The Carbon Footprint of the Rising Indian Middle Class Nicole Grunewald, Mirjam Harteisen, Jann Lay and Jan Minx, Sebastian Renner

2 Thesis Analyzed Brazil, China and India account for a higher share of global GHG emissions

3 Thesis Analyzed Aggregation Theory (Heerink, Mulato and Bulte, 2001): aggregation over households with unequal incomes leads to rising emissions with rising incomes Research Questions: –What are the main determinants of the growing carbon footprint in those fast growing emerging countries? –Hence is there the possibility of redirecting consumption towards “greener growth”? Income Emissions

4 Related Literature Parikh et al. (1997) estimate CO 2 requirements / carbon footprint of households in India with different incomes, rural and urban, estimations for 1990 and 2010 –Differing energy intensities enable CO2 reductions by changing consumption patterns Lenzen (1998) analyze energy and carbon footprint of households in Australia –The indirect energy and GHG demand matters more (65%) Pachauri and Spreng (2002) analyze energy requirements of households in India for –Higher energy requirements due to rising income and population

5 Related Literature Pachauri (2004) analyzes energy requirements of households in India controlling for household characteristics in 1993 and 1994 and divide 9 consumption categories –Income matters but characteristics as well Bin and Dowlatabadi (2005) analyze energy and CO2 requirements of households in the US in 1997 and divide 10 consumption categories –Indirect energy requirements represent the larger share Lenzen et al. (2006) analyze the effect of rising income on per capita energy demand in Australia, Brazil, Denmark, India and Japan –No Kuznets type relationship, energy elasticities vary across countries

6 Related Literature How are we different? –We apply more recent data –Analyze the consumption groups more disaggregated and focus on the main drivers of GHG like: meat consumption, transport –We will look at different waves of the NSS expenditure survey in India –We will be able to confirm or reject to results of Parikh et al. (1997)

7 Methodology Input Output Energy Analysis with household expenditure data –Input Output Tables to estimate cumulative energy and GHG emission intensities of Indian sectors –Merge with expenditure survey data to estimate carbon footprint of households –Analyze the carbon footprint of different income groups –Compare changes over time

8 Methodology Data and operations needed for the three different energy analysis methods: - IO-EA-basic - IO-EA-expend. - IO-EA-process Source: Kok et al:2006, 2749.

9 Data NSS survey on Indian household expenditure –Representative survey of India consisting of households –Data on overall household expenditure disaggregated to around 500 items –Household characteristics like: rural or urban, type of housing, income class, status, size, religion

10 Data Mayor expenditure shares of high and low income households in India in 2002

11 Preliminary Results Which consumption items exhibit high carbon intensities? –Meat products have a much higher carbon intensity than vegetarian products –Fossil fuels and electricity –Transport public as well as private –A change in consumption pattern could reduce emissions (Parikh et al. 1997) How do expenditure elasticities vary between low and high income households? –Low income households show higher expenditure elasticities than high income households –As poverty declines rising emissions will be observed (Pachauri and Spreng 2002)

12 Preliminary Results Egg-Fish-Meat Expenditure Elasticities High IncomeUpper-middle ILower-middle ILow Income VARIABLESlpcexp_egg_fish_meat Log(Exp_pc)0.594***1.320***1.040***1.409*** (0.0274)(0.0723)(0.101)(0.106) SEC *-0.299*** *** (0.0280)(0.0436)(0.0456)(0.0512) STATE_REG *** ** *** ( )( )( )( ) DIST *** *** *** *** ( )( )( )( ) HHD_TYPE ** ( )( )( )(0.0102) RELIGION0.0853***0.110***0.0604***0.0551** ( )( )(0.0121)(0.0235) SOCIAL_GROUP *** **0.0180***0.0225*** ( )( )( )( ) LAND-1.88e e e e-07 (4.85e-08)(8.60e-08)(9.21e-08)(2.13e-07) POSSESSED-3.17e-07*5.85e-07**5.66e e-07 (1.82e-07)(2.62e-07)(3.50e-07)(8.13e-07) DWELLING_CODE0.0136** * ( )( )( )(0.0132) TYPE_DWELLING *** * *** ( )( )( )( ) Constant1.868***-6.268***-3.613***-7.532*** (0.291)(0.743)(1.022)(1.043) Observations8,2116,4054,4581,857 R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

13 Preliminary Results Fuel-Light Expenditure Elasticities High IncomeUpper-middle ILower-middle ILow Income VARIABLESlpcexp_fuel_and_light Log(Exp_pc)0.561***0.717***0.908***0.909*** (0.0148)(0.0339)(0.0443)(0.0527) SEC0.203***0.197***0.125***0.0746*** (0.0151)(0.0208)(0.0211)(0.0241) STATE_REG *** *** *** * (4.49e-05)(4.88e-05)(5.55e-05)( ) DIST *** *** *** ( )( )( )( ) HHD_TYPE ( )( )( )( ) RELIGION0.0345***0.0218***0.0131** ( )( )( )( ) SOCIAL_GROUP *** *** ( ) ( )( ) LAND-5.74e-08**-1.82e-07***-1.71e-07**-3.04e-07*** (2.82e-08)(2.89e-08)(7.64e-08) POSSESSED8.78e-07***-9.13e e e-07 (1.24e-07) (1.58e-07)(2.66e-07) DWELLING_CODE *** *** *** ( )( )( )( ) TYPE_DWELLING ***-5.15e ( )( )( )( ) Constant2.308***0.577*-1.343***-1.356*** (0.157)(0.349)(0.448)(0.518) Observations13,0989,5716,7973,051 R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

14 Outlook Next steps: –Merging our expenditure data with the emission intensities –Extending the analysis to two further waves to analyze changes over time