CO2 emissions from household consumption in India between 1993–94 and 2006–07: A decomposition...

Energy Economics 41 (2014) 90–105

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Energy Economics

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CO2 emissions from household consumption in India between 1993–94and 2006–07: A decomposition analysis

Aparna Das ⁎, Saikat Kumar PaulDepartment of Architecture and Regional Planning, Indian Institute of Technology, Kharagpur, West Bengal - 721302, India

⁎ Corresponding author. Tel.: +91 9874397645.E-mail address: [email protected] (A. Das).

0140-9883/$ – see front matter © 2013 Elsevier B.V. All rihttp://dx.doi.org/10.1016/j.eneco.2013.10.019

a b s t r a c t
a r t i c l e i n f o
Article history:Received 10 October 2012Received in revised form 19 October 2013Accepted 29 October 2013Available online 11 November 2013

JEL Classification:C67D12O53P28Q43Q48

Keywords:Household CO2 emissionsEnergy input–output analysisComplete decomposition analysis

CO2 emission from anthropogenic activities is one of the major causes of global warming. India being an agricul-ture dependent country, global warmingwould meanmonsoon instability and consequent food scarcity, naturaldisasters 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 between1993–94 and 2006–07 has been done to identify the causes into pollution, energy intensity, structure, activityand population effects according to broadhousehold consumption categories. Results indicate that activity, struc-ture and population effects are themain causes of increase in CO2 emission fromhousehold fuel consumption. Toidentify the causes at the sectoral level a second decomposition has been done for changes between 2003–04 and2006–07 to identify the causes in the next stage. Finally alternative energy policy options have been examined foreach consumption category to reduce emissions. Combined strategies of technology upgradation, fuel switchingandmarketmanagement in order to reduce CO2 emissions for sectors like Batteries, Other non-electricalmachin-ery, Construction and Electronic equipments (including Television), for which all the effects are positive, need tobe adopted.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

Earth's atmosphere, especially CO2, is one of the major concerns re-garding global warming since it increases the residing time of watervapor in the atmosphere considerably (Andrews and Jelley, 2007). Poten-tially irreversible changes in global climate are predicted for 2050 (Bolinet al., 1986; Mitchell et al., 1987;Wigley and Schlesinger, 1985). Anthro-pogenic activities under business-as-usual scenario would lead to a 5 °Cincrease in global temperature but proper and timely interventions canrestrict it within 2 °C (TheWorld Bank, 2010). India being an agriculturedependent country, global warming would mean monsoon instability(Goswami et al., 2006; Mani et al., 2009; Muni Krishna, 2008) for thecountry leading to consequent food scarcity, natural disasters and eco-nomic concerns. Policy interventions, both technological as well as eco-nomic can limit emissions of greenhouse gases into the atmosphere.

Anthropogenic activities have resulted in enhanced greenhouseeffect by 46% (range 38–54%) on account of the energy sector (TERI,1995). Therefore we need to model the amount of emissions generatedby 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,

ghts reserved.

commercially viable technologies currently available and in use in thedeveloped countries can decrease it further by 20%.

This paper estimates fuel consumption of the economy between1993–94 and 2006–07 using input–output transaction tables. Changesin emissions and decomposition of changes, into its sources, havebeen analyzed using complete decomposition method. Finally energypolicy guidelines have been suggested for sectors that lead to increaseof emissions between 2003–04 and 2006–07.

The objectives of this paper are:

1. Estimation of direct and indirect CO2 emissions from household con-sumption of fuel between 1993–94 and 2006–07 using input–outputtransaction tables.

2. Decomposition of changes in CO2 emission between 1993–94 and2006–07 into pollution effect, intensity effect, structure effect, activ-ity effect and population effect according to broad consumptioncategories.

3. Decomposition of changes in CO2 emission between 2003–04 and2006–07 at the sectoral level and tracing the changes in emissionsfrom each consumption category to the contributing sectors.

4. Estimation of monetary and physical resource saving under energyconservation and fuel substitution scenarios for sectors under con-cern in 2006–07.

5. Provision of energy policy guidelines pertaining to the consumptioncategories at the sectoral level.

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91A. Das, S.K. Paul / Energy Economics 41 (2014) 90–105

2. Background

In 1993–94, about 761 Mt of CO2 were emitted by India generatedfrom fuel consumption only. Household consumption of fuel comprisedof 12% of these emissions. The rest of the emission was due to fuel con-sumption in agriculture, manufacturing, power generation, transportand service sectors. By 2006–07, household fuel consumption had in-creased CO2 emission to 18% (Fig. 1). This is an account of direct energyconsumed byhouseholds in terms of cooking, lighting of homes and fuelused for privately ownedmotorized vehicles. A lot of energy is also em-bodied in goods and services consumed by households. They account fora large share of indirect CO2 emission.

3. Literature review

Input–output tables have been used for calculation of CO2 emissionfrom both direct and indirect energy consumption since they portraythe transaction of goods and services within the industry as well as inthe different sectors of final demand. One of the early applications wasfor the Australian economy (Common and Salma, 1992). Estimation ofstructural adjustments necessary to achieve 20% reduction in CO2 emis-sions over 20 years for Germany and the UK was done by Gay andProops (1993). Input–output tables have also been used for assessingresource or pollutant embodiments in goods and services on a macro-economic scale (Lenzen, 1998). Some studies on CO2 emissions usinginput–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; Yunfengand Laike, 2010), Spain (Tarancón Morán and Del Río González, 2007;Zafrilla et al., 2012), Austria (Muñoz and Steininger, 2010) and G-7countries (Hocaoglu and Karanfil, 2011). Earlier studies have beencited in Tarancón Morán and Del Río González (2012).

Certain studies using input–output models have been specific tohousehold consumption. CO2 emissions generated from household con-sumption only have also been analyzed using input–output analysis. Arecent study calculates embedded carbon footprint of Chinese urbanhouseholds using the model (Fan et al., 2012). It shows that with risingincome CO2 emission intensity rises or remains the same for transport,recreation, housing and enjoyment. Input–output model has also beencombined with hybrid analysis to calculate household CO2 emissionsfor Netherlands, UK, Sweden and Norway (Kerkhof et al., 2009). Resultsshow that country characteristics, like energy supply, populationdensity and the availability of district heating, influence variation inhousehold CO2 emissions between and within countries.

0

200

400

600

800

1000

1200

1400

1600

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

Direct CO2 Emission by Industry Household Direct Emission

CO

2 em

issi

on in

Mt

Fig. 1. Direct CO2 emission by the Indian economy in comparison to direct householdenergy 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.

Emission calculations have been done by researchers in India. Parikhand Gokarn (1993) have analyzed CO2 emissions in the Indian economyfor 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 withprojections for 2005 had been done by Murthy et al. (1997). Trends ofsix greenhouse gases (asmentioned in Kyoto Protocol) and local air pol-lutant emissions of India for 1985–2005 have been provided by Garget al. (2006). Their paper considers emissions from all sources and notfuel consumption alone. Analysis of CO2 emission of the Indian economyby producing sectors and due to household final consumption basedon input–output table and Social Accounting Matrix for 2003–04,distinguishing 25 sectors and 10 household income classes has beendone by Parikh et al. (2009). However, analysis of CO2 emissions fromhousehold energy consumption during post reform period in Indianeconomy between 1993–94 and 2006–07 has not been done yet.

Understanding the forces of change over time has best beenanalyzed through decomposition analysis. The two broad classificationsof 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 econo-my and index decomposition analysis (IDA) which does not dependupon IOTT, is mostly applied sectorally. SDA can handle absolute indica-tors whereas IDA can handle both absolute and intensity indicatorsDifferences between these two methods have been discussed in detailin Su and Ang, 2012a. Applications of decomposition analysis on house-hold emissionswhich have been undertaken in the current decade haveused both IDA (Chung et al., 2011 for Hong Kong; Zhang et al., 2013for Beijing; Zhao et al., 2012 for China; Fan et al., 2013 for China) andSDA (Cellura et al., 2012 for Italy; Zhu et al., 2012 for China) methods.Decomposition analysis looks into effects of changing one parameterat a time, while keeping the rest unchanged at the base year, alongwith an interaction effect. The interaction term generates due to com-bined effects of all the parameters. Different decomposition methodshandle the interaction effects differently and hence there is ambiguityin it (Ang and Zhang, 2000; Munksgaard et al., 2000; Sun, 1998).Some researchers have come up with a complete decomposition meth-od in the additive and multiplicative forms, where the residual term iseliminated. The different complete decomposition methods as men-tioned 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 latershown 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 otherproperties and is consistent with the "ideal" index used in the indexnumber theory (Su and Ang, 2012a).

Between 1970 and 1996 emissions profile was decomposed usingDivisia Index to understand the contribution of factors like activitylevels, structural changes, energy intensity, fuel mix and fuel qualityon changes in aggregate carbon intensity of the economy, takingdeclining coal quality into consideration (Nag and Parikh, 2000). CO2

emission changes between 1973–74 and 1996–97 were analyzed byMukhopadhyay (2001) with five factors like variation of industrialadded values, changes in CO2 intensity of various industries, changesin technical coefficient, changes in final demand of various industriesand total joint effects. An input–output structural decomposition analy-sis for India revealed petroleumproducts and electricity as the dominat-ing sectors in CO2 emissions (Mukhopadhyay and Chakraborty, 2002).Emission analysis between 1973–1996 for India using the approximateD&L(1998) method shows eco-efficiency of production, productionstructure and volume of final demand as the major causes of increasein emissions (Mukhopadhyay and Forssell, 2005; Su and Ang, 2012a).Complete decomposition analysis of emissions from the Indian

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

economy between 1980–1996 reveals economic growth as the largestcontributor towards increased emissions (Paul and Bhattacharya,2004). No studies have been done in analyzing household emissionsduring 1993–94 and 2006–07 in the post reform period.

4. Methodology

In this research paper fuel consumption for households has been cal-culated for 1993–94, 1998–99, 2003–04 and 2006–07 using sectoral1

data available from input–output transaction tables (IOTT), commercialenergy data for the study years, as well as sectoral price indices.Commodity × commodity tables were used for the calculations. Sincepublished commodity × commodity tables for the year 2006–07 werenot available, they were calculated from corresponding AbsorptionMatrix1 and Make Matrix2 which were published by official sources.IOTT obtained from CSO (Central Statistical Organisation) were usedfor the analysis. During the 90s IOTT comprised of a 115 × 115 sectorclassification, whereas during 2003–04 and 2006–07 they comprise ofa 130 × 130 sector classification. The changes in sectoral classificationare due to addition of new sectors to broad categories of food; Animalhusbandry and livestock products; Miscellaneous manufacturing;Other transport services andOther services. These sectors have been de-fined in ‘Sources andMethods’manuals for IOTT of the respective years.

4.1. Input–output model and sectoral aggregation

The basic structure of an input–output transaction table (Miller andBlair, 1985) is

X ¼ I−Að Þ−1Yh i

ð1Þ

where,

A = Technical coefficient matrixX = Vector of total outputY = 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 disag-gregate level (Miller and Blair, 1985). Aggregation helped in allocationof energy flows as energy data available for corresponding years wereavailable on such aggregated basis. Most of the manufacturing sectorswere left disaggregated keeping in mind different household consump-tion categories. In the first round, 1993–94 and 1998–99 tableswere ag-gregated to 89 × 89 tables while 2003–04 and 2006–07 tables wereaggregated to 100 × 100 tables to help in fuel allocation. In the secondstage the tables were reaggregated to 13 sectors based upon householdconsumption categories whichwere defined in the samemanner for allthe four years. This helped in consumption based emission analysisbetween 1993–94 and 2006–07. The detailed household consumptioncategories comprising of its constituent sectors have been provided inAppendix C. Computational detail of sectoral aggregation can be re-ferred to inMiller and Blair, 1985. Sectors that were aggregated includeFood crops, Cash crops, Plantation crops, Animal husbandry, Othermin-erals, Sugar and khandsari, Food products and Cotton textiles. Additionof new industrial and a number of service sectors after 1998–99 haveled to changes in constituents of the different household consumptioncategories from 2003–04 onwards. They have been discussed in thesection under indirect emissions from households.

1 Absorption Matrix is a commodity × industry matrix which tabulates the flow ofgoods and services for Primary, Manufacturing and Service sectors.

2 Make Matrix is an industry × commodity matrix which tabulates the flow of goodsand services for the Primary, Manufacturing and Service sectors.

4.2. Fuel allocation and calculation of CO2 emissions by households

Energy data was obtained from official sources which includeMinistry of Statistics and Programme Implementation, Government ofIndia (GoI), for Coal and Lignite statistics; Ministry of Petroleum andNatural Gas, GoI, for Crude petroleum, Natural gas and Petroleum prod-uct statistics; andMinistry of Power and Central Electricity Authority forElectricity statistics. Monetary flow values in the input–output tableswere used to make proportional allocations, wherever physical flowsof energy required more sectoral disaggregation than was available inpublished sources. This hybrid approach of fuel allocation gives betterresults after sector aggregation (Su and Ang, 2012b; Su et al., 2010).Appendix A gives a detailed description of the method used forallocating fuel consumption flows.

According to the basic balance equation based upon input–outputmodel, any energy sector ei is given as,

ei ¼Xnk¼1

eik þ eiy ð2Þ

where,

ei = Total output of energy sector ieik = Intersectoral transaction from energy sector i to keiy = Sale of energy source of type i to final demand.

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

E ¼Xr

i¼1

ei ¼Xr

i¼1

Xnk¼1

eik þ eiy

" #ð3Þ

E ¼Xnk¼1

Ek þ Ey ð4Þ

where, Ek is the energy required for intersectoral transaction forproducing commodity k and Ey is the energy sold to final demand.

Now,

Ek ¼ Ek=Xk½ �Xk; ð5Þ

or,

Xnk¼1

Ek ¼Xnk¼1

Ek=Xk

" # Xnl¼1

I−Að Þ−1h i

klYl

" #; ð6Þ

substituting the value of Xk from Eq. (1) for the sector k. The matrix A isthe total production coefficient matrix as India follows the assumptionof competitive imports.

Or

Xnk¼1

Ek ¼Xnk¼1

Rk

" # Xnl¼1

I−Að Þ−1h i

klYl

" #; ð7Þ

where, Rk is the direct energy intensity or energy (aggregate of allenergy types measured in units of oil equivalent) required to produce1Re (in 1993–94 prices) worth of commodity of sector k.

Now, direct energy intensity multiplied by the Leontief Inversematrix gives the total energy intensity for each sector.

Let,

Tk ¼ Rk � I−Að Þ−1h i

ð8Þ


or,

E ¼Xnk¼1

TkY þ Ey; ð9Þ

where, Tk is the Total energy intensity of sector k for consumption of en-ergy E, measured in units of oil equivalents consumed per unit value ofoutput in monetary units. Again, values for total energy intensity arecalculated for a base year (1993–94) since the value of Rupee changesfrom year to year.

Total energy consumed by households can be defined as:

H ¼Xdk¼1

Hdirect þXne¼1

Hindirect d≠eð Þ ð10Þ

¼Xdk¼1

Tk � ck þXne¼1

Te � ce; ð11Þ

where, Tk is the total energy intensity of energy E consumed by kenergy sectors and Te is the total energy intensity of energy E consumedby e non-energy sectors. ck and ce are the values for Private FinalConsumption Expenditure3 (PFCE) of the energy and non-energysectors in the economy. Hdirect and Hindirect signify direct and indirectenergy consumed by households.

Next, in order to quantify the energy consumed for each consump-tion category, the values for Hk (total household energy consumptionbecause of consumption of goods from k sectors) were aggregatedover the h consumption categories.

Or,

H ¼Xdi¼1

Hi þXge¼1

He þ ::::::::::::::::þXhm¼1

Hm ð12Þ

where, Hi,He,............,Hm are values of total energy consumption by eachhousehold consumption category, while i,e,..........,m are the number ofsectors 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 tablesas discussed in detail in Appendix A. Fuel allocationwas thenmultipliedby respective country specific emission factors detailed in Appendix B tocalculate the amount of emissions. Sectoral CO2 emission is estimatedfollowing IPCC, 2006 guidelines for National Greenhouse Gas Invento-ries Tier 2 approach which requires data on sectoral energy consump-tion and country specific emission factors. The following equation(IPCC, 2006) gives the procedure for calculating the total emissions:

TotalEmissionsGHG;fuel¼

Xfuels

Fuel Consumptionfuel � Country Specific Emission FactorGHG;fuel:

ð13Þ

Fraction of carbon oxidized in the fuel is assumed to be 1.

3 PFCE represents the consumption expenditure of households and non-profit institu-tions. The methodology adopted to prepare the vector of PFCE is the same as that adoptedfor NAS. However, to arrive at the sector-wise estimates of PFCE, the item-wise details ofPFCE by object for the year 1993–94 available in the NAS have been used along with theoutput data (at four digit level national industrial classification) from the results of surveysconducted on registered and unregisteredmanufacturing sectors for the year1993-94. Therelevant import/export data obtained from RBI have also been used to arrive at the sector-wise estimates PFCE. (Source: Central Statistical Organisation, 2000).

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

ei ¼Xnk¼1

eik þ eiy:

Therefore in case of emission,

Ci ¼Xnk¼1

eik � aikð Þ þ Ciy ð14Þ

where, Ci is the CO2 emission from energy sector i, aik is the countryspecific CO2 emission factor of energy type i consumed by sector k andCiy is the CO2 emission of energy type i consumed by final demand y.

Or,

Ci ¼Xnk¼1

Cik þ Ciy ð15Þ

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

C ¼Xnk¼1

Ck þ Cy ð16Þ

where, Ck is the CO2 emission from energy required for intersectoraltransaction for producing commodity and Cy is CO2 emission from theenergy sold to final demand Y.

Now,

Ck ¼Xnk¼1

Ck=Xk½ �Xk þ Cy ð17Þ

or,

Xnk¼1

Ck ¼Xnk¼1

Bk

" # Xnl¼1

I−Að Þ−1h i

klYl

" #þ Cy ð18Þ

where, Bk = Ck/Xk is the direct CO2 emission intensity of sector k forconsumption of energy,

or,

Ck ¼Xnk¼1

FkY þ Cy ð19Þ

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

Q ¼Xdk¼1

Qdirect þXne¼1

Qindirect d≠eð Þ ð20Þ

¼Xdk¼1

Fk � ck þXne¼1

Fe � ce; ð21Þ

where, Fk is the total CO2 emission intensity of energy consumed by kenergy sectors and Fe is the total CO2 emission intensity of energyconsumed by e non-energy sectors. ck and ce are the values for PFCE ofthe energy and non-energy sectors in the economy.

In order to quantify the CO2 emissions for each consumptioncategory, the values for Qk were aggregated over the h consumptioncategories.

Or,

Q ¼Xdi¼1

Qi þXge¼1

Qe þ :::::::::þXhm¼1

Qm ð22Þ


where, Qi,Qe,.........Qm are values of total CO2 emissions from each house-hold consumption category, while i,e.....,m are the number of sectorscomprising the different consumption categories.

Input–output tables in India, just like the US and China, follow theassumption of competitive imports and therefore CO2 emissions fromhousehold consumption based upon SRIO model (Miller and Blair,1985) include not only domestic emissions but also embodied emis-sions that have entered the production chain due to imports. Emissioncalculations based upon similar assumptions have been done for Chinaby Lin and Sun (2010), Chen and Zhang (2010) and Xu et al. (2011) ascited by Su and Ang (2013).

4.3. The complete decomposition approach

The second stage looks into the causes of change in household emis-sions during 1993–94 and 2006–07 through a decomposition analysis.CO2 emissions from fossil fuel burning and industrial processeswere re-lated to Climate Change through the Kaya Identity which expresses theglobal fossil fuel emissions to four factors like global population, percapita world GDP (world GDP/population), energy intensity of worldGDP (energy consumed/GDP generated) and carbon intensity of energy(CO2 emitted/energy consumed). In this model the structure effect hasbeen included which is the composition of household consumptiongoods. Based upon changing lifestyles and people's preference of onehousehold good over another, the changes in structural compositionalso make a difference in the household CO2 emissions.

Application of input–output analysis helps in calculating totalemissions which include both the direct as well as the indirectcomponent. Total household CO2 emissions Q can be evaluated asproduct of pollution coefficient W, energy intensity coefficient I,structure coefficient S, activity coefficient A and population P. Previousworks (Paul and Bhattacharya, 2004) had decomposed total house-hold CO2 emissions into first four factors of pollution, intensity,structure and activity. This paper looks into population effect as thefifth variable. The annual CO2 emissions from household energy usecan be written as:

Qt ¼Xni¼1

Qit

Hit� Hit

cit� citXn

i¼1

cit

�

Xni¼1

cit

Pt� Pt

¼Xni¼1

Wit � Iit � Sit�Ait � Pt ð23Þ

where n is number of sectors for each consumption category in ahousehold; Qit is CO2 emissions of the ith category at time t; Hit is en-ergy (primary and secondary) consumption of the ith category attime t; cit is private final consumption expenditure of the ith categoryat time t and Pt is population at time t. The five variables consideredfor the decomposition are:

(a) Pollution coefficient: It is defined by the ratio of CO2 emitted tothe 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 releasedper unit of oil equivalent consumed is called the carbonizationindex (Grubler, 1998;Mielnik and Goldemberg, 1999).Wit is pol-lution coefficient of CO2 emissions of the ith category at time t. Ithas been used previously for decomposing carbon emissions byPaul and Bhattacharya, 2004.

(b) Energy intensity coefficient: It is defined as the ratio of energyconsumed to the household consumption expenditure. It varieswith choice of energy, efficiency of energy systems and techno-logical choices. Iit is energy intensity coefficient of CO2 emissionsof the ith category at time t.

(c) Structure coefficient: It is defined as the ratio of householdconsumption expenditure for a particular category to the totalhousehold consumption. It varies with changes in socio-economic structure of the society and consequent lifestylechanges. Sit is structural coefficient (composition of privateconsumption) of CO2 emissions of the ith category at time.

(d) Activity coefficient: It is defined by the ratio of total householdconsumption expenditure to population. GDP of the economyhas been considered as the activity effect as it gives a theoreticalquantification of CO2 emissions caused by economic activities(Sun, 1998). In this paper it is defined as per capita expenditurewhich changeswith changes in socio-economyand has influenceon lifestyle changes. At is activity coefficient (per capitaexpenditure) of CO2 emissions at time t.

(e) Population: Population figures change every year and theyhave a direct impact on CO2 emissions. Therefore this is con-sidered as a decomposition variable. It cumulates emissionsgenerated at the per capita level. Pt is population at time t.

In this paper we follow the complete decomposition method pro-posed 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) andmathematically simple. Shapley decomposition takes an average of n!calculations for each effect (Albrecht et al., 2002) and therefore applica-tion of this method is comparatively easier. For an exact decompositiontotal change in the quantity being decomposed over a certain period isgiven by sum of its constituting effects. Therefore, change of CO2

emissions in a period [0,t] is given by:

ΔQ ¼ Weffect þ Ieffect þ Seffect þ Aeffect þ Peffect : ð24Þ

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

Weffect ¼Xni¼1

Wið ÞIi0Si0A0P0 þ12

� � Xni¼1

Wið Þ ΔIið ÞSi0A0P0 þ Ii0 ΔSið ÞA0P0þIi0Si0 A0ð ÞP0 þ Ii0Si0A0 ΔPð Þ

� �" #

þ 13

� � Xni¼1

Wið Þ ΔIið Þ ΔSið ÞA0P0 þ ΔIið ÞSi0 ΔAð ÞP0 þ ΔIið ÞSi0A0 ΔPð ÞþIi0 ΔSið Þ ΔAð ÞP0 þ Ii0 ΔSið ÞA0 ΔPð Þ þ Ii0Si0 ΔAð Þ ΔPð Þ

� �" #

þ 14

� � Xni¼1

Wið Þ ΔIið Þ ΔSið Þ ΔAð ÞP0 þ Ii0 ΔSið Þ ΔAð Þ ΔPð Þ þ ΔIið ÞSi0 ΔAð Þ ΔPð Þf g" #

þ 15

� �Xni¼1

ΔWiΔIiΔSiΔAΔP:

ð25ÞIn order to understand the cumulative effects of temporal changes

additive Chaining Decomposition Analysis was carried out to analyzethe changes in effects when the entire time period under concern wasbroken down into the constituent smaller time periods. Therefore, fora time-series data, change in CO2 emissions between two time periodsis given by Su and Ang, 2012b,

ΔQ t0; :::::::; tkð Þ ¼Xks¼1

Qs−Qs−1ð Þ ¼Xks¼1

ΔQ ts−1; tsð Þ ¼ ΔQ t0; tkð Þ: ð26Þ

The change in CO2 emissionsΔQ(ts−1,ts) between time ts−1 to ts, canbe decomposed into the following five effects (Su and Ang, 2012b):

ΔQ ts−1; tsð Þ ¼ ΔQpollution ts−1; tsð Þ þ ΔQ intensity ts−1; tsð Þþ ΔQstructure ts−1; tsð Þ þ ΔQactivity ts−1; tsð Þþ ΔQpopulation ts−1; tsð Þ: ð27Þ

23141 32161 21829 23870

1135 1532 3175 3428

0.00

0.05

0.10

1

1000

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

HSD and LDO consumed in the transport sector

HSD and LDO consumed in the service sector

CO2

CH4

CO

2 E

mis

sion

s in

Mt.

CH

4 an

d N

2O E

mis

sion

s in

Mt.

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

69.065

84.893

103.41

144.52

380.67484.41

613.77732.26

0

20

40

60

80

100

120

140

160

0

200

400

600

800

1000

1200

1400

1600

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

Installed capacity(GW) Power Sector Total

CO

2 E

mis

sion

s in

Inst

alle

d C

apac

ity in

GW

.

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


Aggregate changes given by the chaining decomposition are,

ΔQ t0; :::::::; tkð Þ

¼Xks¼1

ΔQpollution ts−1; tsð Þ þ ΔQ intensity ts−1; tsð Þ þ ΔQstructure ts−1; tsð ÞþΔQactivity ts−1; tsð Þ þ ΔQpopulation ts−1; tsð Þ

� �

ð28Þ

¼ ΔQpollution t0; :::::::; tkð Þ þ ΔQ intensity t0; :::::::; tkð Þ þ ΔQstructure t0; :::::::; tkð ÞþΔQactivity t0; :::::::; tkð Þ þ ΔQpopulation t0; :::::::; tkð Þ:

ð29Þ

where, ΔQpollution(t0,.......,tk) is the chaining pollution effect,ΔQintensity(t0,.......,tk) 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 ofthe non-chaining and chaining decomposition can be calculated as

Δpollution ¼ ΔQpollution t0; tkð Þ−ΔQpollution t0; ::::::::::::; tkð Þ: ð30Þ

5. Results and discussions

5.1. Total sectoral emissions

Total sectoral emissions in terms of CO2, CH4 and N2O have been cal-culated. CO2 emissions have increased by 86% during this period. CH4

emissions have also increased (40%) but had decreased between1998–99 and 2003–04 mainly because of a decrease in usage of petro-leum products especially HSD during this period. N2O emissions have

49.14.2

104.67

41.4

160.5

227.6

124

0100200300400500600700800900

1000

AG

RIC

UL

TU

RE

MIN

ING

MA

NU

FAC

TU

RIN

G

EL

EC

TR

ICIT

Y, G

AS

CO

2e E

mis

sion

s in

Mt.

Fig. 3. Carbon dioxide equivalent emissions of maSource: Calculated from data on fuel consumptio

increased steadily (136%) during this period. Fig. 2 shows trends in sec-toral emissions between 1993–94 and 2006–07. Sectoral comparison ofCO2e emissions shows a steady increase for agriculture, manufacturingand electricity and gas sectors while there is an alternate increase anddecrease for the transport and service sectors (Fig. 3).

A comparison of percentage increases in emissions and GDP forthese sectors, reveals that emissions from agriculture are higher thanits GDP growth,while for other sectors it is not that high.Manufacturingsector has experienced an increase of more than 100% in emissions butwith a considerable growth in GDP.

5.2. Emissions from the electricity sector

CO2 emission from the power sector (Fig. 4) is almost half the totalemissions. CO2 emissions from the electricity sector increased from380. 67 Mt in 1993–94 to 732.26 Mt in 2006–07. Emissions fromusage of coal and lignite itself amount to 78% of CO2 emission in1993–94 which increased to 86% in 2006–07. Between 1993–94 and2006–07 total emissions increased by 86% while those from the powersector increased by 92%. Installed capacity increased by 109% over theyears, 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 householdconsumption have been decomposed into pollution, intensity, structure,activity and population effects. There has been a change of 66% (Actual

5.7

62.9

.4

188.5

0.0

50.0

100.0

150.0

200.0

250.0

AN

D W

AT

ER

SU

PPL

Y

TR

AN

SPO

RT

AN

DSE

RV

ICE

S

1993-94

1998-99

2003-04

2006-07

Emissions

GDPPer

cent

age

incr

ease

jor sectors between 1993–94 and 2006–07.n and GDP (at 1993–94 prices), from CSO, India.

0

50

1993

-94

1998

-99

2003

-04

2006

-07

100

150

200Pollution

Intensity

Structure

Activity

Population

Actual

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

-100

-50

0

50

100

Natural gas Petroleumproducts

Electricity

CO

2 E

mis

sion

s in

Mt

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 and2006–07.Source: Calculated from respective IOTT and emission data.


change, including direct and indirect) in household CO2 emissions be-tween1993–94 and 2006–07 (Fig. 5) brought aboutmainly by the activ-ity effect (76%), population effect (30%) structure effect (27%) andpollution effect (3%). Energy intensity has gone down by 60% duringthis period. Chaining analysis records higher values for all the effects,especially the pollution effect (higher by 166%). Therefore temporalaggregation 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 majorchanges since sectoral definitions have changed after 1998–99. Analysiswith aggregated values would have generated errors while analyzing atthe sectoral level. Therefore, to analyze in greater detail a second de-composition analysis was carried out between 2003–04 and 2006–07so that sectors could be identified which need to be addressed towardsreducing CO2 emissions. In the next section, each consumption categoryhas been analyzed for overall changes between 1993–94 and 2006–07,along with identification of sectors that have caused increasedemissions between 2003–04 and 2006–07.

5.3.1. Direct emission from household consumptionEmissions 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, secondaryenergy sectors show an increase in CO2 emission by 171% between1993–94 and 2006–07.

Sharp changes in declining energy intensity and increasing structureeffect are also noticed from 2003–04 onwards (Fig. 6).

Activity and structure effects are the major contributors towards anincrease in emissions from primary and secondary energies (Fig. 7).Rapid urbanization and changing lifestyles have influenced coal substi-tution with diesel and electricity. LPG as well as electricity is being usedfor cooking.

-50

0

50

100

150

200

250

300SE

-50

0

50

100

150

200PRIMARY ENERGY

1993

-94

1998

-99

2003

-04

2006

-07

1993

-94

Fig. 6. Index of total CO2 emissions from household consSource: Calculated from emission data.

Increasing number of private vehicles has led to rise in usage of pet-rol/motor gasoline for transport. Electricity usage for home illuminationand entertainment has also gone up with current trends. Sectoraldecomposition analysis between 2003–04 and 2006–07 reveals thatemissions are positive for Natural gas, Petroleum products and Electric-ity. Emissions have reduced for Coal and lignite indicating structuralchanges towards cleaner fuels. Compared to Natural gas and Petroleumproducts, pollution effect for electricity is highest at 7%. In these threeyears emissions have increased by 29% for electricity sector.

5.3.2. Indirect emission from household consumptionIndirect emissions have increased by 76% over 1993–94 values. As

seen from Fig. 8, falling intensity effect has been guided by activityand population effects towards structural shifts in emissions from ener-gy embodied in goods and services consumed by households. Between1993–94 and 2006-07 (Fig. 9), results for decomposition of changes re-veal that maximum increases have been brought about by consumptioncategories 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 inemissions. Medical care and hygiene have led to a decrease in CO2 emis-sions (2%) during this period (Fig. 9). Sectoral analysis of individual con-sumption categories reveals the following.

5.3.2.1. Food, beverage, tobacco and primary goods. Emission increaseunder this category has been caused by activity and population effect(Fig. 10). 2003–04 onwards structure effect is one of the major causesof decline alongwith intensity and pollution effects indicating that com-position of private consumption has changed from food to other goods.

In 2003–04, new sectors were added to this category like Other oil-seeds, Fruits, Vegetables, and Other crops. In 1998–99 they were allclubbed under Other Crops. Similarly, Poultry and eggs; Other livestock

CONDARY ENERGY Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actual change

1998

-99

2003

-04

2006

-07

umption of direct (primary and secondary energy).

-20

0

20

40

60

80

100

120

CO

2 E

mis

sion

s in

Mt

Fig. 9. CO2 emissions from different consumption categories between 1993–94 and2006–07.Source: Calculated from respective IOTT and emission data.

-30

-20

-10

0

10

20

30

40

Food crop Cash crop Othercrops

Beverages Tobaccoproducts

CO

2 E

mis

sion

s in

Mt

Pollution Effect

Activity Effect

Intensity Effect

Population Effect

Structure 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.

0

20

40

60

80

100

120

140

160

180

200PollutionEffect

IntensityEffect

StructureEffect

ActivityEffect

PopulationEffect

Actualchange19

93-9

4

1998

-99

2003

-04

2006

-07

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


products and gobar gas were included under Animal husbandry previ-ously. Sectoral decomposition analysis for the period between2003–04 to 2006–07 reveals that Food crops, Cash crops, Other crops,Beverages and Tobacco products are the sectors where emissions arepositive.

Increasing use of diesel and electricity for farm mechanization andirrigation purposes for Food crops, Tobacco and Other crops (Lal,2004) along with processing technologies for Beverages and Tobaccoproducts have increased pollution effect for these sectors (Fig. 11).

0

50

100

150

200

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

FOOD, BEVERAGE, TOBACCO & PRIMARY

GOODSPollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actual change

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

Reduction in energy intensity would bring about positive changes inemissions for the food crop sector. Rise in incomes leading to greaterconsumption of high value foods and cash crops like sugarcane, ground-nut, other oilseeds, jute, cotton and tobacco along with agriculturalpolicies have created economic opportunities for farmers (Fafchamps,1992; Von Braun and Kennedy, 1986). Changing lifestyles with a stronginfluence of work environment along with growing social interactionfor recreational purposes have increased people's consumption ofbeverages and tobacco products leading to increased fuel consumptionfor irrigation and food manufacturing requirements. Structural as wellas pollution effect reduction is required for Beverage sector.

5.3.2.2. Clothing and footwear. Activity effect is the major cause inincreasing emissions between1993–94 and 2006–07. Sectoral composi-tions under this category have remained the same from 1993–94.

After 2003–04, decline in emissions is engineered by falling intensityand structure effects (Fig. 12). Manufacturing processes have becomemore energy efficient but people's consumption patterns have shiftedaway from basic items.

Sectors under this category where emissions have increased be-tween 2003–04 and 2006–07 are Silk textiles; Art silk, synthetic fibertextiles; 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, aremuchmore energy intensive than natural fibres and therefore pollutionand intensity effects have increased emissions for Art, silk, syntheticfiber textiles. In case of Leather footwear, pollution effect has increasedemissions 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 growingrequirement for goods and services from contemporary markets.

-100

0

100

200

300

400CLOTHING AND FOOTWEAR

PollutionEffect

IntensityEffect

StructureEffect

Activity Effect

PopulationEffect

Actual change19

93-9

4

1998

-99

2003

-04

2006

-07

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

-9-8-7-6-5-4-3-2-1012345

Silk textiles Art silk,

synthetic

fiber textiles

Jute, hemp,

mesta textiles

Leather

footwear

CO

2 E

mis

sion

s in

Mt

Pollution Effect

Activity Effect

Intensity Effect

Population Effect

Structure Effect

Actual Change

Fig. 13.Causes of increased CO2 emissions from “clothing and footwear” between2003–04and 2006–07.Source: Calculated from respective IOTT and emission data.

-50

0

50

100

150

200LIFESTYLE EFFECTS

Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actual change

1993

-94

1998

-99

2003

-04

2006

-07

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


Consequently, the need for getting connected to the outside world hasalso grown over the years.

Emissions have increased after 2003–04. After 1998–99, Office com-puting and accounting machines sector was no longer a separate sectorunder this category. It was distributed partly between Miscellaneousmanufacturing 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 revealthat Wood and wood products; Leather and leather products; Rubberproducts; Batteries; Watches and clocks; Gems and jewelry and Com-munication are the sectors where emissions have increased. Alongwith activity and population effects, pollution and structure effects arepositive for Wood and wood products; Leather and leather products;Rubber products; Watches and clocks; Gems and jewelry and Commu-nication. Batteries sector is unique for which all the effects are positive.Economic reforms4 havemodernized people's way of living and therebyinitiated a structural shift towards lifestyle enhancing goods. This hasled to a positive component for pollution effect of the sectors contribut-ing to household and other consumer goods (Fig. 15).

5.3.2.4. Education and research. Emissions have increased mainly be-cause of activity effects. Decreases in structure and intensity effectsfrom 1998–99 onwards have been able to check the increasing trendsin emissions. However there has been a slight increase after 2003–04.Computer and related activities initially included within Other servicesunder “medical care and hygiene” in 1998–99 was a new addition tothis category from 2003–04 onwards (Fig. 16).

Decomposition analysis between 2003–04 and 2006–07 revealsPaper, paper products and newsprint; Education & research; and Com-puter & 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 & researchsector has positive pollution effects although intensity and structureeffects have decreased emissions. Growing literacy among people hasmade people more inclined to spend on goods and services related toeducation. Therefore, positive structural effect for Paper, paper products& newsprint and Computer & related activities leads to increasedemissions. Improved energy efficiency is required for the paper

4 Economic reforms in India started during 1991.Main objective of the governmentwasto initiate a systemic shift to a market oriented open economy, where the private sectorincluding foreign investment played a major role (Ahluwalia, 2002).

manufacturing sector since developed nations require far less energyfor paper production (Schumacher and Sathaye, 1999).

5.3.2.5. Medical care and hygiene. “Medical care and hygiene” is the onlycategory where emissions have reduced mainly because of intensityeffects. Consumption of medicines had gone up till 1998–99 but afterthat there has been a structural shift away from this category indicatingimproving health conditions. Energy intensity declined majorly after1998–99 but has slightly increased from 2003–04 onwards. Pollutioneffect also increased from 2003–04 (Fig. 18).

In 1998–99 the category constituted a sector called Other serviceswhich is a combination of all personal services.

However with growth of the service sector in 2003–04, new servicesectors were added and the new constituent was Other commercial, so-cial and personal services to account for similarity between 1998–99and 2003–04. After 2003–04, increase in emissions has been broughtabout by Soaps, cosmetics & glycerin and Medical & health services(Fig. 19). Lifestyle changes indicate a positive structural effect forthese two sectors.

5.3.2.6. House building. “House building” is the only category whereintensity effect has increased and is the major reason for increasedemissions (Fig. 20). Increasing use of electricity for industrial processeshas contributed towards this positive intensity effect from 1998–99onwards. However pollution effect declined after 1993–94 because ofcoal substitution by diesel and electricity but has been increasing after2003–04. Real estate activities was a new sector added to this categoryin 2003–04. Initially it was included into Other services.

2003–04 onwards, increased emissions have been brought about byOther non-electrical machinery, Construction and Real estate activities.Construction and Other non-electrical machinery are two sectors forwhich all the effects are positive. CO2 emissions from Real estate activi-ties have gone up mainly because of structure and pollution effectsother than the common effects of population and activity. Rising popu-lation, need for newhousing and construction of newhouses havemadethe structural effect positive for this consumption category (Fig. 21). Ce-ment and steel which are raw materials of the construction industryboth account for high amount of CO2 emissions. Cement manufacturingitself contributes to CO2 emission because of calcinations of limestoneduring production of cement. Total amount of CO2 emitted per ton of ce-ment production ranges from 0.74 ton to 1.24 ton as reported by differ-ent 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 cleanerfuels for manufacturing. However continuously decreasing structure ef-fect after 1998–99 has raised the intensity effect after 2003–04. Sectoralcompositions have remained unchanged after 1993–94.

-3

-2

-1

0

1

2

3

Wood and woodproducts

Leather andleather products

Rubber products Batteries Watches andclocks

Gems & jewelry Communication

CO

2 E

mis

sion

s in

Mt

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.

0

50

100

150

200

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

EDUCATION AND RESEARCH

Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actual change

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

0

20

40

60

80

100

120

140

160

180MEDICAL CARE AND HYGIENE

Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actual change

1993

-94

1998

-99

2003

-04

2006

-07

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


Miscellaneous metal products, Electrical appliances and Communi-cation equipments are the sectors responsible for increased emissionsbetween 2003–04 and 2006–07 (Fig. 23). Activity and population arethe positive drivers of change in all of them. Intensity and pollutioneffects are positive for the sectors Miscellaneous metal products andCommunication equipments. For Electrical appliances, structure andpollution effects are positive. Growing use of electricity for electricalappliances at home has a strong structural effect on increase in CO2

emissions for the Electrical appliances sector.

6

8
5.3.2.8. Recreation. Emissions have increasedmainly because of structureand activity effects (Fig. 24).
-1.5

-1

-0.5

0

0.5

1

1.5

Paper, paperprods. &newsprint

Education andresearch

Computer &related

activitiesCO

2 E

mis

sion

s in

Mt

Pollution Effect

Activity Effect

Intensity Effect

Population Effect

Structure Effect

Actal Change

Fig. 17. Causes of increased CO2 emissions from “education and research” between2003–04 and 2006–07.Source: Calculated from respective IOTT and emission data.

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

-10

-8

-6

-4

-2

0

2

4

Soaps,cosmetics &

glycerin

Medical andhealth

CO

2 E

mis

sion

s in

Mt

Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actal Change

Fig. 19. Causes of increased CO2 emissions from “medical care and hygiene” between2003–04 and 2006–07.Source: Calculated from respective IOTT and emission data.

0

50

100

150

200

250

300

350

400HOUSE BUILDING

Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actual change19

93-9

4

1998

-99

2003

-04

2006

-07

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

0

20

40

60

80

100

120

140

160

180

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

HOUSING AND LIFESTYLE

Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actual change

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


All the effects are positive for Electronic equipments (includingTelevision). Intensity effects have decreased but other effects have in-creased net emissions for Hotels and restaurants. Modern lifestyle hasmade, recreation both at home and outside it, a part of life. Thereforestructure 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 increasingemissions for “transport” between 1993–94 and 2006–07 (Fig. 26). De-creasing trends are visible from 1998–99 mainly because of decliningenergy intensity. Sectorally, from 2003–04 there was diversification ofOther transport services to different sectors for Land transport includingvia pipeline; Water transport; Air transport and Supporting & auxiliarytransport activities.

Sectors which have contributed to increased emissions after2003–04 (Fig. 27) are Motor cycles and scooters; Bicycles, cycle-rickshaw; Land transport including via pipeline; Water transport andsupporting andAuxiliary transport activities. Activity andpopulation ef-fects are positive for all the sectors. Structure and pollution effects arepositive for Motor cycles and scooters; Land transport via pipeline andWater transport. Need for fast conveyance has made the structuraleffect negative in case of Bi-cycles and cycle rickshaw. Use of diesel inthe transport services sector is the main cause of increasing CO2

emissions.

5.3.2.10. Other personal services. “Other personal services” (4.4%) has alsocontributed to increased emissions mainly because of activity effect.

-2

0

2

4

6

8

10

12

14

16

18

Other non-electrical

machinery

Construction Real estateactivities

CO

2 E

mis

sion

s in

Mt

Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actal Change

Fig. 21. Causes of increased CO2 emissions from “house building” between 2003–04 and2006–07.Source: Calculated from respective IOTT and emission data.

Decreasing trends are visible from 2003–04. Energy intensity effectshave been negative after 1993–94. The service sector has grown after1998–99. Addition of new service sectors like Business services; Legalservices; Renting of machinery and equipment and Other serviceshave taken place from 2003–04.

Service sectors like Insurance, Business services, Legal servicesand Other services had increased emissions between 2003–04 and2006–07. Activity and population effects are positive for all sectors(Fig. 28).

Structure and pollution effects are positive for Insurance and Busi-ness services (Fig. 29). Intensity and pollution effects are positive forLegal services. For Other services it is pollution effect that is the sole con-tributor to increases in CO2 emissions. Structure effect is positive asmore people enterprise for Business and Insurance services. Pollutioneffect is positive as increasing amount of diesel is being used for trans-portation related to services sector.

From the findings above it can be thus concluded that alongwith ac-tivity and population effects, structural effects have caused pollution ef-fects to increase in categories of “lifestyle effects”, “education andresearch”, “recreation”, “transport” and “other personal services”. Inthese categories intervention is required regarding use of cleanerfuels. The “house building” category needs intensity reduction alongwith structural changes.

A study on Italian households reveals that “Agriculture, hunting andsylviculture” and “road transports” are sectors affecting air emissions(Cellura et al., 2012). Studies for other countries in this decade revealthat increases in household emissions are caused by growing numberof households, intensity effects (Chung et al., 2011; Zhu et al., 2012);final demand (Cellura et al., 2012; Zhang et al., 2013); per capita energyconsumption (Zhang et al., 2013); structural changes towards more en-ergy intensive consumption and cleaner fuels (Zhao et al., 2012) and en-ergy end-use mode choice (Fan et al., 2013).

-2

-1

0

1

2

3

4

5

Miscellaneousmetal products

Electricalappliances

Communicationequipments

CO

2 E

mis

sion

s in

Mt Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actal Change

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

0

100

200

300

400

500

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

RECREATIONPollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actual change

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

-10

-5

0

5

10

15

Electronic

equipments(incl.TV)

Hotels and

restaurantsCO

2 E

mis

sion

s in

Mt

Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population

Effect

Actal Change

Fig. 25. Causes of increased CO2 emissions from “recreation” between 2003–04 and2006–07.Source: Calculated from respective IOTT and emissions data.

-80

-60

-40

-20

0

20

40

Motor cyclesand scooters

Bicycles, cycle-rickshaw

Land tptincluding via

pipeline

Watertransport

Supportingand aux. tpt

activities

CO

2 E

mis

sion

s in

Mt

Pollution Effect

Activity Effect

Intensity Effect

Population Effect

Structure Effect

Actal Change

Fig. 27. Causes of increased CO2 emissions from “transport” between 2003–04 and2006–07.Source: Calculated from respective IOTT and emissions data.


The next section discusses about energy policy options for theseconsumption categories as well as sectors comprising them whichneed attention.

6. Energy policy options

CO2 emissions (direct and indirect) from household consumption ofgoods and services have increased between 1993–94 and 2006–07 by66%. Mostly it is activity and population effects that have a positive im-pact on emission increase as mentioned in works of other researchersfor India (Mukhopadhyay, 2001; Nag and Parikh, 2000). CO2 emissionsare related to amount and type of energy consumed. Therefore proper

-100

199.

..

199.

..

200.

..

200.

..

-50

0

50

100

150

200

250TRANSPORT

Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actual change

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

energy policy guidelines can help to reduce CO2 emissions fromdifferent consumption categories.

The study period concerned falls within the planning of 8th(1992–97), 9th (1997–2002) and 10th (2002–07) Plan periods of thePlanning Commission. Sustainable development, deregulation of energyprices,market basedpricing alongwith increasing emphasis on demandmanagement, energy security, conservation and efficiency have beenpart of the energy strategy from the 8th Plan itself. In order to restrictoil imports andmaintain energy security, progressive substitution of pe-troleum products by coal, lignite, natural gas and electricity had beensuggested. Renewable energy application was also promoted. In 2001,the Energy Conservation Act was formulated with the setting up ofthe Bureau of Energy Efficiency (BEE). Labeling programs were takenup to provide decision makers and consumers with information on en-ergy efficiency. Energy efficient technologies were targeted for indus-tries like iron, steel, chemicals, petroleum, pulp and paper and cement.Recycling was part of the demandmanagement programwhich includ-ed energy conservation, optimum fuel mix, structural changes in theeconomy, appropriate modal mix in transport, greater reliance on co-generation and reduction of material intensity (9th Five Year Plan).During the 10th Plan, alongwith energy conservation, energy efficiency,energy security strategies also included development of alternativefuels and usage of clean fuels.

Therefore, based upon the decomposition analysis (2003–04 and2006–07), fuel conservation and fuel substitution exercises werecarried out to plan for reduction in emissions for the latest year2006–07. CO2 emissions are a function of pollution and intensity co-efficients along with structure, activity and population. Sectors

0

20

40

60

80

100

120

140

160

180

200OTHER PERSONAL SERVICES

Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actual change

1993

-94

1998

-99

2003

-04

2006

-07

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

-2

-1

0

1

2

3

4

5C

O2

Em

issi

ons

in M

t

Pollution Effect

Intensity Effect

Structure Effect

Activity Effect

Population Effect

Actal ChangeIn

sura

nce

Busine

ss se

rvice

s

Lega

l ser

vices

Other

serv

ices

Fig. 29. Causes of increased CO2 emissions from “other personal services” between2003–04 and 2006–07.Source: Calculated from respective IOTT and emissions data.


having positive pollution effects can have lower emissions if thevalue of pollution coefficient reduces. Therefore, using a cleanerfuel keeping the amount of energy used to be same or fuel substitu-tion can reduce emissions. Use of renewable energy also reduces pol-lution coefficient since they are cleaner sources of energy. Likewise,sectors having positive intensity effects can have lower emissions ifthe value of energy intensity coefficient reduces. Therefore, reducingenergy usage keeping the output same or fuel conservation can re-duce emissions. This also leads to energy efficiency in the economy.Recycling also reduces intensity coefficient since it reduces energyintensity of processing newmaterial. Similar viewpoints on reducingemissions by reducing the pollution coefficient and intensity

Food CropFood, Beverage, Tobacco

and Primary goods

Art silk, synthetic

fiber textiles

Clothing and

footwear

Batteries Lifestyle Effects

Other non-electrical

machinery; ConstructionHouse building

Miscellaneous metal products;

Communication equipments

Housing and

lifestyle

Electronic equipments

(including TV)

Recreation

Legal Other personal

services

Fuel Savi

10% Coal

Energy effi

efficient pu

Energy effi

speed frame

simplex fra

fans in wea

spinning mi

Industrial b

and concret

concrete by

Energy effic

Industrial u

speed drive

Companies

Consumption CategoriesSectors with positive

Intensity Effect

Fig. 30. Energy policy framework for sectors having positive pollution effect in the decomSource: Author's analysis

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

6.1. Reducing intensity effect - energy conservation and recycling

We assume a saving of 10% of the amount of coal being used in in-dustries. The useful energy in coal is also assumed to be saved from oiland natural gas as well. Conversion efficiency was considered in theratio of the net calorific values for respective fuels. Therefore, coalwith a net calorific value of 19.6 TJ/kt was substituted by oil andnatural gas. Oil to coal conversion efficiency was estimated to be2.19 for HSD/LDO, 2.06 for FO/LSHS and 1.92 (1 MCM of natural gas to1 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 petroleumproducts. Consequently savings generated in case of coal is only 4186million Rs as compared to 50,020 million Rs for petroleum products.Therefore from the point of view of GHG reduction and climate changeissues, saving coal is a much better option than saving oil and naturalgas. It needs to bementioned here that this is in deviation from our gov-ernment energy policywhich calls for saving oil rather than coal. Savingnatural gas does not put forth an advantageous scenario in either emis-sion or monetary savings. Similar results were also derived for theIndian economy in 1983–84 (Parikh and Gokarn, 1993). Recycling canto some extent offset the need for new energy. Fig. 30 gives the energypolicy options for sectors with positive intensity effect between2006–07 and 2003–04.

Reduce Intensity Effect

Energy conservation Recycling

Recycling

electronic

waste

Recycling

and re-use

batteries

ng –

Energy Efficiency

cient cropping, less irrigation intensive crop varieties,

mping systems

cient soft flow dyeing, installation of photocells for

, installation of soft starter-cum-energy saver in

mes, installation of FRP fan blades for humidification

ving and use of energy efficient pneumafil fans in

lls (Velavan et.al., 2009)

y-products like blast furnace slag and fly ash in bricks

e. Large-scale mechanization and production of

engineered means (Bhattacharjee B., 2010).

ient construction practices.

se of energy efficient motors, efficient boilers, variable

s, following policy formulations by Energy Saving

(Integrated Energy Policy, 2006)

position analysis between 2003–04 and 2006–07. Data from: Velavan et al. (2009).


It needs to bementioned here that except Food crops (62%)which isan essential component in household consumption, major emissionsavers would be “house building” (19%), “recreation” (7%), “clothingand footwear” (6%), and “housing and lifestyle” (5%).

Energy efficiency measures in cropping, textile dyeing andmanufacturing of building materials would help in reducing energy re-quirements for “food, beverage, tobacco and primary goods”, “clothingand footwear” and “house building”.

Recycling and reuse of batteries and electronic wastes can reduceusage of processing energy for new materials. Energy for processing ofbattery raw materials is reduced by 65% compared to virgin materialsthrough recycling. Although energy use in battery manufacture activityremains constant irrespective of recycling rate, for a NiCd battery lifecycle, complete or near complete recycling reduces CO2 emissionsfrom 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 prac-tices are not entirely environmental friendly (Eckfeld et al., 2003).Growing motor and motor-cycle industry would add to the increase indemand for batteries in future. Therefore environment complianttechnologies for battery recycling should be undertaken. Recycling elec-tronic 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 fuelswitching by keeping the net amount of energy used to be same. Changein the fuel type brings about changes in cost incurred aswell as changesin emissions. Since oil is expensive compared to coal, India follows a pol-icy of oil being substituted by coal. However, two other scenarios of coalbeing substituted by oil and oil being substituted by natural gas werecreated to see which option generates lesser emissions. It should bementioned here that only relative prices of fuel are considered here,

Sectors with positive Pollution Effect

Food Crop; Other crops; Beverages; Tobacco Products Food, Beverage, T

and Primary goodSilk textiles; Art silk, synthetic fiber textiles; Jute,

hemp, mesta textiles; Leather footwearClothing and foo

Wood and wood products; Leather and leather

products; Rubber products; Batteries; Watches and

clocks; Gems and jewelry; Communication

Lifestyle Effe

Paper, paper products & newsprint; Education and

research; Computer and related activities

Education and resear

Soaps, cosmetics & glycerin; Medical and health

Medical care

health

Other non-electrical machinery; Construction; Real estate activities

Hous

BuildMiscellaneous metal products; Electrical

appliances; Communication equipmentsHousing and lifest

Electronic equipments (including TV); Hotels and restaurants Recreati

Motor cycles and scooters;

Bicycles, cycle-rickshaw;

Supporting and auxiliary transport

activities

Trans

Insurance; Business services; Legal services

Other pe

services

Natural gas; Petroleum products; Electricity Ener

Other services

Consumption Cat

Land transport including via

pipeline; Water transport

Fig. 31. Energy policy frameworkSource: Authors' analysis

while there are other costs involved like cost of changing infrastructure,reduced usage of petroleum products and consequent income and em-ployment effects, and change in the nature of pollutants (Parikh andGokarn, 1993). Results show that substituting coal with oil decreasesemissions for all categories except “transport” and “other personal ser-vices” categories. Therefore for Land transport including via pipeline;Water transport and Other services substituting oil with natural gas isa better option in terms of reducing emissions. Monetarily, althoughsubstituting coal with oil is a disadvantage since oil is expensive, aswell as imported, offsetting oil usagewith natural gas generates savings.Therefore a combined approach of substituting oil with natural gas for“transport” and “other personal services” related sectors and coal withoil 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 emissionsunder base case. Monetary savings are 140,230 million Rs, which isaround 4% of the money spent under base case.

Additional savings in emissions can be realized through use of re-newable energy (Panwar et al., 2011) from investments of themoney saved from fuel substitution. Solar hot water can be used inindustrial 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 usedfor this purpose in Ranipet, Tamil Nadu, India. Solar and windbased electricity generation can provide clean energy to homes forlighting, water heating and cooking. Building integrated photovol-taics (BIPV) can offset emissions from coal based electricity genera-tion in homes.

Total emissions saved through energy conservation and fuel substi-tution 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 in2006) per Mt of CO2 not emitted.

obacco

s

twear

cts

ch

and

e

ing

yle

on

port

rsonal

gy

Energy substitution Renewable Energy

Substitute coal usage

with petroleum products

Substitute petroleum products usage

with natural gas – replace diesel

with CNG or use clean diesel

Solar hot water for

textile; hot air for

leather drying

Solar, wind energy for

home electrification,

cooking & water heating

Solar hot water for food,

tobacco and breweries;

Hot air for drying

Solar hot water for industrial

processes in paper,

machinery, chemicals

egories

for reducing pollution effect.


7. Summary and future scope

The final decomposition for 2003–04 and 2006–07 reveals thatsectors like Batteries, Other non-electrical machinery; Construction;and Electronic equipments (including television) for which all the effectsare positive, need to adopt combined strategies of technologyupgradation, energy conservation, fuel switching and market manage-ment in order to reduce CO2 emissions. Efforts to reduce greenhousegas emissions from fossil fuel use address two principal objectives of a)mitigation of climate change and b) improvement of energy security. Ef-ficient use of energy needs to be combinedwith cost efficiency to help inpolicy level interventions. Proper pricing mechanism should be lookedinto to dissuade people from fuelswhich lead to increased CO2 emissions.

A few limitations to themethodology followed includeworkingwiththe total production coefficient matrix following the assumption ofcompetitive imports (Su and Ang, 2013) which includes embodiedemissions within imported raw materials. The basic input–outputmodel is linear in structure, with the assumptions of fixed proportionsof inputs and constant returns to scale (Pearson, 1989). Thismay not re-late to a world which is non-linear in nature. Fuel substitution exercisedoes not look into costs associated with changes in technology, laborand new type of emissions. Moreover the complete decompositionmethod by Sun becomes slightly more tedious when the number offactors is more than five.

However, future studies could look at – (i) sector specific studies toassess CO2 emission reduction possibilities and how they affect our con-sumption patterns, (ii) studies on household electricity consumptionpattern in detail to find outwhether the usage is optimumor indiscrim-inate (guided by the activity effect), (iii) use of non-conventional energyby households and (iv) calculation of domestic emissions subject to theavailability of competitive importmatrices and therefore findout its im-plications on bilateral trade and embodied emission transitions.

Acknowledgement

Wewould 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.

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