specific fuel type in any sector of the
economy. In this study we can see that Pakistan is also moving towards fossil fuels like coal, which
will affect the environment quite badly in the future.
∆Cemf = ∑ij (Cijt-Cij
o/logCijt-logCij
0) log (Uijt-Uij
o) … (5e)
CO2 emission factor is calculated through Equation (5e). Uijt in the above equation equals (Cij
t/Eijt).
In this study the main variables for which data is required are final energy consumption for each
sector of the economy and its output level. Energy consumption data is collected from various issues
of Energy Yearbook and the output data is collected from Pakistan Economic Survey. From Energy
Yearbook, we also collect the energy consumption data of different fuel types for different sectors.
In Pakistan there are four main fuel types that is, oil, gas, electricity and coal. Now in this study we
collect the share of each fuel type in final energy consumption of specific sectors.
In this study we divide the economy into three sectors industrial sector, agriculture sector and
services sector. We divide the economy in such a manner because the sector wise data for energy
consumption and output level is also present in this form. We need the energy consumption data of
different fuel types for each of the sector. In energy year book the data for energy consumption
from different fuel types of these three sectors are present. For each fuel type consumed by these
sectors, we have to calculate the amount of CO2 emissions and this can be calculated using the
energy consumption data. The study decomposes the CO2 emissions of Pakistan because of sector
level data limitation. The data for sub sectors of industry and services is not available.
10
We have to calculate the CO2 emission level for each fuel type because different fuels have different
pollution level. And for each fuel type a formula is present. Care must be taken in data collection and
handling. If one of the data is missing then we cannot decompose accurately. For this reason we
gather the data of each fuel type for each sector and then converted it to CO2 emission by a formula
presented by Intergovernmental panel on climate change (IPCC).
Step 1: Final energy consumption data in tons of oil equivalent (TOE) is collected from Pakistan
Energy Yearbook for three different sectors of the Pakistan economy. Energy produced by electricity
needs special attention. Since electricity is produced by different methods in Pakistan. We have to
calculate the weights of oil and gas in total electricity generation. After calculating the weights we
convert it to CO2 emissions.
Step 2: Now convert this TOE value to a common energy unit called Terra Joule. TOE values are
converted to TJ values by a formula. TJ = TOE*41868/106. We have to convert TOE to terra joule
because terra joule is a common energy unit. The reason for converting the fuel types into terra joule
is that the carbon emissions factor is given in tons per terra joule.
Step 3: After calculating TJ for each fuel type, now we are going to calculate the carbon content.
Carbon content is calculated when multiplying TJ values with CEF (carbon emission factor) for each
fuel type. CEF values are presented in Table 4.1. Each fuel type contain different amount of carbon
content. So we have to multiply the energy unit (TJ) of each fuel type with its own carbon emissions
factor value.
Step 4: Calculating actual carbon emission (ACE). ACE is calculated by multiplying C with global
default value (GDV) for fraction of carbon oxidized.
Step 5: Now we convert ACE into CO2 emission. To convert ACE into CO2 emission we multiply
the values of ACE with (44/12). In this step we calculate the actual CO2 emission.
Step 6: CO2 emission for each fuel type are summed to get the CO2 emission for each specific sector
of Pakistan economy.
4. Results and Discussion
The study decomposes CO2 emissions in Pakistan using the methodology discussed in Section 4. We
calculated the five effects of decomposition analysis using Additive LMDI technique. In order to
analyze the results, first we have to analyze the main data aspects of the estimation technique. In the
first graph we plot energy consumption of Pakistan economy and its estimated CO2 emissions for
11
1990, 2000 and 2012. It is quite obvious that with the increase in final energy consumption the CO2
emissions level also increase.
Table 1: Carbon emission factor of different fuel types
Fuel Type Carbon Emission Factor (C/Tj)
Gasoline 18.9
Kerosene 19.6
Gas/ Diesel Oil 20.2
Residual Fuel Oil 21.1
LPG 17.2
Naphtha 20.0
Refinery Gas 18.2
Coking Coal 25.8
Natural Gas (Dry) 15.3 __________________________________________________________________________________
Source: Inter-Governmental Panel for Climatic Change (IPCC)
Figure 1: CO2 emissions and energy consumption for 1990-2012
1990 2000 2012
1990 2000 2012
Figure 2: CO2 emissions and output level for 1990-2012 Output(Billion Rs)
1990 2000 2012
CO2 (Million Tons)
1990 2000 2012
0
2
4
6
8
10
12
0
2000
4000
6000
8000
0
5
10
15
0 10 20 30 40 50
CO2 Tons Energy(MTOE)
12
There is an upward trend in both the variables. Both the variable in the graph shows same upward
trend which shows that output level and CO2 emissions level has positively correlated to each other.
Table 2 shows changes in overall CO2 emissions level for the period of 1990-2000. Among the five
factors, the activity effect contributes more to the overall change. In the study period of 1990-2000 it
can be seen from the above table that the largest contributor to CO2 emissions is the activity effect.
The second largest contributor to changes in CO2 emissions is Intensity effect. Since the CO2
emission intensity decreases in the study period, the sign of the intensity effect is negative. The third
largest impact for the same time period is the structural effect. But this change is very low almost
negligible if we compare it with other factors. Because of the Power Policy of 1995, the share of
thermal energy is increased from 35% to 65%. With increasing share of fossil fuel in final energy
consumption the fuel-mix effect turns out to be significant and the sign of the fuel-mix effect is
positive. The fifth factor that is emission factor is very low and it has almost a negligible effect on
overall changes in CO2 emissions level.
Table 2: Results of decomposition analysis of CO2 emissions (Mtons)
1990-2000 2000-2012 1990-2012
∆Ctot 1.419 3.779 5.279
∆Cact 2.544 4.337 7.101
∆Cstr -0.018 0.500 0.413
∆Cint -1.543 -1.018 -2.82
∆Cmix 0.436 -0.045 0.592
∆emf 0.000 0.000 0.000
The results of decomposition analysis of CO2 emissions for the period of 2000-2012 are presented in
the third column of Table 2. In this table it can be seen that like activity effect of 1990-2000, the
activity effect for the period 2000-2012 is also contributes more to the overall changes. It can be
seen that the share of activity effect for the period 2000-2012 is even larger than that of 1990-2000.
And this is because of rapid economic growth in this time period. In this period the structural effect
also plays a vital role and has a great impact on changing the overall emissions level. The share of
structural effect increases as compare with the structural effect of (1990-2012). Since Pakistan has
industrial sector which is very energy intensive, it contributes positively to the overall changes in
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CO2 emissions level. Like previous period (1990-2000), the CO2 emissions intensity for this time
period is also diminishing which is a good sign for the economy. The sign of the fuel mix effect is
also negative.
The fourth column of the Table 2 shows the results of different effects of CO2 emissions in absolute
numbers. We can see from the above table that activity effect contributes to a large extent in overall
changes in CO2 emissions for the study period. The second largest factor that contributes to the
changes in emissions level is the intensity effect. The negative sign of intensity effect shows that the
overall energy intensity and CO2 intensity decreases since 1990 which is a good sign for the
economy. We can say that due to improvement in technology and better techniques of productivity
forces the intensity to diminish. The third largest contributor to the changes in emissions level is the
fuel mix effect. The main reason behind the positive sign of the fuel-mix effect is that Pakistan is
now moving towards more pollutant fuel types. The structural effect has a positive sign which shows
that structure of Pakistan economy is changing. Since 2000 Pakistan’s industry flourishes, due to
which the structural effect for that time period turn out to be positive and this positive value
outweighed the negative value of structural effect of time period 1990-2000.
Table 3 shows that activity effect contributes up to 179% to total change in CO2 emissions for the
period of 1990-2000. Intensity effect has the second largest share among all five effects. The third
largest effect is that of the fuel mix effect. The fuel-mix effect has a positive sign because of the
power policy of 1995 in which the share of Thermal energy was increased up to 65% and with
increase in consumption of fossil fuel CO2 emissions also increases. Structural effect contributes only
-1.27% in total change and the fifth effect that is emission factor effect is almost negligible.
Table 3: Results of Decomposition analysis in percentages
1990-2000 2000-2012 1990-2012
%∆Ctot 100 100 100
%∆Cact 179.24 114.76 134.52
%∆Cstr -1.27 13.38 7.83
%∆Cint -108.73 -26.94 -53.58
%∆Cmix 30.75 -1.2 11.228
%∆emf 0.01 0.01 0.001
14
The third column of the table shows the results of decomposition analysis in percentage form for
the time period of 2000-2012. It can be clearly seen from the table that the activity effect of
20002012 is also the major contributor to the overall change in CO2 emissions level. The intensity
effect share in total change is -26.94%. The third largest factor contributes to the change is overall
CO2 emissions is structural effect. The positive sign shows that structural effect contributes to
increase the emissions level for the study period. This is because in this time period Pakistan
economy moves towards rapid industrialization and facing high economic growth. Although energy
intensity is decreasing in the study period, but a structural change towards industrialization increases
the overall CO2 emissions level of Pakistan economy. The fourth factor that is fuel mix effect
contributes only -1.2% in total change. And the emission factor effect is almost negligible like for the
period of 1990-2000.
Figure 3: Results of decomposition analysis (Emissions in Mtons)
In the second bar chart, the scenario is a bit different. We see that structural effect also plays a role
in changing the overall CO2 emissions level. It is quite obvious from the history that in the 90’s
almost every industry remained stagnant that’s why we cannot see any structural effect in this period.
But since 2000 Pakistan faces a good economic growth which causes the final energy consumption
to increase as a result of which the overall CO2 emissions level also increase. During this time period
the economy is shifted towards less polluted fuels, which is captured in the graph. The third bar
shows different effects that contributes to overall CO2 emissions for the period of 1990-2012.
-4000000
-2000000
0
2000000
4000000
6000000
8000000
10000000
1990-200 0 2000-201 2 1990-201 2
CO2
mix
int
str
act
15
The results of our decomposition analysis are consistent with various past studies conducted for
different developing countries. (See, for example, Paul and Bhattacharya, 2004; Reddy and Ray,
2010; Sahu and Narayanan, 2010; Nasab et al., 2012). Most of the developing countries shows similar
trend as far as decomposition analysis is concerned. In our decomposition analysis the main effects
contributes to changes in CO2 emissions are activity effect, structural effect, intensity effect and to
some extent the fuel- mix effect.
5. Conclusion
The study decomposes CO2 emissions in Pakistan for the period 1990-2012 using LMDI method
proposed by Ang and Choi (1997).The analysis also focuses on different fuel types that are used for
energy purposes in the main sectors of the economy including agriculture, industrial and services
sectors. In economy five energy forms are used including petroleum products, natural gas, coal, LPG
and electricity. These fuel types have different emissions level. LMDI method enables us to calculate
different effects that contribute to changes in overall CO2 emissions including activity effect,
structural effect, intensity effect, fuel mix effect and emissions factor effect. The decomposition of
emissions is carried out for each decade that is, separately for 1990-2000 and 2001-2012 as well as
for the whole period of 1990-2012. The purpose of the decomposition for the whole period is to get
clearer picture of different factors that contribute to the accumulated emissions and to analyze its
overall trend.
On the basis of decomposition analysis, we found that the main factor contributing to changes in the
CO2 emissions level is the activity effect. With improving economic growth, CO2 emission also
increases as a result of the increase in final energy consumption. It is observed that the relationship
between economic growth and CO2 emissions is pro cyclical. CO2 emissions increase with the
increase in overall economic activity and decreases with the decline in economic activity.
The second most important factor that contributes to changes in CO2 emissions is the intensity
effect. This effect forces the emission level to decline. This is because the energy intensity of all the
three sectors considered declined with the passage of time. The share of services sector is started to
increase since 2000. Services sector is less energy intensive sector as compare to industrial sector.
With increase in the share of comparatively less energy intensive sector, the CO2 emissions intensity
effect also declined. High negative values of intensity effect indicate that overall energy intensity of
16
all three sectors declined. From this trend of energy intensity we can say that Pakistan’s economy is
becoming energy efficient over the time. Energy intensity decline can be attributed to improvement
in technology and using up-to-date technologies in production processes.
The results suggest that the third major factor that contributes to changes in CO2 emissions level is
the structure effect. In the first phase of the decomposition analysis, we can see a stagnant growth
rate of all three sectors of the economy. With a stagnant economic growth, the structural effect has
low magnitude. But for the time period of 2000-2012 we can see that structure effect has a relatively
higher share in total CO2 emissions. The positive sign of the structure effect shows that the share of
energy intensive industrial sector is increasing. Resultantly, the CO2 emissions also increase.
Therefore it is observed that a structural shift from agriculture sector to industrial and services
sectors increases the final energy consumption of the economy. With increase in final energy
consumption the CO2 emissions also tend to increase.
The fourth effect is the fuel mix effect, which for the period of 2000-2012 is negative. This can be
attributed to rapid increase in natural gas consumption during 2000’s. The share of natural gas in
total consumption increases which decreases the CO2 emissions level to great extent. Since vehicular
emissions are the main factor responsible for pollution in urban areas, hence the major factor behind
decrease in CO2 emissions is the replacement of natural gas in household and transport sectors of
the economy. Pakistan is energy scarce country and is facing acute energy shortage. The inefficient
energy use and lack of energy conservation raise the environmental problems that lead to climatic
changes. Pakistan is one among the countries hard hit by the climate change. The findings of this
study have various implications for the energy and environmental policies of the country.
Special attention is needed to introduce energy efficient policy especially in industrial and services
sector of the economy. Energy efficiency could be achieved by introducing technically improved
technologies in all sectors of the economy. With increasing energy efficiency the energy intensity of
all the sectors will decline which will cause the emissions level to reduce to a great extent. The
policies should encourage diversification of the economic activity at the sub sector level. The
diversification should be more inclined towards less energy intensive sub sectors. This will help to
reduce the energy intensity of the economy which in turn reduces the CO2 emissions.
17
Government should also diversify the final energy use. Special focus on energy pricing policies is
required to encourage renewable energy sources which have less carbon emissions factor. This will
help to reduce the overall CO2 emissions intensity of the economy. Recently, Pakistan is facing
energy crisis, on the other hand Pakistan has abundant natural resources like coal. Special attention is
needed to develop clean coal technologies because in near future the consumption of coal will
increase with new projects in line. Although introduction of gas in services sector of the economy
control the emissions level up to some extent but government should also focus on gas price
regulations in order to discourage inefficient gas use. Introducing new technologies in electricity
generation and the introduction of renewable energy sources such as, wind, biomass and solar energy
will decrease the carbon coefficient of electricity generation as well as reducing line losses and
distribution losses. On the other hand, policy should be required to set and implement standards for
end use appliances in order to control the energy consumption. In order to get a clearer picture at
the sub sector level more segregated data is required. Steps should be taken to present the energy
consumption data at sub sectors level. After getting more segregated data, we can decompose the
emissions level of a specific sector. Since services sector is the largest sector contributing to CO2
emissions of Pakistan economy, decomposition analysis of services sector at the sub sector level is
required.
18
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