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

2. 2 Starting point Research Questions:  What are the main determinants of the growing carbon footprint in fast growing emerging countries?  Which consumption categories are the most carbon intensive, which one are the least intensive?  How will carbon emissions develop over time when household income increases? Unique features:  First detailed view of household carbon footprint in developing countries  Valuable insight for future climate & development research by adopting a micro perspective on carbon emissions

3. 3 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.

4. 4 Data NSS survey on Indian household expenditure 2004-05 –Representative survey of India consisting 125000 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 IO Tables from Central Statistical Organization 2004 –The whole Indian economy disaggregated to 130 sectors

5. 5 Descriptive Analysis

6. 6

7. 7

8. 8

9. 9 Preliminary Results ●Expenditure categories with high carbon intensity and growing share of total expenditures:  fuel and light: coal, electricity etc.  Entertainment: recreation goods  Toilet articles: soap, shampoo etc.  Clothing ●Expenditure categories with low carbon intensity and growing share of total expenditures:  Consumer services: telephone charges, barber etc.  Education: books, school fees etc.

10. 10 Income Elasticities

11. 11 Bias in OLS estimates

12. 12 Regression results (All India) IV Estimation High carbon intensity, high income elasticity Low carbon intensity, high income elasticity (1)(2)(3)(4)(5)(6) LbevLfuellightLentertltoilltranslperg LY1.253 *** 0.937 *** 1.563 *** 1.008 *** 1.365 *** 1.171 *** (0.000) N1003251013363657210124117239866 p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 (7)(8) leduclserv LY1.488 *** 1.868 *** (0.000) N4926397877 p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

13. Regression results II IV Estimation Income elasticities differ for rural population: and urban population: 13 (1)(2)(3)(4)(5)(6) lbevlfuellightlentertltoilltranslperg LY1.344 *** 0.871 *** 1.629 *** 1.052 *** 1.514 *** 1.303 *** (0.000) N6278763764169656354310946517 p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 (1)(2)(3)(4)(5)(6) lbevlfuellightlentertltoilltranslperg LY0.941 *** 0.960 *** 1.159 *** 0.908 *** 1.498 *** 1.140 *** (0.000) N375383757219607376986293349 p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

14. Outlook Next steps for Indian data: –Extending the analysis to two further waves (including recent 2009/2010 survey) to analyze changes over time –Select categories to analyze in more detail Apply approach to other countries (Indonesia, Mexico are planned) Possibly back up IO carbon analysis with process analysis to make sure calculated carbon intensities are correct 14