CO2 emissions from household consumption in India

Energy Economics 41 (2014) 90–105

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CO2 emissions from household consumption in India between 1993–94 and 2006–07: A decomposition analysis Aparna Das ⁎, Saikat Kumar Paul Department of Architecture and Regional Planning, Indian Institute of Technology, Kharagpur, West Bengal - 721302, India

a r t i c l e

i n f o

Article history: Received 10 October 2012 Received in revised form 19 October 2013 Accepted 29 October 2013 Available online 11 November 2013 JEL Classification: C67 D12 O53 P28 Q43 Q48 Keywords: Household CO2 emissions Energy input–output analysis Complete decomposition analysis

a b s t r a c t CO2 emission from anthropogenic activities is one of the major causes of global warming. India being an agriculture dependent country, global warming would mean monsoon instability and consequent food scarcity, natural disasters and economic concerns. However with proper policy interventions, CO2 emissions can be controlled. Input–output analysis has been used to estimate direct and indirect CO2 emissions by households for 1993–94, 1998–99, 2003–04 and 2006–07. Complete decomposition analysis of the changes in CO2 emissions between 1993–94 and 2006–07 has been done to identify the causes into pollution, energy intensity, structure, activity and population effects according to broad household consumption categories. Results indicate that activity, structure and population effects are the main causes of increase in CO2 emission from household fuel consumption. To identify the causes at the sectoral level a second decomposition has been done for changes between 2003–04 and 2006–07 to identify the causes in the next stage. Finally alternative energy policy options have been examined for each consumption category to reduce emissions. Combined strategies of technology upgradation, fuel switching and market management in order to reduce CO2 emissions for sectors like Batteries, Other non-electrical machinery, Construction and Electronic equipments (including Television), for which all the effects are positive, need to be adopted. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Earth's atmosphere, especially CO2, is one of the major concerns regarding global warming since it increases the residing time of water vapor in the atmosphere considerably (Andrews and Jelley, 2007). Potentially irreversible changes in global climate are predicted for 2050 (Bolin et al., 1986; Mitchell et al., 1987; Wigley and Schlesinger, 1985). Anthropogenic activities under business-as-usual scenario would lead to a 5 °C increase in global temperature but proper and timely interventions can restrict it within 2 °C (The World Bank, 2010). India being an agriculture dependent country, global warming would mean monsoon instability (Goswami et al., 2006; Mani et al., 2009; Muni Krishna, 2008) for the country leading to consequent food scarcity, natural disasters and economic concerns. Policy interventions, both technological as well as economic can limit emissions of greenhouse gases into the atmosphere. Anthropogenic activities have resulted in enhanced greenhouse effect by 46% (range 38–54%) on account of the energy sector (TERI, 1995). Therefore we need to model the amount of emissions generated by fuel consumption in the economy. Energy and water are important resources that India requires, in order to sustain an 8% growth in GDP in the next 25 years (Planning Commission, 2006). Although energy intensity has declined, ⁎ Corresponding author. Tel.: +91 9874397645. E-mail address: [email protected] (A. Das). 0140-9883/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.eneco.2013.10.019

commercially viable technologies currently available and in use in the developed countries can decrease it further by 20%. This paper estimates fuel consumption of the economy between 1993–94 and 2006–07 using input–output transaction tables. Changes in emissions and decomposition of changes, into its sources, have been analyzed using complete decomposition method. Finally energy policy guidelines have been suggested for sectors that lead to increase of emissions between 2003–04 and 2006–07. The objectives of this paper are: 1. Estimation of direct and indirect CO2 emissions from household consumption of fuel between 1993–94 and 2006–07 using input–output transaction tables. 2. Decomposition of changes in CO2 emission between 1993–94 and 2006–07 into pollution effect, intensity effect, structure effect, activity effect and population effect according to broad consumption categories. 3. Decomposition of changes in CO2 emission between 2003–04 and 2006–07 at the sectoral level and tracing the changes in emissions from each consumption category to the contributing sectors. 4. Estimation of monetary and physical resource saving under energy conservation and fuel substitution scenarios for sectors under concern in 2006–07. 5. Provision of energy policy guidelines pertaining to the consumption categories at the sectoral level.

A. Das, S.K. Paul / Energy Economics 41 (2014) 90–105

2. Background In 1993–94, about 761 Mt of CO2 were emitted by India generated from fuel consumption only. Household consumption of fuel comprised of 12% of these emissions. The rest of the emission was due to fuel consumption in agriculture, manufacturing, power generation, transport and service sectors. By 2006–07, household fuel consumption had increased CO2 emission to 18% (Fig. 1). This is an account of direct energy consumed by households in terms of cooking, lighting of homes and fuel used for privately owned motorized vehicles. A lot of energy is also embodied in goods and services consumed by households. They account for a large share of indirect CO2 emission.

3. Literature review Input–output tables have been used for calculation of CO2 emission from both direct and indirect energy consumption since they portray the transaction of goods and services within the industry as well as in the different sectors of final demand. One of the early applications was for the Australian economy (Common and Salma, 1992). Estimation of structural adjustments necessary to achieve 20% reduction in CO2 emissions over 20 years for Germany and the UK was done by Gay and Proops (1993). Input–output tables have also been used for assessing resource or pollutant embodiments in goods and services on a macroeconomic scale (Lenzen, 1998). Some studies on CO2 emissions using input–output model in the current decade have been done for China (Du et al., 2011; J. Guo et al., 2012; Liang and Zhang, 2011; Liu et al., 2009; S. Guo et al., 2012; Su and Ang, 2010; Xu et al., 2011; Yunfeng and Laike, 2010), Spain (Tarancón Morán and Del Río González, 2007; Zafrilla et al., 2012), Austria (Muñoz and Steininger, 2010) and G-7 countries (Hocaoglu and Karanfil, 2011). Earlier studies have been cited in Tarancón Morán and Del Río González (2012). Certain studies using input–output models have been specific to household consumption. CO2 emissions generated from household consumption only have also been analyzed using input–output analysis. A recent study calculates embedded carbon footprint of Chinese urban households using the model (Fan et al., 2012). It shows that with rising income CO2 emission intensity rises or remains the same for transport, recreation, housing and enjoyment. Input–output model has also been combined with hybrid analysis to calculate household CO2 emissions for Netherlands, UK, Sweden and Norway (Kerkhof et al., 2009). Results show that country characteristics, like energy supply, population density and the availability of district heating, influence variation in household CO2 emissions between and within countries. 1600

CO2 emission in Mt

1400 1200 1000 800 600 400 200 0 1993-94

1998-99

Direct CO2 Emission by Industry

2003-04

2006-07

Household Direct Emission

Fig. 1. Direct CO2 emission by the Indian economy in comparison to direct household energy consumption between 1993–94 and 2006–07. Source: Estimated from input–output analysis of fuel consumption data for 1993–94, 1998–99, 2003–04 and 2006–07.

91

Emission calculations have been done by researchers in India. Parikh and Gokarn (1993) have analyzed CO2 emissions in the Indian economy for 1983–84 and examined the alternative policies to reduce them. Analysis of CO2 emissions from energy consumption using an input– output model for different sectors of the Indian economy in 1990 with projections for 2005 had been done by Murthy et al. (1997). Trends of six greenhouse gases (as mentioned in Kyoto Protocol) and local air pollutant emissions of India for 1985–2005 have been provided by Garg et al. (2006). Their paper considers emissions from all sources and not fuel consumption alone. Analysis of CO2 emission of the Indian economy by producing sectors and due to household final consumption based on input–output table and Social Accounting Matrix for 2003–04, distinguishing 25 sectors and 10 household income classes has been done by Parikh et al. (2009). However, analysis of CO2 emissions from household energy consumption during post reform period in Indian economy between 1993–94 and 2006–07 has not been done yet. Understanding the forces of change over time has best been analyzed through decomposition analysis. The two broad classifications of decomposition analysis are structural decomposition analysis (SDA) and index decomposition analysis (IDA). SDA is based upon input– output transaction tables (IOTT), usually applied for the whole economy and index decomposition analysis (IDA) which does not depend upon IOTT, is mostly applied sectorally. SDA can handle absolute indicators whereas IDA can handle both absolute and intensity indicators Differences between these two methods have been discussed in detail in Su and Ang, 2012a. Applications of decomposition analysis on household emissions which have been undertaken in the current decade have used both IDA (Chung et al., 2011 for Hong Kong; Zhang et al., 2013 for Beijing; Zhao et al., 2012 for China; Fan et al., 2013 for China) and SDA (Cellura et al., 2012 for Italy; Zhu et al., 2012 for China) methods. Decomposition analysis looks into effects of changing one parameter at a time, while keeping the rest unchanged at the base year, along with an interaction effect. The interaction term generates due to combined effects of all the parameters. Different decomposition methods handle the interaction effects differently and hence there is ambiguity in it (Ang and Zhang, 2000; Munksgaard et al., 2000; Sun, 1998). Some researchers have come up with a complete decomposition method in the additive and multiplicative forms, where the residual term is eliminated. The different complete decomposition methods as mentioned in Su and Ang, 2012a are S/S method (Shapley, 1953; Sun, 1998); Dietzenbacher and Los (D&L) method (Dietzenbacher and Los, 1998); Logarithmic Mean Divisia index method, LMD-I (Ang and Liu, 2001; Ang et al., 1998) and LMDI-II (Ang and Choi, 1997; Ang et al., 2003); and the MRCI method (Chung and Rhee, 2001). It was later shown that the method proposed by Sun (1998) and Shapley (1953) are identical (Ang et al., 2003). This approach is also called "ideal" decomposition method because of its time/factor reversal and other properties and is consistent with the "ideal" index used in the index number theory (Su and Ang, 2012a). Between 1970 and 1996 emissions profile was decomposed using Divisia Index to understand the contribution of factors like activity levels, structural changes, energy intensity, fuel mix and fuel quality on changes in aggregate carbon intensity of the economy, taking declining coal quality into consideration (Nag and Parikh, 2000). CO2 emission changes between 1973–74 and 1996–97 were analyzed by Mukhopadhyay (2001) with five factors like variation of industrial added values, changes in CO2 intensity of various industries, changes in technical coefficient, changes in final demand of various industries and total joint effects. An input–output structural decomposition analysis for India revealed petroleum products and electricity as the dominating sectors in CO2 emissions (Mukhopadhyay and Chakraborty, 2002). Emission analysis between 1973–1996 for India using the approximate D&L(1998) method shows eco-efficiency of production, production structure and volume of final demand as the major causes of increase in emissions (Mukhopadhyay and Forssell, 2005; Su and Ang, 2012a). Complete decomposition analysis of emissions from the Indian

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A. Das, S.K. Paul / Energy Economics 41 (2014) 90–105

economy between 1980–1996 reveals economic growth as the largest contributor towards increased emissions (Paul and Bhattacharya, 2004). No studies have been done in analyzing household emissions during 1993–94 and 2006–07 in the post reform period. 4. Methodology In this research paper fuel consumption for households has been calculated for 1993–94, 1998–99, 2003–04 and 2006–07 using sectoral1 data available from input–output transaction tables (IOTT), commercial energy data for the study years, as well as sectoral price indices. Commodity × commodity tables were used for the calculations. Since published commodity × commodity tables for the year 2006–07 were not available, they were calculated from corresponding Absorption Matrix1 and Make Matrix2 which were published by official sources. IOTT obtained from CSO (Central Statistical Organisation) were used for the analysis. During the 90s IOTT comprised of a 115 × 115 sector classification, whereas during 2003–04 and 2006–07 they comprise of a 130 × 130 sector classification. The changes in sectoral classification are due to addition of new sectors to broad categories of food; Animal husbandry and livestock products; Miscellaneous manufacturing; Other transport services and Other services. These sectors have been defined in ‘Sources and Methods’ manuals for IOTT of the respective years.

4.2. Fuel allocation and calculation of CO2 emissions by households Energy data was obtained from official sources which include Ministry of Statistics and Programme Implementation, Government of India (GoI), for Coal and Lignite statistics; Ministry of Petroleum and Natural Gas, GoI, for Crude petroleum, Natural gas and Petroleum product statistics; and Ministry of Power and Central Electricity Authority for Electricity statistics. Monetary flow values in the input–output tables were used to make proportional allocations, wherever physical flows of energy required more sectoral disaggregation than was available in published sources. This hybrid approach of fuel allocation gives better results after sector aggregation (Su and Ang, 2012b; Su et al., 2010). Appendix A gives a detailed description of the method used for allocating fuel consumption flows. According to the basic balance equation based upon input–output model, any energy sector ei is given as, ei ¼

n X

eik þ eiy

ð2Þ

k¼1

where, ei = Total output of energy sector i eik = Intersectoral transaction from energy sector i to k eiy = Sale of energy source of type i to final demand.

4.1. Input–output model and sectoral aggregation The basic structure of an input–output transaction table (Miller and Blair, 1985) is

Therefore, energy consumed (energy of all types aggregated) by the economy is given by,

h i −1 X ¼ ðI−AÞ Y

E¼

ð1Þ

r X

ei ¼

i¼1

where, A = Technical coefficient matrix X = Vector of total output Y = Vector of final demand and, (I − A)−1 is called the Leontief Inverse Matrix. The analysis would have been most accurate if carried on at a disaggregate level (Miller and Blair, 1985). Aggregation helped in allocation of energy flows as energy data available for corresponding years were available on such aggregated basis. Most of the manufacturing sectors were left disaggregated keeping in mind different household consumption categories. In the first round, 1993–94 and 1998–99 tables were aggregated to 89 × 89 tables while 2003–04 and 2006–07 tables were aggregated to 100 × 100 tables to help in fuel allocation. In the second stage the tables were reaggregated to 13 sectors based upon household consumption categories which were defined in the same manner for all the four years. This helped in consumption based emission analysis between 1993–94 and 2006–07. The detailed household consumption categories comprising of its constituent sectors have been provided in Appendix C. Computational detail of sectoral aggregation can be referred to in Miller and Blair, 1985. Sectors that were aggregated include Food crops, Cash crops, Plantation crops, Animal husbandry, Other minerals, Sugar and khandsari, Food products and Cotton textiles. Addition of new industrial and a number of service sectors after 1998–99 have led to changes in constituents of the different household consumption categories from 2003–04 onwards. They have been discussed in the section under indirect emissions from households. 1 Absorption Matrix is a commodity × industry matrix which tabulates the flow of goods and services for Primary, Manufacturing and Service sectors. 2 Make Matrix is an industry × commodity matrix which tabulates the flow of goods and services for the Primary, Manufacturing and Service sectors.

E¼

n X

" r n X X i¼1

# eik þ eiy

ð3Þ

k¼1

E k þ Ey

ð4Þ

k¼1

where, E k is the energy required for intersectoral transaction for producing commodity k and Ey is the energy sold to final demand. Now, Ek ¼ ½Ek =X k X k ;

ð5Þ

or, n X

Ek ¼

k¼1

" n X

Ek =X k

#" n h X

k¼1

−1

ðI−AÞ

#

i kl

l¼1

Yl ;

ð6Þ

substituting the value of Xk from Eq. (1) for the sector k. The matrix A is the total production coefficient matrix as India follows the assumption of competitive imports. Or n X k¼1

Ek ¼

" n X k¼1

#" Rk

# n h i X −1 ðI−AÞ Yl ; l¼1

kl

ð7Þ

where, Rk is the direct energy intensity or energy (aggregate of all energy types measured in units of oil equivalent) required to produce 1Re (in 1993–94 prices) worth of commodity of sector k. Now, direct energy intensity multiplied by the Leontief Inverse matrix gives the total energy intensity for each sector. Let, h i −1 T k ¼ Rk  ðI−AÞ

ð8Þ

A. Das, S.K. Paul / Energy Economics 41 (2014) 90–105

93

The following equations describe the procedure of calculating emissions from fuel consumption figures empirically. From Eq. (2) we get,

or,

E¼

n X

T k Y þ Ey ;

ð9Þ

k¼1

d X

n X

Hdirect þ

n X

H indirect ðd≠eÞ

ð10Þ

Therefore in case of emission, n X ðeik  aik Þ þ C iy

Ci ¼

where, Ci is the CO2 emission from energy sector i, aik is the country specific CO2 emission factor of energy type i consumed by sector k and Ciy is the CO2 emission of energy type i consumed by final demand y. Or, n X

Ci ¼ ¼

T k  ck þ

n X

T e  ce ;

ð11Þ

where, Tk is the total energy intensity of energy E consumed by k energy sectors and Te is the total energy intensity of energy E consumed by e non-energy sectors. ck and ce are the values for Private Final Consumption Expenditure3 (PFCE) of the energy and non-energy sectors in the economy. Hdirect and Hindirect signify direct and indirect energy consumed by households. Next, in order to quantify the energy consumed for each consumption category, the values for Hk (total household energy consumption because of consumption of goods from k sectors) were aggregated over the h consumption categories. Or,

H¼

d X i¼1

Hi þ

g X e¼1

C ik þ C iy

ð15Þ

k¼1

e¼1

k¼1

ð14Þ

k¼1

e¼1

k¼1

d X

eik þ eiy :

k¼1

where, Tk is the Total energy intensity of sector k for consumption of energy E, measured in units of oil equivalents consumed per unit value of output in monetary units. Again, values for total energy intensity are calculated for a base year (1993–94) since the value of Rupee changes from year to year. Total energy consumed by households can be defined as:

H¼

ei ¼

H e þ :::::::::::::::: þ

h X

Hm

ð12Þ

CO2 emissions as a result of consumption of all energy types is given by, C¼

n X

Ck þ Cy

where, Ck is the CO2 emission from energy required for intersectoral transaction for producing commodity and Cy is CO2 emission from the energy sold to final demand Y. Now, Ck ¼

n X ½C k =X k X k þ C y

where, Hi,He,............,Hm are values of total energy consumption by each household consumption category, while i,e,..........,m are the number of sectors comprising the different consumption categories. Emission estimations have been carried out in the next stage. First, data on fuel consumption has been allocated to the input–output tables as discussed in detail in Appendix A. Fuel allocation was then multiplied by respective country specific emission factors detailed in Appendix B to calculate the amount of emissions. Sectoral CO2 emission is estimated following IPCC, 2006 guidelines for National Greenhouse Gas Inventories Tier 2 approach which requires data on sectoral energy consumption and country specific emission factors. The following equation (IPCC, 2006) gives the procedure for calculating the total emissions:

or, n X

" Ck ¼

n X

Bk

#" n h X

k¼1

Fraction of carbon oxidized in the fuel is assumed to be 1. 3 PFCE represents the consumption expenditure of households and non-profit institutions. The methodology adopted to prepare the vector of PFCE is the same as that adopted for NAS. However, to arrive at the sector-wise estimates of PFCE, the item-wise details of PFCE by object for the year 1993–94 available in the NAS have been used along with the output data (at four digit level national industrial classification) from the results of surveys conducted on registered and unregistered manufacturing sectors for the year1993-94. The relevant import/export data obtained from RBI have also been used to arrive at the sectorwise estimates PFCE. (Source: Central Statistical Organisation, 2000).

#

i kl

Y l þ Cy

ð18Þ

where, Bk = Ck/Xk is the direct CO2 emission intensity of sector k for consumption of energy, or, Ck ¼

n X

F k Y þ Cy

ð19Þ

k¼1

where, Fk = Bk × [(I − A)− 1] is the total CO2 emission intensity of sector for consumption of energy, total CO2 emission from households for consumption of energy can be defined as: Q¼

¼

d X

Q direct þ

d X

n X

Q indirect ðd≠eÞ

ð20Þ

e¼1

F k  ck þ

n X

F e  ce ;

ð21Þ

e¼1

k¼1

fuels

ð13Þ

−1

ðI−AÞ

l¼1

k¼1

TotalEmissions GHG;fuel X ¼ Fuel Consumptionfuel  Country Specific Emission FactorGHG;fuel :

ð17Þ

k¼1

k¼1

m¼1

ð16Þ

k¼1

where, Fk is the total CO2 emission intensity of energy consumed by k energy sectors and Fe is the total CO2 emission intensity of energy consumed by e non-energy sectors. ck and ce are the values for PFCE of the energy and non-energy sectors in the economy. In order to quantify the CO2 emissions for each consumption category, the values for Qk were aggregated over the h consumption categories. Or, Q¼

d X i¼1

Qi þ

g X e¼1

Q e þ ::::::::: þ

h X m¼1

Qm

ð22Þ

94

A. Das, S.K. Paul / Energy Economics 41 (2014) 90–105

where, Qi,Qe,.........Qm are values of total CO2 emissions from each household consumption category, while i,e.....,m are the number of sectors comprising the different consumption categories. Input–output tables in India, just like the US and China, follow the assumption of competitive imports and therefore CO2 emissions from household consumption based upon SRIO model (Miller and Blair, 1985) include not only domestic emissions but also embodied emissions that have entered the production chain due to imports. Emission calculations based upon similar assumptions have been done for China by Lin and Sun (2010), Chen and Zhang (2010) and Xu et al. (2011) as cited by Su and Ang (2013). 4.3. The complete decomposition approach The second stage looks into the causes of change in household emissions during 1993–94 and 2006–07 through a decomposition analysis. CO2 emissions from fossil fuel burning and industrial processes were related to Climate Change through the Kaya Identity which expresses the global fossil fuel emissions to four factors like global population, per capita world GDP (world GDP/population), energy intensity of world GDP (energy consumed/GDP generated) and carbon intensity of energy (CO2 emitted/energy consumed). In this model the structure effect has been included which is the composition of household consumption goods. Based upon changing lifestyles and people's preference of one household good over another, the changes in structural composition also make a difference in the household CO2 emissions. Application of input–output analysis helps in calculating total emissions which include both the direct as well as the indirect component. Total household CO2 emissions Q can be evaluated as product of pollution coefficient W, energy intensity coefficient I, structure coefficient S, activity coefficient A and population P. Previous works (Paul and Bhattacharya, 2004) had decomposed total household CO 2 emissions into first four factors of pollution, intensity, structure and activity. This paper looks into population effect as the fifth variable. The annual CO2 emissions from household energy use can be written as: n X

Qt ¼

n X Q i¼1

¼

n X

H c  it  n it  H it cit X cit it

Pt

 Pt

i¼1

W it  I it  Sit Ait  P t

In this paper we follow the complete decomposition method proposed by Sun in 1998. It has the advantages of being complete/perfect (no residuals, Sun, 1998), ideal (time/factor reversal, Su and Ang, 2012a), symmetric (no theoretical assumptions for the factors) and mathematically simple. Shapley decomposition takes an average of n! calculations for each effect (Albrecht et al., 2002) and therefore application of this method is comparatively easier. For an exact decomposition total change in the quantity being decomposed over a certain period is given by sum of its constituting effects. Therefore, change of CO2 emissions in a period [0,t] is given by:

ΔQ ¼ W effect þ I effect þ Seffect þ Aeffect þ P effect :

ð23Þ

i¼1

where n is number of sectors for each consumption category in a household; Qit is CO2 emissions of the ith category at time t; Hit is energy (primary and secondary) consumption of the ith category at time t; cit is private final consumption expenditure of the ith category at time t and Pt is population at time t. The five variables considered for the decomposition are: (a) Pollution coefficient: It is defined by the ratio of CO2 emitted to the amount of energy consumed. It can evaluate fuel quality, fuel substitution and installation of abatement technologies (Paul and Bhattacharya, 2004). A similar ratio of carbon released per unit of oil equivalent consumed is called the carbonization index (Grubler, 1998; Mielnik and Goldemberg, 1999). Wit is pollution coefficient of CO2 emissions of the ith category at time t. It has been used previously for decomposing carbon emissions by Paul and Bhattacharya, 2004. (b) Energy intensity coefficient: It is defined as the ratio of energy consumed to the household consumption expenditure. It varies with choice of energy, efficiency of energy systems and technological choices. Iit is energy intensity coefficient of CO2 emissions of the ith category at time t.

ð24Þ

A sample calculation of the pollution effect (W) is given by:

W effect ¼

cit

i¼1

(c) Structure coefficient: It is defined as the ratio of household consumption expenditure for a particular category to the total household consumption. It varies with changes in socioeconomic structure of the society and consequent lifestyle changes. Sit is structural coefficient (composition of private consumption) of CO2 emissions of the ith category at time. (d) Activity coefficient: It is defined by the ratio of total household consumption expenditure to population. GDP of the economy has been considered as the activity effect as it gives a theoretical quantification of CO2 emissions caused by economic activities (Sun, 1998). In this paper it is defined as per capita expenditure which changes with changes in socio-economy and has influence on lifestyle changes. At is activity coefficient (per capita expenditure) of CO2 emissions at time t. (e) Population: Population figures change every year and they have a direct impact on CO2 emissions. Therefore this is considered as a decomposition variable. It cumulates emissions generated at the per capita level. Pt is population at time t.

 "X  # n n X 1 ðΔI i ÞSi0 A0 P 0 þ I i0 ðΔSi ÞA0 P 0 ðW i ÞI i0 Si0 A0 P 0 þ ðW i Þ þI i0 Si0 ðA0 ÞP 0 þ I i0 Si0 A0 ðΔP Þ 2 i¼1 i¼1  "X  # n 1 ðΔI i ÞðΔSi ÞA0 P 0 þ ðΔI i ÞSi0 ðΔAÞP 0 þ ðΔI i ÞSi0 A0 ðΔP Þ þ ðW i Þ þI i0 ðΔSi ÞðΔAÞP 0 þ I i0 ðΔSi ÞA0 ðΔP Þ þ I i0 Si0 ðΔAÞðΔP Þ 3 i¼1 #  "X n 1 þ ðW i ÞfðΔI i ÞðΔSi ÞðΔAÞP 0 þ I i0 ðΔSi ÞðΔAÞðΔP Þ þ ðΔI i ÞSi0 ðΔAÞðΔP Þg 4 i¼1  X 1 n þ ΔW i ΔI i ΔSi ΔAΔP: 5 i¼1

ð25Þ In order to understand the cumulative effects of temporal changes additive Chaining Decomposition Analysis was carried out to analyze the changes in effects when the entire time period under concern was broken down into the constituent smaller time periods. Therefore, for a time-series data, change in CO2 emissions between two time periods is given by Su and Ang, 2012b,

ΔQ ðt 0 ; :::::::; t k Þ ¼

k k X X ðQ s −Q s−1 Þ ¼ ΔQ ðt s−1 ; t s Þ ¼ ΔQ ðt 0 ; t k Þ: ð26Þ s¼1

s¼1

The change in CO2 emissions ΔQ(ts−1,ts) between time ts−1 to ts, can be decomposed into the following five effects (Su and Ang, 2012b): ΔQ ðt s−1 ; t s Þ ¼ ΔQ pollution ðt s−1 ; t s Þ þ ΔQ intensity ðt s−1 ; t s Þ þ ΔQ structure ðt s−1 ; t s Þ þ ΔQ activity ðt s−1 ; t s Þ þ ΔQ population ðt s−1 ; t s Þ:

ð27Þ

21829

3428

3175

1532

1000 0.05

1

0.00 1998-99

2003-04

2006-07

1600

140 103.41

1200

120 84.893

1000

HSD and LDO consumed in the service sector

800

80 732.26

600

613.77 484.41

400

20

0

0 1993-94

CH4

1998-99

2003-04

Installed capacity(GW)

Fig. 2. Sectoral emissions between 1993–94 and 2006–07. Source: Calculated from data on fuel consumption and emissions.

ΔQ ðt 0 ; :::::::; t k Þ  k  X ΔQ pollution ðt s−1 ; t s Þ þ ΔQ intensity ðt s−1 ; t s Þ þ ΔQ structure ðt s−1 ; t s Þ ¼ þΔQ activity ðt s−1 ; t s Þ þ ΔQ population ðt s−1 ; t s Þ s¼1

ð28Þ ¼ ΔQ pollution ðt 0 ; :::::::; t k Þ þ ΔQ intensity ðt 0 ; :::::::; t k Þ þ ΔQ structure ðt 0 ; :::::::; t k Þ þΔQ activity ðt 0 ; :::::::; t k Þ þ ΔQ population ðt 0 ; :::::::; t k Þ:

ð29Þ where, ΔQ pollution (t 0, .......,t k ) is the chaining pollution effect, ΔQ intensity (t 0, .......,t k ) is the chaining energy intensity effect, ΔQstructure(t0,.......,tk) is the chaining structure effect, and ΔQactivity(t0,.......,tk) is the chaining activity effect. The difference between the results of the non-chaining and chaining decomposition can be calculated as ð30Þ

5. Results and discussions 5.1. Total sectoral emissions Total sectoral emissions in terms of CO2, CH4 and N2O have been calculated. CO2 emissions have increased by 86% during this period. CH4 emissions have also increased (40%) but had decreased between 1998–99 and 2003–04 mainly because of a decrease in usage of petroleum products especially HSD during this period. N2O emissions have

CO2 emission from the power sector (Fig. 4) is almost half the total emissions. CO2 emissions from the electricity sector increased from 380. 67 Mt in 1993–94 to 732.26 Mt in 2006–07. Emissions from usage of coal and lignite itself amount to 78% of CO2 emission in 1993–94 which increased to 86% in 2006–07. Between 1993–94 and 2006–07 total emissions increased by 86% while those from the power sector increased by 92%. Installed capacity increased by 109% over the years, 1992, 1997, 2002 and 2007 (Planning Commission, GoI, 1992, 2002, 2008). 5.3. Total emissions from household consumption Total emissions, including both direct and indirect, from household consumption have been decomposed into pollution, intensity, structure, activity and population effects. There has been a change of 66% (Actual 250.0 200.0

124.4 150.0 62.9

100.0 50.0

TRANSPORT AND SERVICES

MANUFACTURING

ELECTRICITY, GAS AND WATER SUPPLY

104.6 75.7

4.2 MINING

AGRICULTURE

49.1

Total

5.2. Emissions from the electricity sector

188.5

41.4

Power Sector

increased steadily (136%) during this period. Fig. 2 shows trends in sectoral emissions between 1993–94 and 2006–07. Sectoral comparison of CO2e emissions shows a steady increase for agriculture, manufacturing and electricity and gas sectors while there is an alternate increase and decrease for the transport and service sectors (Fig. 3). A comparison of percentage increases in emissions and GDP for these sectors, reveals that emissions from agriculture are higher than its GDP growth, while for other sectors it is not that high. Manufacturing sector has experienced an increase of more than 100% in emissions but with a considerable growth in GDP.

227.6 160.5

2006-07

Fig. 4. CO2 emissions from the power sector in relation to total emissions from the economy (1993–2007) and installed capacity (1992–2007). Source: Calculated from emission data, data on installed capacity form Planning Commission Reports (8th to 11th Plan),

Aggregate changes given by the chaining decomposition are,

Δpollution ¼ ΔQ pollution ðt 0 ; t k Þ−ΔQ pollution ðt 0 ; ::::::::::::; t k Þ:

60 40

380.67

CO2

CO2e Emissions in Mt.

100

69.065

200

HSD and LDO consumed in the transport sector

1000 900 800 700 600 500 400 300 200 100 0

160

144.52

1400

0.0

Percentage increase

1993-94

95

Installed Capacity in GW.

1135

0.10

23870

CO2 Emissions in

32161

23141

CH4 and N2O Emissions in Mt.

CO2 Emissions in Mt.

A. Das, S.K. Paul / Energy Economics 41 (2014) 90–105

Fig. 3. Carbon dioxide equivalent emissions of major sectors between 1993–94 and 2006–07. Source: Calculated from data on fuel consumption and GDP (at 1993–94 prices), from CSO, India.

1993-94 1998-99 2003-04 2006-07 Emissions GDP

96

A. Das, S.K. Paul / Energy Economics 41 (2014) 90–105

200

100

CO2 Emissions in Mt

Pollution Intensity

150

Structure 100 Activity 50

Population Actual

0 Natural gas -50

Fig. 5. Index of total CO2 emissions from household consumption. Source: Calculated from emission data.

change, including direct and indirect) in household CO2 emissions between 1993–94 and 2006–07 (Fig. 5) brought about mainly by the activity effect (76%), population effect (30%) structure effect (27%) and pollution effect (3%). Energy intensity has gone down by 60% during this period. Chaining analysis records higher values for all the effects, especially the pollution effect (higher by 166%). Therefore temporal aggregation reveals the true accumulation of CO2 emissions over time. Although this gives a holistic picture of indirect energy consumption, it is difficult to find out the sectors which are responsible for major changes since sectoral definitions have changed after 1998–99. Analysis with aggregated values would have generated errors while analyzing at the sectoral level. Therefore, to analyze in greater detail a second decomposition analysis was carried out between 2003–04 and 2006–07 so that sectors could be identified which need to be addressed towards reducing CO2 emissions. In the next section, each consumption category has been analyzed for overall changes between 1993–94 and 2006–07, along with identification of sectors that have caused increased emissions between 2003–04 and 2006–07.

5.3.1. Direct emission from household consumption Emissions from usage of primary and secondary energy have increased between 1993–94 and 2006–07. Primary energy usage in terms of coal and lignite and natural gas (negligible usage) has increased by 6%. Contrary to this, secondary energy sectors show an increase in CO2 emission by 171% between 1993–94 and 2006–07. Sharp changes in declining energy intensity and increasing structure effect are also noticed from 2003–04 onwards (Fig. 6). Activity and structure effects are the major contributors towards an increase in emissions from primary and secondary energies (Fig. 7). Rapid urbanization and changing lifestyles have influenced coal substitution with diesel and electricity. LPG as well as electricity is being used for cooking.

PRIMARY ENERGY 200

Electricity

Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actal Change

Fig. 7. Causes of increased CO2 emissions from “direct energy” between 2003–04 and 2006–07. Source: Calculated from respective IOTT and emission data.

Increasing number of private vehicles has led to rise in usage of petrol/motor gasoline for transport. Electricity usage for home illumination and entertainment has also gone up with current trends. Sectoral decomposition analysis between 2003–04 and 2006–07 reveals that emissions are positive for Natural gas, Petroleum products and Electricity. Emissions have reduced for Coal and lignite indicating structural changes towards cleaner fuels. Compared to Natural gas and Petroleum products, pollution effect for electricity is highest at 7%. In these three years emissions have increased by 29% for electricity sector. 5.3.2. Indirect emission from household consumption Indirect emissions have increased by 76% over 1993–94 values. As seen from Fig. 8, falling intensity effect has been guided by activity and population effects towards structural shifts in emissions from energy embodied in goods and services consumed by households. Between 1993–94 and 2006-07 (Fig. 9), results for decomposition of changes reveal that maximum increases have been brought about by consumption categories of “transport” (21%), “recreation” (12%) and “food, beverage, tobacco and primary goods” (20%). Categories of house building (5%) and other personal services (4%) follow next with lower increase in emissions. Medical care and hygiene have led to a decrease in CO2 emissions (2%) during this period (Fig. 9). Sectoral analysis of individual consumption categories reveals the following. 5.3.2.1. Food, beverage, tobacco and primary goods. Emission increase under this category has been caused by activity and population effect (Fig. 10). 2003–04 onwards structure effect is one of the major causes of decline along with intensity and pollution effects indicating that composition of private consumption has changed from food to other goods. In 2003–04, new sectors were added to this category like Other oilseeds, Fruits, Vegetables, and Other crops. In 1998–99 they were all clubbed under Other Crops. Similarly, Poultry and eggs; Other livestock

SECONDARY ENERGY 300 250

150

Petroleum products

-100

20 06 -0 7

20 03 -0 4

19 98 -9 9

19 93 -9 4

0

50

Pollution Effect Intensity Effect

200 100

150

50

100

Structure Effect Activity Effect

50 0

20 06 -0 7

20 03 -0 4

19 98 -9 9

19 93 -9 4

20 06 -0 7

20 03 -0 4

19 98 -9 9

19 93 -9 4

Population Effect

0 -50

-50

Actual change

Fig. 6. Index of total CO2 emissions from household consumption of direct (primary and secondary energy). Source: Calculated from emission data.

A. Das, S.K. Paul / Energy Economics 41 (2014) 90–105

Pollution Effect

180 160

Intensity Effect

140 120

Structure Effect

100 80 60

Activity Effect

40 20

40

CO2 Emissions in Mt

200

30 20 10 0 -10

20 06 -0 7

20 03 -0 4

19 98 -9 9

19 93 -9 4

Actual change

Food crop

Cash crop

Other crops

Beverages

Tobacco products

-20 -30

Population Effect

0

97

Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actual Change

Fig. 11. Causes of increased CO2 emissions from “food, beverage, tobacco & primary goods” between 2003–04 and 2006–07. Source: Calculated from respective IOTT and emission data.

Fig. 8. Index of CO2 emissions from household consumption of indirect energy. Source: Calculated from emission data.

Reduction in energy intensity would bring about positive changes in emissions for the food crop sector. Rise in incomes leading to greater consumption of high value foods and cash crops like sugarcane, groundnut, other oilseeds, jute, cotton and tobacco along with agricultural policies have created economic opportunities for farmers (Fafchamps, 1992; Von Braun and Kennedy, 1986). Changing lifestyles with a strong influence of work environment along with growing social interaction for recreational purposes have increased people's consumption of beverages and tobacco products leading to increased fuel consumption for irrigation and food manufacturing requirements. Structural as well as pollution effect reduction is required for Beverage sector.

CO2 Emissions in Mt

120 100 80 60 40 20 0 -20

Fig. 9. CO2 emissions from different consumption categories between 1993–94 and 2006–07. Source: Calculated from respective IOTT and emission data.

products and gobar gas were included under Animal husbandry previously. Sectoral decomposition analysis for the period between 2003–04 to 2006–07 reveals that Food crops, Cash crops, Other crops, Beverages and Tobacco products are the sectors where emissions are positive. Increasing use of diesel and electricity for farm mechanization and irrigation purposes for Food crops, Tobacco and Other crops (Lal, 2004) along with processing technologies for Beverages and Tobacco products have increased pollution effect for these sectors (Fig. 11).

5.3.2.2. Clothing and footwear. Activity effect is the major cause in increasing emissions between 1993–94 and 2006–07. Sectoral compositions under this category have remained the same from 1993–94. After 2003–04, decline in emissions is engineered by falling intensity and structure effects (Fig. 12). Manufacturing processes have become more energy efficient but people's consumption patterns have shifted away from basic items. Sectors under this category where emissions have increased between 2003–04 and 2006–07 are Silk textiles; Art silk, synthetic fiber textiles; Jute, hemp, mesta textiles; and Leather footwear (Fig. 13). Structure and pollution effects are positive for Silk textiles; and Jute, hemp and mesta textiles. Petrochemical based synthetic fibres, are much more energy intensive than natural fibres and therefore pollution and intensity effects have increased emissions for Art, silk, synthetic fiber textiles. In case of Leather footwear, pollution effect has increased emissions because of processing requirements in the drying stage. 5.3.2.3. Lifestyle effects. Emissions have increased for “lifestyle effects” mainly because of activity effect. This indicates that there is a growing requirement for goods and services from contemporary markets.

FOOD, BEVERAGE, TOBACCO & PRIMARY

CLOTHING AND FOOTWEAR 400

GOODS Pollution Effect

200 150

Pollution Effect

300

Intensity Effect

200

Intensity Effect

Structure Effect

100

Structure Effect

100 Activity Effect 50

Activity Effect

0

Fig. 10. Index of CO2 emissions from “food, beverage, tobacco & primary goods”. Source: Calculated from emission data.

20 06 -0 7

Actual change

20 03 -0 4

1993-94 1998-99 2003-04 2006-07

19 98 -9 9

0

19 93 -9 4

-100 Population Effect

Population Effect Actual change

Fig. 12. Index of CO2 emissions from “clothing and footwear”. Source: Calculated from emission data.

A. Das, S.K. Paul / Energy Economics 41 (2014) 90–105

LIFESTYLE EFFECTS

Silk textiles

Art silk,

Jute, hemp,

Leather

synthetic

mesta textiles

footwear

fiber textiles

200

Pollution Effect

150

Intensity Effect

100

Structure Effect

50

Activity Effect

0

Population Effect

-50

Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actual Change

Fig. 13. Causes of increased CO2 emissions from “clothing and footwear” between 2003–04 and 2006–07. Source: Calculated from respective IOTT and emission data.

Consequently, the need for getting connected to the outside world has also grown over the years. Emissions have increased after 2003–04. After 1998–99, Office computing and accounting machines sector was no longer a separate sector under this category. It was distributed partly between Miscellaneous manufacturing and Communication equipment. Gems and jewelry, which was partly under Miscellaneous manufacturing in 1998–99, was considered as a new sector under this category from 2003–04 (Fig. 14). A separate identity made its inclusion in “lifestyle effects” more appropriate than under “housing and lifestyle”. Decomposition of changes between 2003–04 and 2006–07 reveal that Wood and wood products; Leather and leather products; Rubber products; Batteries; Watches and clocks; Gems and jewelry and Communication are the sectors where emissions have increased. Along with activity and population effects, pollution and structure effects are positive for Wood and wood products; Leather and leather products; Rubber products; Watches and clocks; Gems and jewelry and Communication. Batteries sector is unique for which all the effects are positive. Economic reforms4 have modernized people's way of living and thereby initiated a structural shift towards lifestyle enhancing goods. This has led to a positive component for pollution effect of the sectors contributing to household and other consumer goods (Fig. 15). 5.3.2.4. Education and research. Emissions have increased mainly because of activity effects. Decreases in structure and intensity effects from 1998–99 onwards have been able to check the increasing trends in emissions. However there has been a slight increase after 2003–04. Computer and related activities initially included within Other services under “medical care and hygiene” in 1998–99 was a new addition to this category from 2003–04 onwards (Fig. 16). Decomposition analysis between 2003–04 and 2006–07 reveals Paper, paper products and newsprint; Education & research; and Computer & related activities as sectors with increased emissions (Fig. 17). Structure and pollution effects are positive for Paper, paper products & newsprint; and Computer & related activities. Education & research sector has positive pollution effects although intensity and structure effects have decreased emissions. Growing literacy among people has made people more inclined to spend on goods and services related to education. Therefore, positive structural effect for Paper, paper products & newsprint and Computer & related activities leads to increased emissions. Improved energy efficiency is required for the paper 4 Economic reforms in India started during 1991. Main objective of the government was to initiate a systemic shift to a market oriented open economy, where the private sector including foreign investment played a major role (Ahluwalia, 2002).

20 06 -0 7

20 03 -0 4

Actual change 19 98 -9 9

5 4 3 2 1 0 -1 -2 -3 -4 -5 -6 -7 -8 -9

19 93 -9 4

CO2 Emissions in Mt

98

Fig. 14. Index of CO2 emissions from “lifestyle effects”. Source: Calculated from emission data.

manufacturing sector since developed nations require far less energy for paper production (Schumacher and Sathaye, 1999). 5.3.2.5. Medical care and hygiene. “Medical care and hygiene” is the only category where emissions have reduced mainly because of intensity effects. Consumption of medicines had gone up till 1998–99 but after that there has been a structural shift away from this category indicating improving health conditions. Energy intensity declined majorly after 1998–99 but has slightly increased from 2003–04 onwards. Pollution effect also increased from 2003–04 (Fig. 18). In 1998–99 the category constituted a sector called Other services which is a combination of all personal services. However with growth of the service sector in 2003–04, new service sectors were added and the new constituent was Other commercial, social and personal services to account for similarity between 1998–99 and 2003–04. After 2003–04, increase in emissions has been brought about by Soaps, cosmetics & glycerin and Medical & health services (Fig. 19). Lifestyle changes indicate a positive structural effect for these two sectors. 5.3.2.6. House building. “House building” is the only category where intensity effect has increased and is the major reason for increased emissions (Fig. 20). Increasing use of electricity for industrial processes has contributed towards this positive intensity effect from 1998–99 onwards. However pollution effect declined after 1993–94 because of coal substitution by diesel and electricity but has been increasing after 2003–04. Real estate activities was a new sector added to this category in 2003–04. Initially it was included into Other services. 2003–04 onwards, increased emissions have been brought about by Other non-electrical machinery, Construction and Real estate activities. Construction and Other non-electrical machinery are two sectors for which all the effects are positive. CO2 emissions from Real estate activities have gone up mainly because of structure and pollution effects other than the common effects of population and activity. Rising population, need for new housing and construction of new houses have made the structural effect positive for this consumption category (Fig. 21). Cement and steel which are raw materials of the construction industry both account for high amount of CO2 emissions. Cement manufacturing itself contributes to CO2 emission because of calcinations of limestone during production of cement. Total amount of CO2 emitted per ton of cement production ranges from 0.74 ton to 1.24 ton as reported by different researchers (Bhattacharjee, 2010). 5.3.2.7. Housing and lifestyle. Activity effect is the main driver of CO2 emission increase (Fig. 22). Declining intensity and pollution effects after 1993–94 indicate that energy conservation was being practiced along with usage of cleaner fuels for manufacturing. However continuously decreasing structure effect after 1998–99 has raised the intensity effect after 2003–04. Sectoral compositions have remained unchanged after 1993–94.

A. Das, S.K. Paul / Energy Economics 41 (2014) 90–105

99

3

CO2 Emissions in Mt

2 1 0 -1

Wood and wood Leather and Rubber products products leather products

Batteries

Watches and clocks

Gems & jewelry

Communication

-2 -3 Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actal Change

Fig. 15. Causes of increased CO2 emissions from “lifestyle effects” between 2003–04 and 2006–07. Source: Calculated from respective IOTT and emission data.

MEDICAL CARE AND HYGIENE

EDUCATION AND RESEARCH 180

200

Pollution Effect Intensity Effect

150 100 50 0

Pollution Effect

160 Intensity Effect

140

Structure Effect

120

Activity Effect

100

Population Effect

80

Actual change

60

Structure Effect Activity Effect

40

1993-94 1998-99 2003-04 2006-07

Population Effect

20

Miscellaneous metal products, Electrical appliances and Communication equipments are the sectors responsible for increased emissions between 2003–04 and 2006–07 (Fig. 23). Activity and population are the positive drivers of change in all of them. Intensity and pollution effects are positive for the sectors Miscellaneous metal products and Communication equipments. For Electrical appliances, structure and pollution effects are positive. Growing use of electricity for electrical appliances at home has a strong structural effect on increase in CO2 emissions for the Electrical appliances sector.

Actual change

0.5 0 -0.5 -1

Paper, paper prods. & newsprint

Education and research

Computer & related activities

7 -0 06 20

No changes in sectoral composition have been made after 1993–94. All the constituent sectors for this consumption category have led to increases in CO2 emissions between 2003–04 and 2006–07.

8 6

CO2 Emissions in Mt

CO2 Emissions in Mt

1

20 03 -0 4

Fig. 18. Index of CO2 emissions from “medical care and hygiene”. Source: Calculated from emission data.

5.3.2.8. Recreation. Emissions have increased mainly because of structure and activity effects (Fig. 24).

1.5

19 98 -9 9

0 19 93 -9 4

Fig. 16. Index of CO2 emissions from “education and research”. Source: Calculated from emission data.

4

Pollution Effect

2

Intensity Effect

0

Structure Effect

-2

Activity Effect

-4 -6

-1.5

Soaps, cosmetics & glycerin

Medical and health

Population Effect Actal Change

-8 Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actal Change

Fig. 17. Causes of increased CO2 emissions from “education and research” between 2003–04 and 2006–07. Source: Calculated from respective IOTT and emission data.

-10 Fig. 19. Causes of increased CO2 emissions from “medical care and hygiene” between 2003–04 and 2006–07. Source: Calculated from respective IOTT and emission data.

100

A. Das, S.K. Paul / Energy Economics 41 (2014) 90–105

HOUSE BUILDING

HOUSING AND LIFESTYLE

400

Pollution Effect

180

Pollution Effect

160

350

140 Intensity Effect

300

Intensity Effect

120 Structure Effect

100

250 Structure Effect

200

80 Activity Effect

60

150

40 Activity Effect

100

0 50

Population Effect

20 1993-94 1998-99 2003-04 2006-07

Actual change

Population Effect Fig. 22. Index of emissions from “housing and lifestyle”. Source: Calculated from emission data.

20 06 -0 7

20 03 -0 4

19 98 -9 9

19 93 -9 4

0 Actual change

Fig. 20. Index of emissions from “house building”. Source: Calculated from emission data.

All the effects are positive for Electronic equipments (including Television). Intensity effects have decreased but other effects have increased net emissions for Hotels and restaurants. Modern lifestyle has made, recreation both at home and outside it, a part of life. Therefore structure effect is positive for both the sectors of Electronic equipments (including television) and Hotel & restaurant services (Fig. 25). 5.3.2.9. Transport. Structure effect is the largest contributor in increasing emissions for “transport” between 1993–94 and 2006–07 (Fig. 26). Decreasing trends are visible from 1998–99 mainly because of declining energy intensity. Sectorally, from 2003–04 there was diversification of Other transport services to different sectors for Land transport including via pipeline; Water transport; Air transport and Supporting & auxiliary transport activities. Sectors which have contributed to increased emissions after 2003–04 (Fig. 27) are Motor cycles and scooters; Bicycles, cyclerickshaw; Land transport including via pipeline; Water transport and supporting and Auxiliary transport activities. Activity and population effects are positive for all the sectors. Structure and pollution effects are positive for Motor cycles and scooters; Land transport via pipeline and Water transport. Need for fast conveyance has made the structural effect negative in case of Bi-cycles and cycle rickshaw. Use of diesel in the transport services sector is the main cause of increasing CO2 emissions. 5.3.2.10. Other personal services. “Other personal services” (4.4%) has also contributed to increased emissions mainly because of activity effect. 18

Decreasing trends are visible from 2003–04. Energy intensity effects have been negative after 1993–94. The service sector has grown after 1998–99. Addition of new service sectors like Business services; Legal services; Renting of machinery and equipment and Other services have taken place from 2003–04. Service sectors like Insurance, Business services, Legal services and Other services had increased emissions between 2003–04 and 2006–07. Activity and population effects are positive for all sectors (Fig. 28). Structure and pollution effects are positive for Insurance and Business services (Fig. 29). Intensity and pollution effects are positive for Legal services. For Other services it is pollution effect that is the sole contributor to increases in CO2 emissions. Structure effect is positive as more people enterprise for Business and Insurance services. Pollution effect is positive as increasing amount of diesel is being used for transportation related to services sector. From the findings above it can be thus concluded that along with activity and population effects, structural effects have caused pollution effects to increase in categories of “lifestyle effects”, “education and research”, “recreation”, “transport” and “other personal services”. In these categories intervention is required regarding use of cleaner fuels. The “house building” category needs intensity reduction along with structural changes. A study on Italian households reveals that “Agriculture, hunting and sylviculture” and “road transports” are sectors affecting air emissions (Cellura et al., 2012). Studies for other countries in this decade reveal that increases in household emissions are caused by growing number of households, intensity effects (Chung et al., 2011; Zhu et al., 2012); final demand (Cellura et al., 2012; Zhang et al., 2013); per capita energy consumption (Zhang et al., 2013); structural changes towards more energy intensive consumption and cleaner fuels (Zhao et al., 2012) and energy end-use mode choice (Fan et al., 2013). 5

14

Pollution Effect 4

12 Intensity Effect

10

Structure Effect

8 6

Activity Effect

4

Population Effect

2 Actal Change

0 -2

Other nonelectrical machinery

Construction

Real estate activities

Fig. 21. Causes of increased CO2 emissions from “house building” between 2003–04 and 2006–07. Source: Calculated from respective IOTT and emission data.

CO2 Emissions in Mt

CO2 Emissions in Mt

16

Pollution Effect

3

Intensity Effect

2

Structure Effect

1

Activity Effect Population Effect

0 -1

Miscellaneous metal products

Electrical appliances

Communication equipments

Actal Change

-2 Fig. 23. Causes of increased CO2 emissions from “housing and lifestyle” between 2003–04 and 2006–07. Source: Calculated from respective IOTT and emission data.

A. Das, S.K. Paul / Energy Economics 41 (2014) 90–105

40 Pollution Effect

400

Intensity Effect

300

Structure Effect

200

Activity Effect

100

Population Effect 1993-94 1998-99 2003-04 2006-07

Actual change

CO2 Emissions in Mt

RECREATION 500

0

CO2 Emissions in Mt

Fig. 24. Index of emissions from “recreation”. Source: Calculated from emissions data.

Pollution Effect

10

Intensity Effect

Structure Effect

5

Activity Effect 0 Hotels and restaurants

-5

20 0 -20

Motor cycles Bicycles, cycleLand tpt and scooters rickshaw including via pipeline

Water transport

Supporting and aux. tpt activities

-40 -60 -80

15

Electronic equipments(incl.TV)

101

Population Effect Actal Change

-10 Fig. 25. Causes of increased CO2 emissions from “recreation” between 2003–04 and 2006–07. Source: Calculated from respective IOTT and emissions data.

The next section discusses about energy policy options for these consumption categories as well as sectors comprising them which need attention.

6. Energy policy options CO2 emissions (direct and indirect) from household consumption of goods and services have increased between 1993–94 and 2006–07 by 66%. Mostly it is activity and population effects that have a positive impact on emission increase as mentioned in works of other researchers for India (Mukhopadhyay, 2001; Nag and Parikh, 2000). CO2 emissions are related to amount and type of energy consumed. Therefore proper

Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actal Change

Fig. 27. Causes of increased CO2 emissions from “transport” between 2003–04 and 2006–07. Source: Calculated from respective IOTT and emissions data.

energy policy guidelines can help to reduce CO2 emissions from different consumption categories. The study period concerned falls within the planning of 8th (1992–97), 9th (1997–2002) and 10th (2002–07) Plan periods of the Planning Commission. Sustainable development, deregulation of energy prices, market based pricing along with increasing emphasis on demand management, energy security, conservation and efficiency have been part of the energy strategy from the 8th Plan itself. In order to restrict oil imports and maintain energy security, progressive substitution of petroleum products by coal, lignite, natural gas and electricity had been suggested. Renewable energy application was also promoted. In 2001, the Energy Conservation Act was formulated with the setting up of the Bureau of Energy Efficiency (BEE). Labeling programs were taken up to provide decision makers and consumers with information on energy efficiency. Energy efficient technologies were targeted for industries like iron, steel, chemicals, petroleum, pulp and paper and cement. Recycling was part of the demand management program which included energy conservation, optimum fuel mix, structural changes in the economy, appropriate modal mix in transport, greater reliance on cogeneration and reduction of material intensity (9th Five Year Plan). During the 10th Plan, along with energy conservation, energy efficiency, energy security strategies also included development of alternative fuels and usage of clean fuels. Therefore, based upon the decomposition analysis (2003–04 and 2006–07), fuel conservation and fuel substitution exercises were carried out to plan for reduction in emissions for the latest year 2006–07. CO2 emissions are a function of pollution and intensity coefficients along with structure, activity and population. Sectors

OTHER PERSONAL SERVICES 200

TRANSPORT 250 Pollution Effect 200

160

Intensity Effect

140 Intensity Effect

150

Structure Effect

100

120

Structure Effect

100 80

50

Activity Effect

Activity Effect

60 40

Fig. 26. Index of emissions from “transport”. Source: Calculated from emissions data.

0 20 06 -0 7

20 0. ..

20 0. ..

19 9. ..

19 9. ..

Actual change

Population Effect

20 20 03 -0 4

-50

19 98 -9 9

Population Effect

19 93 -9 4

0

-100

Pollution Effect

180

Actual change

Fig. 28. Index of emissions from “other personal services”. Source: Calculated from emissions data.

102

A. Das, S.K. Paul / Energy Economics 41 (2014) 90–105

coefficient through fuel substitution and fuel conservation respectively have also been supported by Paul and Bhattacharya, 2004. This paper combines the options of renewable energy and recycling along with them. Policy options explored are detailed below.

4 Pollution Effect 3 Intensity Effect 2

Activity Effect

0 s

s

vi ce

vi ce

er O th e

ga Le

rs

ls

se ss si ne

Actal Change

Bu

-2

er

rv

an

ic e

s

ce

Population Effect In s

-1

6.1. Reducing intensity effect - energy conservation and recycling

Structure Effect

1

ur

CO2 Emissions in Mt

5

Fig. 29. Causes of increased CO2 emissions from “other personal services” between 2003–04 and 2006–07. Source: Calculated from respective IOTT and emissions data.

having positive pollution effects can have lower emissions if the value of pollution coefficient reduces. Therefore, using a cleaner fuel keeping the amount of energy used to be same or fuel substitution can reduce emissions. Use of renewable energy also reduces pollution coefficient since they are cleaner sources of energy. Likewise, sectors having positive intensity effects can have lower emissions if the value of energy intensity coefficient reduces. Therefore, reducing energy usage keeping the output same or fuel conservation can reduce emissions. This also leads to energy efficiency in the economy. Recycling also reduces intensity coefficient since it reduces energy intensity of processing new material. Similar viewpoints on reducing emissions by reducing the pollution coefficient and intensity

Sectors with positive

We assume a saving of 10% of the amount of coal being used in industries. The useful energy in coal is also assumed to be saved from oil and natural gas as well. Conversion efficiency was considered in the ratio of the net calorific values for respective fuels. Therefore, coal with a net calorific value of 19.6 TJ/kt was substituted by oil and natural gas. Oil to coal conversion efficiency was estimated to be 2.19 for HSD/LDO, 2.06 for FO/LSHS and 1.92 (1 MCM of natural gas to 1 kt of coal) for natural gas. Conserving coal leads to saving of 21,704.97 Mt (9%) of CO2 emissions as compared to 4654.3 Mt (2%) in case of saving of petroleum products. Consequently savings generated in case of coal is only 4186 million Rs as compared to 50,020 million Rs for petroleum products. Therefore from the point of view of GHG reduction and climate change issues, saving coal is a much better option than saving oil and natural gas. It needs to be mentioned here that this is in deviation from our government energy policy which calls for saving oil rather than coal. Saving natural gas does not put forth an advantageous scenario in either emission or monetary savings. Similar results were also derived for the Indian economy in 1983–84 (Parikh and Gokarn, 1993). Recycling can to some extent offset the need for new energy. Fig. 30 gives the energy policy options for sectors with positive intensity effect between 2006–07 and 2003–04.

Reduce Intensity Effect

Consumption Categories

Energy conservation

Intensity Effect

Recycling

Fuel Saving – 10% Coal

Energy Efficiency

Food, Beverage, Tobacco Food Crop

and Primary goods

Energy efficient cropping, less irrigation intensive crop varieties, efficient pumping systems

Art silk, synthetic fiber textiles Batteries

Clothing and footwear Lifestyle Effects

Energy efficient soft flow dyeing, installation of photocells for speed frame, installation of soft starter-cum-energy saver in

Recycling

simplex frames, installation of FRP fan blades for humidification

and re-use

fans in weaving and use of energy efficient pneumafil fans in

batteries

Other non-electrical

House building

spinning mills (Velavan et.al., 2009)

machinery; Construction Industrial by-products like blast furnace slag and fly ash in bricks Miscellaneous metal products; Communication equipments

Electronic equipments

Housing and

and concrete. Large-scale mechanization and production of

lifestyle

concrete by engineered means (Bhattacharjee B., 2010).

Recycling

Energy efficient construction practices.

electronic

Recreation

(including TV)

Legal

waste Industrial use of energy efficient motors, efficient boilers, variable

Other personal services

speed drives, following policy formulations by Energy Saving Companies (Integrated Energy Policy, 2006)

Fig. 30. Energy policy framework for sectors having positive pollution effect in the decomposition analysis between 2003–04 and 2006–07. Data from: Velavan et al. (2009). Source: Author's analysis

A. Das, S.K. Paul / Energy Economics 41 (2014) 90–105

103

while there are other costs involved like cost of changing infrastructure, reduced usage of petroleum products and consequent income and employment effects, and change in the nature of pollutants (Parikh and Gokarn, 1993). Results show that substituting coal with oil decreases emissions for all categories except “transport” and “other personal services” categories. Therefore for Land transport including via pipeline; Water transport and Other services substituting oil with natural gas is a better option in terms of reducing emissions. Monetarily, although substituting coal with oil is a disadvantage since oil is expensive, as well as imported, offsetting oil usage with natural gas generates savings. Therefore a combined approach of substituting oil with natural gas for “transport” and “other personal services” related sectors and coal with oil for the remaining is best (Fig. 31). Calculations show that total CO2 emissions saved are 51.75 Mt which is around 5.8% of the emissions under base case. Monetary savings are 140,230 million Rs, which is around 4% of the money spent under base case. Additional savings in emissions can be realized through use of renewable energy (Panwar et al., 2011) from investments of the money saved from fuel substitution. Solar hot water can be used in industrial applications for washing, pasteurizing, boiling, sterilizing, distilling, bleaching, dyeing in industries like food crops, breweries, textiles, pulp and paper, machinery and chemicals (Weiss, 2012). Solar drying can help food and leather processing industries (Weiss, 2012). A roof integrated solar hot air system is being used for this purpose in Ranipet, Tamil Nadu, India. Solar and wind based electricity generation can provide clean energy to homes for lighting, water heating and cooking. Building integrated photovoltaics (BIPV) can offset emissions from coal based electricity generation in homes. Total emissions saved through energy conservation and fuel substitution is 73,452 Mt with a monetary saving of 144,416 million Rs. Therefore cost of savings is almost 2 million Rs (1USD = 45.22Rs in 2006) per Mt of CO2 not emitted.

It needs to be mentioned here that except Food crops (62%) which is an essential component in household consumption, major emission savers would be “house building” (19%), “recreation” (7%), “clothing and footwear” (6%), and “housing and lifestyle” (5%). Energy efficiency measures in cropping, textile dyeing and manufacturing of building materials would help in reducing energy requirements for “food, beverage, tobacco and primary goods”, “clothing and footwear” and “house building”. Recycling and reuse of batteries and electronic wastes can reduce usage of processing energy for new materials. Energy for processing of battery raw materials is reduced by 65% compared to virgin materials through recycling. Although energy use in battery manufacture activity remains constant irrespective of recycling rate, for a NiCd battery life cycle, complete or near complete recycling reduces CO2 emissions from 0.41 kg/Wh to 0.26 kg/Wh (Rydh and Karlström, 2002). In India, battery recycling is done by an unorganized sector and prevailing practices are not entirely environmental friendly (Eckfeld et al., 2003). Growing motor and motor-cycle industry would add to the increase in demand for batteries in future. Therefore environment compliant technologies for battery recycling should be undertaken. Recycling electronic wastes would reduce CO2 emissions for electronic equipments (“recreation” category). 6.2. Reducing pollution effect - fuel substitution and renewable energy In this scenario we experiment with the possibilities of fuel switching by keeping the net amount of energy used to be same. Change in the fuel type brings about changes in cost incurred as well as changes in emissions. Since oil is expensive compared to coal, India follows a policy of oil being substituted by coal. However, two other scenarios of coal being substituted by oil and oil being substituted by natural gas were created to see which option generates lesser emissions. It should be mentioned here that only relative prices of fuel are considered here,

Sectors with positive Pollution Effect

Consumption Categories

Food Crop; Other crops; Beverages; Tobacco Products

Food, Beverage, Tobacco

Solar hot water for food,

Silk textiles; Art silk, synthetic fiber textiles; Jute,

and Primary goods

tobacco and breweries;

Energy substitution

Renewable Energy

Hot air for drying

hemp, mesta textiles; Leather footwear

Clothing and footwear Wood and wood products; Leather and leather

Lifestyle Effects

products; Rubber products; Batteries; Watches and

Solar hot water for

Substitute coal usage with petroleum products

clocks; Gems and jewelry; Communication

textile; hot air for leather drying

Education and research

Paper, paper products & newsprint; Education and Solar hot water for industrial

Medical care and research; Computer and related activities

processes in paper,

health Soaps, cosmetics & glycerin; Medical and health

machinery, chemicals

House Other non-electrical machinery; Construction; Real estate activities

Building Miscellaneous metal products; Electrical

Housing and lifestyle Substitute petroleum products usage

appliances; Communication equipments Electronic equipments (including TV); Hotels and restaurants

with natural gas – replace diesel

Recreation with CNG or use clean diesel

Motor cycles and scooters;

Transport

Bicycles, cycle-rickshaw;

Land transport including via

Supporting and auxiliary transport

pipeline; Water transport

activities Business services; Legal services Insurance; Natural gas; Petroleum products; Electricity

Other services

Other personal

Solar, wind energy for

services

home electrification,

Energy Fig. 31. Energy policy framework for reducing pollution effect. Source: Authors' analysis

cooking & water heating

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7. Summary and future scope The final decomposition for 2003–04 and 2006–07 reveals that sectors like Batteries, Other non-electrical machinery; Construction; and Electronic equipments (including television) for which all the effects are positive, need to adopt combined strategies of technology upgradation, energy conservation, fuel switching and market management in order to reduce CO2 emissions. Efforts to reduce greenhouse gas emissions from fossil fuel use address two principal objectives of a) mitigation of climate change and b) improvement of energy security. Efficient use of energy needs to be combined with cost efficiency to help in policy level interventions. Proper pricing mechanism should be looked into to dissuade people from fuels which lead to increased CO2 emissions. A few limitations to the methodology followed include working with the total production coefficient matrix following the assumption of competitive imports (Su and Ang, 2013) which includes embodied emissions within imported raw materials. The basic input–output model is linear in structure, with the assumptions of fixed proportions of inputs and constant returns to scale (Pearson, 1989). This may not relate to a world which is non-linear in nature. Fuel substitution exercise does not look into costs associated with changes in technology, labor and new type of emissions. Moreover the complete decomposition method by Sun becomes slightly more tedious when the number of factors is more than five. However, future studies could look at – (i) sector specific studies to assess CO2 emission reduction possibilities and how they affect our consumption patterns, (ii) studies on household electricity consumption pattern in detail to find out whether the usage is optimum or indiscriminate (guided by the activity effect), (iii) use of non-conventional energy by households and (iv) calculation of domestic emissions subject to the availability of competitive import matrices and therefore find out its implications on bilateral trade and embodied emission transitions. Acknowledgement We would like to thank our reviewers for their insightful comments. Their opinions have helped us to improve our paper to a great extent. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.eneco.2013.10.019. References Ahluwalia, M.S., 2002. Economic reforms in India since 1991: Has gradualism worked? J. Econ. Perspect. 16 (3), 67–88. Albrecht, J., Francois, D., Schoors, K., 2002. A Shapley decomposition of carbon emissions without residuals. Energy Pol. 30, 727–736. Andrews, J., Jelley, N., 2007. Energy Science: Principles, Technologies and Impacts. Oxford University Press. Ang, B.W., Choi, K.H., 1997. Decomposition of aggregate energy and gas emission intensities for industry: a refined Divisia index method. Energy J. 18 (3), 59–73. Ang, B.W., Liu, F.W., 2001. A new energy decomposition method: perfect in decomposition and consistent in aggregation. Energy 26 (6), 537–548. Ang, B.W., Zhang, F.Q., 2000. A survey of index decomposition analysis in energy and environmental studies. Energy 25, 1149–1176. Ang, B.W., Zhang, F.Q., Choi, K.H., 1998. Factorizing changes in energy and environmental indicators through decomposition. Energy 23 (6), 489–495. Ang, B.W., Liu, F.L., Chew, E.P., 2003. Perfect decomposition techniques in energy and environmental analysis. Energy Pol. 31 (14), 1561–1566. Bhattacharjee, B., 2010. Sustainability of concrete construction in Indian context. Indian Concr. J. 84 (7), 45–51. Bolin, B., Doos, B.R., Jager, J., Warrick, R.A., 1986. The Greenhouse Effect, Climatic Change and Ecosystems. Wiley, New York. Cellura, M., Longo, S., Marina, M., 2012. Application of the structural decomposition analysis to assess the indirect energy consumption and air emission changes related to Italian households consumption. Renew. Sust. Energ. Rev. 16 (2), 1135–1145. Central Statistical Organisation, 2000. Input–Output Transaction Tables, Ministry of Statistics and Programme Implementation. 4–20. Central Statistical Organisation (CSO), 1994. “Sources and Methods”, Input–Output Transaction Tables 1993–94, Ministry of Statistics and Programme Implementation.

Chen, G.Q., Zhang, B., 2010. Greenhouse gas emissions in China 2007: inventory and input–output analysis. Energy Pol. 38 (10), 6180–6193. Chung, H.S., Rhee, H.C., 2001. A residual-free decomposition of the sources of carbon dioxide emissions: a case of the Korean industries. Energy 26 (1), 15–30. Chung, W., Kam, M.S., Ip, C.Y., 2011. A study of residential energy use in Hong Kong by decomposition analysis, 1990–2007. Appl. Energy 88 (12), 5180–5187. Common, M.S., Salma, U., 1992. Accounting for Australian carbon dioxide emissions. Econ. Rec. 68, 31–42. http://dx.doi.org/10.1111/j.1475-4932.1992.tb01747.x. Dietzenbacher, E., Los, B., 1998. Structural decomposition techniques: sense and sensitivity. Econ. Syst. Res. 10 (4), 307–323. Du, H., Guo, J., Mao, G., Smith, A.M., Wang, X., Wang, Y., 2011. CO2 emissions embodied in China-US trade: input–output analysis based on the emergy/dollar ratio. Energy Pol. 139 (10), 5980–5987. Eckfeld, S., Manders, J.E., Stevenson, M.W., 2003. The Asian battery market - a decade of change. J. Power Sources 116, 32–39. Fafchamps, M., 1992. Cash crop production, food price volatility and rural market integration in the Third World. Am. J. Agric. Econ. 74 (1), 90–99. Fan, J., Guo, X., Marinova, D., Wu, Y., Zhao, D., 2012. Embedded carbon footprint of Chinese urban households: structure and changes. J. Clean. Prod. 33, 50–59. Fan, J.-L., Liao, H., Liang, Q.-M., Tatano, H., Liu, C.-F., Wei, Y.-M., 2013. Residential carbon emission evolutions in urban–rural divided China: an end-use and behavior analysis. Appl. Energy 101, 323–332. Garg, A., Shukla, P.R., Khapse, M., 2006. The sectoral trends of multigas emissions inventory of India. Atmos. Environ. 40, 4608–4620. Gay, P.W., Proops, J.L.R., 1993. CO2 production by the UK economy: an input–output assessment. Appl. Energy 44, 113–130. Goswami, B.N., Venugopal, V., Sengupta, D., Madhusoodanam, M.S., Xavier, P.K., 2006. Increasing trends of extreme rain events over India in a warming environment. Science 314, 1442–1445. Government of India, Planning Commission, 2006. Integ. Energy Pol. 2006. Grubler, A., 1998. Technology and Global Change. Cambridge University Press, Cambridge, UK. Guo, J., Zhang, Z., Meng, L., 2012a. China's provincial CO2 emissions embodied in international and interprovincial trade. Energy Pol. 42, 486–497. Guo, S., Shao, L., Chen, H., Li, Z., Liu, J.B., Xu, F.X., Li, J.S., Han, M.Y., Meng, J., Chen, Z.-M., Li, S.C., 2012b. Inventory and input–output analysis of CO2 emissions by fossil fuel consumption in Beijing 2007. Ecol. Inform. 12, 93–100. Hocaoglu, F.O., Karanfil, F., 2011. Examining the link between carbon dioxide emissions and the share of industry in GDP: modeling and testing for the G-7. Energy Pol. 39 (6), 3612–3620. IPCC, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Energy vol. 2 (Accessed at http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html, May 2012). Kerkhof, A.C., Benders, R.M.J., Moll, H.C., 2009. Determinants of variation in household CO2 emissions between and within countries. Energy Pol. 37 (4), 1509–1517. Lal, R., 2004. Carbon emission from farm operations. Environ. Int. 30, 981–990. Lenzen, M., 1998. Primary energy and greenhouse gases embodied in Australian final consumption: an input–output analysis. Energy Pol. 26 (6), 495–506. Liang, S., Zhang, T., 2011. What is driving CO2 emissions in a typical manufacturing center of South China? The case of Jiangsu Province. Energy Pol. 39 (11), 7078–7083. Lin, B.Q., Sun, C.W., 2010. Evaluating carbon dioxide emissions in international trade of China. Energy Pol. 38 (1), 613–621. Liu, H.-T., Guo, J.-E., Qian, D., Xi, Y.-M., 2009. Comprehensive evaluation of household indirect energy consumption and impacts of alternative energy policies in China by input–output analysis. Energy Pol. 37 (8), 3194–3204. Mani, N.J., Suhas, E., Goswami, B.N., 2009. Can global warming make Indian monsoon weather less predictable? Geophys. Res. Lett. 36. http://dx.doi.org/10.1029/ 2009GL037989 L08811. Mielnik, O., Goldemberg, J., 1999. The evolution of the “carbonization index” in developing countries. Energy Pol. 27, 307–308. Miller, R.E., Blair, P.D., 1985. Input–Output Analysis: Foundations and Extensions. Prentice-Hall, Englewood Cliffs. Mitchell, J.F.B., Wilson, C.A., Cunningham, W.M., 1987. CO2 climate sensitivity and model dependence of results. Q. J. R. Meteorol. Soc. 193, 293–322. Mukhopadhyay, K., 2001. An empirical analysis of the sources of CO2 emission changes in India. Asian J. Energy Environ. 2 (3–4), 233–271. Mukhopadhyay, K., Chakraborty, D., 2002. Economic reforms, energy consumption and CO 2 emissions in India: a quantitative analysis. Asia Pac. Dev. J. 9 (2), 107–127. Mukhopadhyay, K., Forssell, O., 2005. An empirical investigation of air pollution from fossil fuel combustion and its impact on health in India during 1973–74 to 1996–97. Ecol. Econ. 55 (2), 235–250. Muni Krishna, K., 2008. Intensifying tropical cyclones over the North Indian Ocean during summer monsoon - global warming. Glob. Planet. Change 65 (1–2), 12–16. Munksgaard, J., Pedersen, K.A., Wien, M., 2000. Impact of household consumption on CO2 emissions. Energy Econ. 22, 423–440. Muñoz, P., Steininger, K.W., 2010. Austria's CO2 responsibility and the carbon content of its international trade. Ecol. Econ. 69 (10), 2003–2019. Murthy, N.S., Panda, M., Parikh, J., 1997. Economic development, poverty reduction and carbon emissions in India. Energy Econ. 19, 327–354. Nag, B., Parikh, J., 2000. Indicators of carbon emission intensity from commercial energy use in India. Energy Econ. 22, 441–461. Panwar, N.L., Kaushik, S.C., Kothari, S., 2011. Role of renewable energy sources in environmental protection: a review. Renew. Sust. Energ. Rev. 15 (3), 1513–1524. Parikh, J., Gokarn, S., 1993. Climate change and India's energy policy options. New perspectives on sectoral CO2 emissions and incremental costs. Glob. Environ. Change 3 (3), 276–291.

A. Das, S.K. Paul / Energy Economics 41 (2014) 90–105 Parikh, J., Panda, M., Ganesh-Kumar, A., Singh, V., 2009. CO2 emissions structure of the Indian economy. Energy 34, 1024–1031. Paul, S., Bhattacharya, R.N., 2004. CO2 emission from energy use in India: a decomposition analysis. Energy Pol. 32 (5), 585–593. Pearson, P.J.G., 1989. Proactive energy - environment policy strategies: a role for input output analysis? Environ. Plann. A 21 (10), 1329–1348. Planning Commission, Government of India, 1992. Ninth five year plan. Energy vol. II (Part II, Power Sector. Accessed at http://planningcommission.nic.in/plans/planrel/ fiveyr/welcome.html in September, 2012). Planning Commission, Government of India, 2002. Tenth Five Year Plan, vol. II 897–930 (Accessed at http://planningcommission.nic.in/plans/planrel/fiveyr/10th/volume2/ v2_ch8_2.pdf in September, 2012). Planning Commission, Government of India, 2006. Integrated Energy Policy: Report of the Expert Committee. Government of India 1–148. Planning Commission, Government of India, 2008. Eleventh Five Year Plan, vol. III 342–393 (Accessed at http://planningcommission.nic.in/plans/planrel/fiveyr/11th/ 11_v3/11v3_ch10.pdf in September, 2012). Rydh, C.J., Karlström, M., 2002. Life cycle inventory of recycling portable nickel-cadmium batteries. Resour. Conserv. Recycl. 34, 289–309. Schumacher, K., Sathaye, J., 1999. India's cement industry: productivity, energy efficiency and carbon emissions. Report of the Environmental Energy Technologies Division. Lawrence Berkeley National Laboratory, Berkeley, CA. Shapley, L.S., 1953. A value for n-person games. Contributions to the Theory of Games II. Princeton University Press, pp. 307–317. Su, B., Ang, B.W., 2010. Input–output analysis of CO2 emissions embodied in trade: the effects of spatial aggregation. Ecol. Econ. 70 (1), 10–18. Su, B., Ang, B.W., 2012a. Structural decomposition analysis applied to energy and emissions: some methodological developments. Energy Econ. 34, 177–188. Su, B., Ang, B.W., 2012b. Structural decomposition analysis applied to energy and emissions: aggregation issues. Econ. Syst. Res. 24 (3), 299–317. Su, B., Ang, B.W., 2013. Input–output analysis of CO2 emissions embodied in trade: competitive versus non-competitive imports. Energy Pol. 56, 83–87. Su, B., Huang, H.C., Ang, B.W., Zhou, P., 2010. Input–output analysis of CO 2 emissions embodied in trade: the effects of sector aggregation. Energy Econ. 32, 166–175.

105

Sun, J.W., 1998. Changes in energy consumption and energy intensity: a complete decomposition model. Energy Econ. 20 (1), 85–100. Tarancón Morán, M.A., Del Río González, P., 2007. A combined input–output and sensitivity analysis approach to analyse sector linkages and CO2 emissions. Energy Econ. 29 (3), 578–597. Tarancón Morán, M.A., Del Río González, P., 2012. Assessing energy-related CO2 emissions with sensitivity analysis and input-output techniques. Energy 37, 161–170. TERI, 1995. TEDDY (1995/96) (Tata Energy Data Directory Year Book). Environmental Effects of Energy Use. TERI publication, pp. 220–225. The World Bank, 2010. World Development Report. The World Bank, Washington D.C. Velavan, R., Rudramoorthy, R., Balachandran, S., 2009. CO 2 emission reduction opportunities for small and medium scale textile sector in India. J. Sci. Ind. Res. 68, 630–633. Von Braun, J., Kennedy, E., 1986. Commercialization of subsistence agriculture: income and nutritional effects in developing countries. Working Papers of the International Food Policy Research Institute, No.1, pp. 1–79. Weiss, W., 2012. Solar Heat for Industrial Applications: Potential, Framework Conditions and Built Examples. Institute of Sustainable Technologies, AEE, Austria (http:// www.sustainability.vic.gov.au/resources/documents/solar_heat_for_industrial_ processes.pdf. Retrieved October 2012.). Wigley, T.M.L., Schlesinger, M.E., 1985. Analytical solution for the effect of increasing CO2 on global mean temperature. Nature 315, 649–652. Xu, M., Li, R., Crittenden, J.C., Chen, Y., 2011. CO2 emissions embodied in China's exports from 2002 to 2008: a structural decomposition analysis. Energy Pol. 39 (11), 7381–7388. Yunfeng, Y., Laike, Y., 2010. China's foreign trade and climate change: a case study of CO2 emissions. Energy Pol. 38 (1), 350–356. Zafrilla, J.E., López, L.A., Cadarso, M.A., Dejuán, Ó., 2012. Fulfilling the Kyoto protocol in Spain: a matter of economic crisis or environmental policies? Energy Pol. 51, 708–719. Zhang, J., Zhang, Y., Yang, Z., Fath, B.D., Li, S., 2013. Estimation of energy-related carbon emissions in Beijing and factor decomposition analysis. Ecol. Model. 252, 258–265. Zhao, X., Li, N., Ma, C., 2012. Residential energy consumption in urban China: a decomposition analysis. Energy Pol. 41, 644–653. Zhu, Q., Peng, X., Wu, K., 2012. Calculation and decomposition of indirect carbon emissions from residential consumption in China based on the input–output model. Energy Pol. 48, 618–626.