58
The Journal of Industrial Statistics (2016), 5 (1), 58 - 76
Status of Energy Consumption in the Manufacturing Industry of
Eastern India – A Decomposition Analysis
Gopa Ghosh1, Indian Institute of Engineering Science and Technology, Shibpur, India
Madhumati Dutta, Indian Institute of Engineering Science and Technology, Shibpur,
India
Abstract
In the present economic scenario, India, including each of its states, needs to adopt
appropriate policies that would achieve rapid economic growth while simultaneously
reducing its greenhouse gas emissions for a low carbon future. As manufacturing is a
crucial component of the engine that spurs economic growth, and as it is a major consumer
of energy, this paper aims to identify the core factors that have influenced energy
consumption by the manufacturing industries of the eastern states (Bihar, Chhattisgarh,
Jharkhand, Odisha and West Bengal) of India, in the period 2008-09 to 2012-13 and also
wishes to make an interstate comparison of energy consumption. We conduct an index
decomposition analysis, more specifically the log mean divisia index, to identify key
factors behind the increase in energy consumption. The findings of the paper suggest that
besides energy intensity, level of activity is a major contributing factor. The findings also
suggest that there remains a lot of scope for improving energy related policies with
regard to the manufacturing industries of eastern India.
1.
Introduction
1.1 Preliminary data from the Washington based National Oceanic and Atmospheric
Administration (NOAA)2 show that global average CO2 concentrations were 398.43 ppm3
in April, 2014 and 401.24 ppm in April, 2015. Crossing the CO2 concentration of 400 ppm
could be noted as a significant milestone. According to the Intergovernmental Panel on
Climate Change (IPCC, 2013), between 1951 and 2010 the global mean surface temperature
has increased in the range of 0.5°C to 1.3°C due to greenhouse gas (GHG)4 emissions,
specifically emissions of CO2, which in turn are due to anthropogenic activities. Energy
consumption is one of the major causes of GHG (including CO2) emissions, and therefore
energy efficiency policies would be one of the necessary options among energy policies
for the mitigation of climate change. Again, there exists an inter-linkage between the activity
level and energy consumption of an economy and the Indian manufacturing industry is
one of the key production sectors of the Indian economy - and it is also a major consumer
of energy. According to Energy Balance Statistics (2012), industry was the second largest
energy consuming sector in India in 2010, consuming 37% of total energy. According to
Bhattacharya and Copper (2010), the top six industries in India in terms of energy
consumption are aluminium, cement, fertilizer, iron and steel, paper and glass. India has also
e-mail: [email protected]
http://www.esrl.noaa.gov (last access 20 th July, 2015)
3
ppm = Parts Per Million
4
Six major green house gases (GHGs) are available in the atmosphere and they are Carbon Dioxide
(CO2), Methane (CH4), Nitrous Oxide (N2O), Perfluoro carbons (PFCs), Hydrofluro carbons (HFCs)
and Sulphur Hexafluoride (SF6)
1
2
Status of Energy Consumption in the Manufacturing Industry of Eastern ......
59
made a commitment to reduce the energy intensity of its GDP by 20-25% from 2005 levels
within 2020 (Planning Commission 2011), and several policy measures were launched to
achieve this goal. As a result, the emission intensity of India’s GDP has decreased by 12%
between 2005 and 2010 (Govt. of India 2015). India recently submitted its new climate action
plan on October 1, 2015 to the UN Framework Convention on Climate Change (UNFCCC)
and according to Intended Nationally Determined Contribution (INDC), India wants to
reduce the emissions intensity of its GDP by 33 to 35 % by 2030 from 2005 level (UNFCC
2015, Govt. of India 2015)5. In this context, therefore, it is important to identify the policies
that each state of India should adopt to reduce the consumption of energy in the
manufacturing sector and for that it is necessary to obtain a clear view of the present status
and historical trend of energy consumption at the state level. The Government of India took
already an initiative to enhance energy efficiency as one of the challenges in National
Action Plan on Climate Change (2008) and also different state and local governments have
addressed the goal and therefore, industry’s participation is most important to achieve the
goal. That is why; this study used the last five years (2008-09 to 2012-136) data to obtain the
current scenario of energy use pattern to adopt the future policies that promote our goal to
achieve the low carbon future. Literature (Golovo and Eto, 1997, Bhattacharyya, 2011)
suggested that energy efficiency policies, which are consistent with the economic efficiency,
should be considered for future policy options. Energy consumption may increase or
decrease due to some crucial factors. The change in energy consumption is described
using a decomposition methodology to identify the crucial influencing factors. The present
study focuses on the aggregate manufacturing sector of the eastern India (i.e. Bihar,
Chhattisgarh, Jharkhand, Odisha and West Bengal) to know the status of energy
consumption by these states. The states of eastern India are abundantly blessed with rich
mineral resources, specifically coal which is one of the major sources of energy for
manufacturing industry. In 2014, approximately 80%7 (Jharkhand 27%, Odisha 25%,
Chhattisgarh 17%, West Bengal 10% and Bihar 0.05%) of coal was reserved in eastern India
with respect to all over India. Again, the states of eastern India contributed 20% to the total
emission of CO2 and total carbon could be stored in eastern India is 98829.9 Gg8
(Ramachandra and Shwetmala, 2012). The current paper also wants to make an interstate
comparison of the energy consumption by the manufacturing sector for the time period
2008-09 to 2012-13.
1.2 This paper is arranged in the following manner. The estimation method, specifically,
decomposition method used in this study and the required data for analysis is explained in
Section 2. In Section 3, we present a brief overview of the trends in the industrial output
growth and energy consumption of the aggregate manufacturing industries of the eastern
states of India. The data analysis itself as well as a discussion of the results is presented in
Section 4. In Section 5, we discuss the policies which have already been promoted to
encourage the growth of the manufacturing industries and to reduce energy consumption
in an efficient manner in the eastern states of India. Finally, we add some conclusions in
Section 6.
http://www4.unfccc.int/submissions/INDC/Published%20Documents/India/1/
INDIA%20INDC%20TO%20UNFCCC.pdf (last access on 16th December, 2015)
6
Available up to 2012-13
7
http://coal.nic.in/content/coal-reserves (last access on 4th February, 2016)
8
The percentage of emission data and total carbon storage data have been calculated on the basis of
the data which is available in the paper for the time period 2010.
5
60
2.
The Journal of Industrial Statistics, Vol. 5, No. 1
Estimation Method and Data Coverage
2.0 To design appropriate policies towards efficiency in energy consumption in the
manufacturing sector at the state level, the policymakers need a clear view of the present
status and the historical trend of the energy consumption and that is why, energy demand
analysis is an essential to identify the underlying factors affecting energy demand. There
are various analytical methodologies are available in the literature for energy demand analysis
and three important approaches are (a) simple descriptive analysis, (b) econometric analysis
and (c) decomposition analysis (Bhattacharyya, 2011). The decomposition methods have
been used by Jenne and Cattell (1983), Marlay (1984), Park (1992), Liu, Ang and Ong (1992),
Choi, Ang and Ro (1995), Ang and Zhang (2000), Ang B. W. (2004) and Shahiduzzaman and
Alam (2012) and this paper also uses decomposition analysis to identify the factors that
have influenced changes in energy consumption of manufacturing sector of eastern India
for the time period 2008-09 to 2012-13 and uses the results to design a policy towards
energy efficient and low carbon future. The literature indicates that there are two different
types of methodologies (Hoekstra and Bergh, 2003) available to decompose change of a
variable into their determinant effects. They are structural decomposition analysis (SDA)
and index decomposition analysis (IDA). The index decomposition analysis (IDA) method
has been classified into two groups: methods linked to the Laspeyres index and methods
linked to the Divisia index (Ang and Choi, 1997, Ang, 2004 and Ang, Huang and Mu, 2009).
In the literature of energy decomposition methods, there are three different methods available
for the IDA method linked to the Divisia index - they are the arithmetic mean divisia index
(AMDI) method, the logarithmic mean divisia index method I (LMDI I) and the logarithmic
mean divisia index method II (LMDI II). The above mentioned three methodolgies are
distinguished according to their individual weight function. The AMDI method uses an
arithmetic weight fuction where as the LMDI I and the LMDI II methods use the log mean
weight function (Ang, 2004)9. The method used before 2000 was mainly based on the
Laspeyres Index and AMDI10. Researchers have found that these energy decomposition
methods have an unexplained residual term in the decomposition results and therefore,
recent studies have used other methods to get proper results. According to the available
literature, the LMDI I is perfect in decomposition and most preferred method (Ang, 2004)
and the LMDI I method has been adopted in this study. According to the LMDI I
decomposition technique, the three factors which influence the consumption of energy
are: level of economic activity, economic structure and energy intensity. If economic structure
of manufacturing sector and conservation measures remain the same as in the base year,
then the energy consumption can be changed due to any change in economic activity.
Again, any change in the economic structure of the manufacturing industry can affect the
energy consumption of the sector, when level of the economic activity and the energy
intensity remain same as in the base year. Also, the energy consumption can be changed
due to revision of energy conservation measures when the economic activity and the
structural change are remaining same as in the base year value. Liu, Ang and Ong (1992),
Choi, Ang and Ro (1995), Ray and Reddy (2007), Sahu and Narayanan (2010), Ghosh,
Ang, B. W. (2004). Decomposition Analysis for Policymaking in Energy: Which is Preferred
Method? Energy policy , 1131-1139.
10
See Jenne & Cattell (1983), Marlay (1994), Reitler, et al. (1987), Howarth, et al. (1991), Park
(1992), Sun (1998), Ang, et al. (2002) [mentioned in (Ang, 2004)]
9
Status of Energy Consumption in the Manufacturing Industry of Eastern ......
61
Dasgupta, Ghosh, & Ghosh (2012) and Ghosh, Dasgupta, Ghosh, & Ghosh (2014) have
used the LMDI I methodology to analyze energy demand patterns of the manufacturing
industry sector of Singapore, Korea and India. Mehodi and Aalami (2011) and Nasab,
Aalami, Dahr and Sadeghzadeh (2012) have used the LMDI technique for the transport and
industrial sectors. Besides these studies, Shahibuzzaman and Alam (2012), and Cian,
Schymura, Verdolini and Voigt (2013) have offered a sector wise energy demand analysis of
Australia and the 40 major economies respectively. This study is expected to contribute to
the literature in energy demand by providing useful information about the causal factors of
energy consumption in industrial sector of eastern India. Further there are two additional
reasons for the relevance of this study. Firstly, to become an energy efficient country, India
needs to carry out state wise energy demand analysis. There is a lot of literature available
for India, but nearly nothing at the state level. Secondly, there exists a literature of sector
wise analysis of energy demand rather source wise. Therefore, this study would be a good
contribution to literature that tries to understand energy use pattern.
2.1
Index Decomposition Analysis – Model of Log Mean Divisia Index I (LMDI I)
2.1.1 The following is the LMDI I energy efficiency accounting framework using the IDA
approach (Ang, Mu, & Zhou, 2010).
Assuming an economy is divided into ‘n’ no of energy consuming sectors and the total
energy consumption at period ‘t’ can be expressed as:
=∑=
,
= ∑=
,
.
,
= ∑=
,
.
,
,
Equation 1
Where
E t = total energy consumption for all sectors at the time period‘t’
E i, t = energy consumption of the ith sector at the time period‘t’
Y t = total production level for all sectors at the time period‘t’
Y i,t = production level of the ith sector at the time period‘t’
S i, t = Yi, t /Yt = production share of the ith sector at the time period‘t’
I i,t = E i, t / Y i, t = energy intensity of the ith sector at the time period‘t’
Equation (1) expresses energy consumption in terms of the three basic factors in the IDA
method. Taking into consideration these three factors, the change in energy demand over
time, for example from year 0 to year t, can be theoretically decomposed in the following
additive manner:
E −
0
=
=
+
+
Equation 2
According to LMDI I approach (Ang, 2010, 2012), we have
ΔEOE =∆
ΔESE = ∆
0,
−
0,
−
= ∑ =1
= ∑ =1
ln
,
,
ln
Equation 3
0
,
,0
Equation 4
62
ΔEIE = ∆
The Journal of Industrial Statistics, Vol. 5, No. 1
0,
−
= ∑ =1
,
ln
,
,0
Equation 5
where ΔEOE , ΔESE and ΔEIE are the activity effect (or output effect), structural effect and
efficiency effect (or intensity effect), representing the contribution of (a) the change of
sectoral production level, (b) the change of sectoral composition and (c) the change of
sectoral energy intensity to the change of total energy consumption from the base year (0)
to the current year (t). The above mentioned effects are the key influential factors due to
which energy consumption of manufacturing sector could be affected. Again , is the
logarithmic weighting scheme, specified in the following,
, where L (x, y)=
(y-x)/ln( y/x), xy.
2.1.2 In general terms, the activity or output effect tells us that when the structure of the
ith sector and conservation measures are remaining same as that of the base year, then what
has been changed in the consumption of energy between the final year and the base year,
due to growth in output of that sector only. It can be described by the contribution of a
given sector to the overall gross domestic product (GDP). In our model, this activity factor
is given by Y in the Equation 1 and the Equation 3 captures the activity effect which is used
in LMDI I method. The structural effect talks about what has been changed in the
consumption of energy due to any change in the structure or composition of the ith sector,
when there is no change in activity effect and intensity effect. In other words, the structural
effect is referring to shifts in the mix of products or activities. These shifts can be either
inter sectoral or intra sectoral. This effect could be captured by the change in product
share of the ith sector in the total gross domestic product and the product share would
affect the use of energy consumption of that sector and Equation 1 provides us the product
share, i.e. Si = Yi / Y. Equation 4 gives us the formula of the structural effect which is used in
LMDI I. The intensity effect shows us that the change in the consumption of energy solely
due to change in any conservation measures, keeping the other two effects unchanged. An
intensity effect is the real change in energy efficiency. This intensity factor is related to Ii in
the Equation 1 and the Equation 5 captures the intensity effect which is used in the LMDI
I method.
2.1.3 To execute the above analysis, we need data on total output of the ith manufacturing
unit and fuels consumed in the factory sector by type of fuel in the five states (Bihar,
Chhattishgarh, Jharkhand, Odisha and West Bengal). The required data set has been
obtained from various issues of the Annual Survey of Industries (ASI) for the years 200809 to 2012-13. Also, there are twenty four 2- digit industry groups available in the National
Industrial Classification11 (NIC) of 2008. But all twenty four 2-digit indutry groups are not
operating in the estern states of India. Therfore, data on twenty four 2-digit industry is not
available for the five states and that is why, to maintain similarity among the data of eastern
states of India, we have to exclude 7 industry groups and they are manufacture of wearing
apparel (14), manufacture of leather and related products (15), manufacture of computer,
electronic and optical products (26), manufacture of motor vehicles, trailers and semiNIC is the counterpart of the International Standard Industry Classification (ISIC) for India and the
manufacturing sector of India has been classified into various 2-digit, 3-digit and 4-digit industry
groups.
11
Status of Energy Consumption in the Manufacturing Industry of Eastern ......
63
trailers (29), manufacture of other transport equipment (30), Other manufacturing (32), repair
and installation of machinery and equipment (33). The ASI reports provide state-wise data
on the consumption of major forms of energy, namely, coal, electricity, petroleum products
and other fuels, by the manufacturing industry. Both value in rupees and quantity in
original units are available for coal and electricity, while the data on petroleum products and
other fuels is available in monetary terms. Hence, to maintain consistency in the data set,
this study uses the monetary value (in Lakh) of consumption of all three sources of energy.
Output data is also represented in monetary units (in Lakh). Hence for both output and
energy consumption, we need to calculate real values on the basis of the corresponding
wholesale price indices (WPI) taking base 2004-05 in order to factor out the effect of
inflation.
3.
Trends in Industrial Output Growth and Energy Consumption in the Eastern
States of India
3.1 During 2008-09 to 2012-13, the CAGR12 of manufacturing output was on an average
13% per annum for the eastern states of India and for India; it was 17% per annum. Figure
1 shows the upward trend of output of manufacturing industries of all eastern states of
India and also it shows more or less similar trend of output production. Among all the
states, West Bengal has shown significant improvement in the production of manufacturing
output and the CAGR of manufacturing output of West Bengal was 15% per annum. During
2008-09 to 2009-10 in West Bengal, the manufacturing production has fallen slightly and
then it has started to increase again since 2010-11. As we know that the states of the eastern
India are resource based, therefore, investment in manufacturing industries has been
encouraged. As a result, industrial growth in these states has been facilitated by the growth
of the manufacturing industries. Figure 2 shows that in 2008-09 to 2012-13, West Bengal is
a major contributing state among the states of eastern India and it contributed on an
average 47% to the total production of the eastern states, followed by Odisha (17%),
Chhattisgarh (16%), Jharkhand (13%) and Bihar (7%).
3.2 It has been mentioned at the start that production in the manufacturing industries and
energy consumption are interdependent. The CAGR growth rate of output production of
the eastern states is less than the CAGR growth of energy consumption (Figure 3). The
CAGR was 10% per annum of the production of output of total manufacturing sector
whereas this was made possible by a CAGR of 14% in the total energy consumption (Figure
3). The interdependency could also be captured through energy intensity. Energy intensity
is a ratio of energy consumption to production of output13. This measure shows that how
much energy is required to produce one unit manufacturing output in a country. Figure 4
shows the overall decreasing trend of energy intensity14. It is falling during 2009-10 and
2010-11 and then it has increased slightly and after that the increasing trend has reversed
The compound annual growth rate is calculated by taking the nth root of the total percentage
growth rate, where n is the number of years in the period being considered.
12
(1⁄No of years )
This can be written as follows: CAGR = (Ending Value⁄Begininig Value)
-1
According to conventional definition, a country’s energy intensity is usually defined as energy
consumption per unit of gross domestic product (GDP) (Paul and Bhattacharya, 2004, Jena, 2011 and
https://www.eia.gov/todayinenergy/detail.cfm?id=10191, last access 05th Feb, 2016)
14
See Table 1 for result
13
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The Journal of Industrial Statistics, Vol. 5, No. 1
between 2011-12 and 2012-13. Figure 5 has shown an industry wise comparative analysis of
energy intensity15 and Figure 6 represents a source wise analysis of energy intensity. It can
be identified from the Figure 5 that the energy intensity of selected industries of Bihar and
Chhattisgarh is higher than other three states. The selected industries are textiles (13),
wood and products of wood and cork except furniture (16), paper and paper products (17),
chemicals and chemical industry (20), other non-metallic mineral products (23), basic metals
(24), furniture (31). Figure 6 has shown that the trend of intensity of coal, petroleum and
other fuels has followed a similar pattern. During 2008-09 to 2010-11, it is not possible to
identify a particular trend of the energy intensity but after 2010-11, intensity of coal, petroleum
and other fuels has a stable trend line. The intensity of electricity has been erratic throughout
the period.
3.3 The above discussion gives us a brief overview of pattern of energy consumption of
the manufacturing industries in the eastern states of India and simultaneously indicates
the necessity to identify the core factors behind the increase of energy (coal, electricity,
petroleum and other fuel) consumption more specifically for the manufacturing industries
of the eastern states of India. This objective can be achieved with the help of decomposition
analysis, specifically Index Decomposition Analysis (IDA) and the following section will
discuss the results.
4.
Discussion of Results
4.0 We can now discuss the results and the impact of key factor analysis which have
affected the energy consumption of manufacturing sectors of the eastern states of India,
applying the decomposition methodology with Log Mean Divisia Index I (LMDI I) method
during 2008-09 to 2012-13, with 2008-09 as base year.
4.1
Coal
4.1.1 Among the conventional sources of energy, coal is the most significant source and
coal is mostly used in iron and steel production, cement production, textile industry, fertilizer
industry, alumina refineries, paper manufacturers, and the chemical and pharmaceutical
industries (Indian Chamber of Commerce , 2012 and Qaisar & Ahmad, 2014). According to
Energy Statistics (2015), industry-wise estimates of consumption of coal show that during
2013-14, steel & washery industries consumed 23.13 MT16s of coal followed by cement
industries (11.96 MTs) and paper industries (1.67 MTs). The CAGR of coal consumption
was 28% per annum of the eastern states of India during 2008-09 to 2012-13 and five major
energy intensive industries (basic metal and alloys, chemical and chemical products, paper
and paper products, non-metallic mineral products and textiles) that consumed approximately
97% of the total energy consumption. The average energy intensities of non-metallic
products of Bihar and Chhattisgarh, basic metal and alloys of Jharkhand, paper and paper
products of Odisha have shown a significant sign of energy inefficiency (shown in Figure
7). Now Figure 8 shows the decomposition result of coal consumption of the manufacturing
The energy intensity has been calculated on the basis of the data of average fuel consumed and value
of output for the study time period (2008-09 to 2011-12).
16
MT = Million Tons
15
Status of Energy Consumption in the Manufacturing Industry of Eastern ......
65
industries of the states of eastern India. It shows that structural effect (SE) is one of the
contributing factors to increase coal consumption of Chhattisgarh, Odisha and West Bengal
during the study time period. The coal consumption of Jharkhand and Chhattisgarh mostly
increased due to intensity effect (IE). Except for Jharkhand, the activity effect or output
effect (OE) is not a contributing factor in increasing coal consumption by the eastern
states. The structural effect dominates over the other two effects of Bihar because, as
Figure 8 shows, the structural effect is negative. Therefore, this finding suggests that total
coal consumption has been increased not only due to intensity effect; structural change of
the industries is also important factor.
4.2
Electricity
4.2.1 Another crucial form of conventional energy demand is electricity. According to
Energy Statistics (2015), the estimated electricity consumption increased from 562888 GWh
to 912057 GWh during 2008-09 to 2012-13 with a CAGR of 13% per annum. Of the total
consumption of electricity in 2013-14, industry sector accounted for the largest share
(43.83%), followed by domestic (22.46%), agriculture (18.03%) and commercial sectors
(8.72%) (Energy Statistics, 2015). The CAGR of electricity consumption by industries was
16% per annum of the eastern states of India during 2008-09 to 2012-13 and on an average
86% of the total energy consumption has been consumed by five major energy intensive
industries during the study period. Figure 9 has shown that the eastern states of the India,
except basic metal and alloy and non-metallic industry of Bihar, consumed electricity in an
efficient manner. But we know that less electricity intensity does not imply that output
produced in an efficient manner. Therefore, decomposition analysis is necessary to identify
the reason of increasing of the electricity consumption. Our results show that for the
manufacturing industries, the activity effect dominates total electricity consumption during
the study period in Bihar, Jharkhand, Odisha and West Bengal except Chhattisgarh (Figure
10). The Figure 10 also shows that intensity effect is positive in Chhattisgarh, Odisha and
West Bengal for the whole study period. This suggests that the manufacturing industries
of these states failed to achieve energy efficiency. As this study considered a broad data
set of electricity consumption of the manufacturing industry, it is not possible to identify
the exact reason for the change in electricity consumption via the change of structure in the
all states of eastern India for the time period of our study. Figure 10 shows that Bihar
consumed electricity in a more efficient way compared to other states of eastern India, and
the core contributing factor to increase the electricity consumption is the activity effect.
4.3
Petroleum
4.3.1 The petroleum industry is a major part of the Indian manufacturing sector. During
2008-09 to 2012-13, keeping pace with the trend in economic growth, the consumption of
petroleum products in India has grown with CAGR of 5% per annum. The CAGR of petroleum
consumption by manufacturing industries of the eastern states of India was 19% per annum
and it was quite high during the study period and during the study period, approximately
59% of the total energy consumption has been consumed by five major energy intensive
industries. Energy Intensive industries of Eastern states of India, except Bihar, consumed
petroleum in an efficient manner (Figure 11). Figure 12 has shown the result of decomposition
analysis and the intensity effect is a dominant component of petroleum consumption in
manufacturing industries of Chhattisgarh, Jharkhand and Odisha. West Bengal and Bihar
66
The Journal of Industrial Statistics, Vol. 5, No. 1
consumed petroleum in an efficient manner because the figure has shown that the intensity
effect of both the states was negative for the entire time period. The activity effect is also
a very major contributing factor in increasing petroleum consumption in the eastern Indian
states, except for Chhattisgarh. The result shows that the structural effect does contribute
to the consumption of petroleum in these states, but to a much lesser extent.
4.4
Other Fuels
4.4.1 Besides coal, electricity and petroleum, the manufacturing industries also depend on
other fuels. In this case, the activity effect is one of the core contributing factors in increasing
the consumption of other fuels in all states except Chhattisgarh (Figure 13). The intensity
effect is negative for Bihar, Jharkhand and West Bengal - this means that these states used
the other fuels in an efficient manner. Fuel consumption has increased due to the intensity
effect in Chhattisgarh, but the intensity effect is a minor contributor in Odisha. The structural
effect is not a significant contributor in increasing the consumption of other fuels for
manufacturing industries.
4.5
Summary of the Decomposition Results
4.5.1 This study used the log mean divisia index (LMDI) to identify the core factor of
increasing energy (Coal, Electricity, Petroleum and Others) consumption in the manufacturing
industries of the eastern states of India (Bihar, Chhattisgarh, Jharkhand, Odisha and West
Bengal) and the total change in energy consumption is decomposed into the activity or
output effect, structural effect and intensity effect. The results for the manufacturing sector
of the eastern states of India as obtained using data for 2009-10 to 2012-13 (with as base
year 2008-09) show that the activity effect is the core contributing factor in increasing
energy consumption in all states of eastern India except Chhattisgarh, because the activity
effect is positive for Bihar, Jharkhand, Odisha and West Bengal (Figure 14). Another
contributing factor is the intensity effect, which is positive for Chhattisgarh, Odisha and
West Bengal. But Bihar and Jharkhand used energy in an efficient manner. The structural
effect is a contributing factor in increasing energy consumption in Chhattisgarh and Odisha,
because the effect is positive for these states. The contribution of the structural effect in
case of Jharkhand and West Bengal is not very significant for increasing energy consumption.
Hence the results show that the imposition of different policies related to energy efficiency
did not reduce total energy consumption by the manufacturing industries of the eastern
states of India.
5.
Overview of Existing Policies for Industries of the Eastern States of India
5.1 This study already identified the key forces of increasing energy demand and helps
us to understand the energy use pattern of the manufacturing sector of the eastern states
of India. In this context, we need to review of the existing policies of the sector to
continuously improve of future policies. We know that India faces a dual challenge. As an
emerging economy, India requires rapid economic growth, and at the same time, there is a
pressing need to address climate change. In response, it has already implemented different
policies to address the threats of climate change. Besides the Government of India’s programs
on climate responsible development goals, several state governments and local bodies
Status of Energy Consumption in the Manufacturing Industry of Eastern ......
67
have also introduced various incentive schemes to encourage industrial growth and promote
environment friendly options to produce output in an energy efficient manner. Here we
have mentioned some of the relevant policies which have been already implemented in the
past few years.
5.2 The eastern states of India (Bihar, Chattisgarh, Jharkhand, Odisha and West Bengal)
have also promoted the Renewable Purchase Obligation (RPO) for Industries, but between
2007 and 2010, Bihar and Odisha have failed to achieve it (Confederation of Indian Industry,
2014) and (Pahuja, Pandey, Mandal et.al., 2014). Then again, Capacity Building for Industrial
Pollution Management has been implemented by the state pollution control boards; this
project scales up the cleanup and rehabilitation of polluted sites and facilitates the reduction
of environmental and health risks through technical capacity building. With financial and
technical assistance from the World Bank, West Bengal has begun implementing this
initiative (Confederation of Indian Industry, 2014). The project became effective on October
13, 2010 and will last 5 years. It was envisioned to support the development of a policy,
institutional, and methodological framework for the establishment of the National Program
for Rehabilitation of Polluted Sites (Confederation of Indian Industry, 2014). The existing
literatures (Heinrich Boll Foundation, 2013), (Confederation of Indian Industry, 2014) and
(Pahuja, Pandey, Mandal et.al, 2014) have identified the different programmes which have
already been put in place to control the consumption of energy in the eastern Indian states.
The Bihar Industrial Incentive Policy has been implemented in 2011. According to this
policy, if firms produced energy through non-conventional sources, then 60% of the
expenditure on plant and machinery will be subsidised and this facility will be available to
existing units. However there is no ceiling of expenditure has been fixed for availing this
incentive. The Industrial Policy of Chhattisgarh (2009-14) includes mandatory measures for
safeguarding the environment such as setting up of Effluent Treatment Plants, hazardous
waste management systems, solid waste disposal systems, recycled water utilization etc.
According to the Jharkhand Industrial Policy (2012-2014), rainwater harvesting, recycling
and re-use of waste water shall remain essential for industries. New plants will not be liable
to pay 50% of electricity duty for a period of 10 years. Mega projects (with investment in
fixed assets in excess of Rs 100 crore) will be allowed to have captive power plants, to
generate power from waste heat recovery. Such units will also enjoy 50% exemption from
electricity duty for a period of 5 years. A Comprehensive Project Investment Subsidy
(CPIS) to the tune of 20% will be given for investment made in pollution control equipment
and environment friendly alternative power generation equipment. The Orissa Industrial
Policy (2007) says that to make the current industrialization process sustainable, maximum
emphasis shall be laid on sound environment management practices. With this objective in
mind, the State government among other things is actively promoting investments in new
cement plants based on blast furnace slag and fly ash, which would be available in
abundance due to the large number of steel and power plants coming up in the State. The
West Bengal Industrial and Investment Policy (2013) incentives for industry are administered
by the Industry Department under its benefit schemes. Albeit these various policies pursued
by the Eastern States, the report of the Heinrich Boll Foundation (2013) tells us that
Jharkhand and West Bengal have highly polluted industries, whilst industries of Bihar,
Chhattisgarh and Orissa are moderately and marginally polluted.
68
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Conclusion
6.1 As economic development crucially depends on the manufacturing sector and this
sector is also a major consumer of energy, policy for growth combined with the abatement
of carbon emissions requires a detailed study of this sector. It is found from the study that
on average five energy intensive industries (basic metal and alloys, chemical and chemical
products, paper and paper products, non-metallic mineral products and textiles) consumed
66% of the total energy consumption within the study period. Among the five industries,
the energy intensity of basic metal and alloys non-metallic mineral products is higher than
other three manufacturing industries of Bihar and Chhattisgarh. Therefore, these states
should take energy efficiency policies for these industries.
6.2 To design proper policies for the manufacturing industry of the eastern states of India,
we need to examine the pattern of energy use in manufacturing sector and also to identify
the key factors behind the increase of energy consumption. This identification is carried
out with the help of the log mean divisia index decomposition approach. This method
decomposes the changes in energy consumption into the activity effect, structural effect
and intensity effect. The overall results show that the level of economic activity is the
primary influencing factor, followed by energy intensity, in increasing the consumption of
energy of the manufacturing units of the states of eastern India. On the other hand, the
consumption of energy has not been significantly influenced by the structure of the
manufacturing units.
6.3 A variety of industrial policies have been implemented to reduce energy consumption
in this sector in last few years but results suggested that manufacturing sector of the
eastern India has not produced output in an energy efficient manner and also the policy
makers should not only confine themselves to increasing energy efficiency. They also
have paid little attention to change the structural adjustments across sectors. There is also
sufficient scope for further refinement and improvement of the policies towards the
development of technologies for the industrial sector as the results have shown that level
of activity is one of the most important key factors to increase energy consumption.
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Table 1: Total Output, Fuels Consumed and Intensity of Eastern States of India during
2008-09 to 2012-13 (in Rs. Lakh in Constant Price base year 2004-05)
Year
2008-09
2009-10
2010-11
2011-12
2012-13
Total Output
30975756.56
30562579.50
39445883.41
43874068.06
45710922.53
Fuels Consumed
1860284.80
1854061.60
2068025.60
2350467.90
2320721.70
Intensity
0.060
0.061
0.052
0.054
0.051
Source: Annual Survey of Industries (2008-09 to 2012-13)
Manufacturing Output (in Rs.)
Figure 1: State wise Production of Manufacturing Output (in Rs. Lakh)
25000000
20000000
BIHAR
15000000
Chhattishgarh
10000000
Jharkhand
5000000
Odisha
0
West Bengal
2008-09
2009-10
2010-11
2011-12
2012-13
Year
Source: Authors’ calculation
Figure 2: Percentage of -Output Contribution by the Eastern States of India
Source: Authors’ calculation
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Figure 3: Compound Annual Growth Rate (CAGR) of Output Production and Energy
Consumption of Eastern States of India, 2008-09 to 2012-13
Source: Authors’ calculation
Figure 4: Energy Intensity of Manufacturing Industries of Eastern States of India,
2008-09 to 2012-13
0.062
0.06
0.058
0.056
0.054
0.052
0.05
0.048
0.046
0.044
2008-09
2009-10
2010-11
2011-12
2012-13
Source: Authors’ calculation
Figure 5: Industry wise Energy Intensity of Manufacturing Industries of India, 2008-09
to 2012-13
Source: Authors’ calculation
Status of Energy Consumption in the Manufacturing Industry of Eastern ......
73
Figure 6: Source wise Energy Intensity of Manufacturing Industries of eastern States
of India, 2008-09 to 2012-13
Source: Authors’ calculation
Figure 7: Intensity of Coal of Major Energy Intensive Industries of eastern states of
India
Source: Authors’ calculation
Figure 8: Results of Decomposition Analysis: Coal Consumption (in Rs. Lakh) in the
Manufacturing Industries of the Eastern States of India
West Bengal
Odisha
Jharkhand
Chattishgarh
Bihar
Source: Authors’ calculation
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Figure 9: Intensity of Electricity of Major Energy Intensive Industries of eastern states
of India
Source: Authors’ calculation
Figure 10: Results of Decomposition Analysis: Electricity Consumption (in Rs. Lakh)
in the Manufacturing Industries of the Eastern States of India
West Bengal
Odisha
Jharkhand
Chattishgarh
Bihar
Source: Authors’ calculation
Status of Energy Consumption in the Manufacturing Industry of Eastern ......
75
Figure 11: Intensity of Petroleum of Major Energy Intensive Industries of eastern
states of India
Source: Authors’ calculation
Figure 12: Results of Decomposition Analysis: Petroleum Consumption (in Rs. Lakh)
in the Manufacturing Industries of the Eastern States of India
West Bengal
Odisha
Jharkhand
Chattishgarh
Bihar
Source: Authors’ calculation
Figure 13: Results of Decomposition Analysis: Consumption of other energy (in Rs.
Lakh) in the Manufacturing Industries of the Eastern States of India
West Bengal
Odisha
Jharkhand
Chattishgarh
Bihar
Source: Authors’ calculation
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Figure 14: Results of Decomposition Analysis: Activity, Structural and Intensity Effect
(in Rs. Lakh) of Manufacturing Industries of the Eastern States of India, 2008-09 to
2012-13
West Bengal
Odisha
Jharkhand
Chattishgarh
Bihar
Source: Authors’ calculation