1. De-carbonization and Development Path: Analysis of Household Carbon Footprint in Indonesia and the Philippines M. Iqbal Irfany Moises Neil Seriño Entdeken Project Meeting 16.10.12

2. Outline Introduction Research questions Literature review Methods Preliminary findings Conclusion and further works

3. Introduction By consuming goods and services, households directly and indirectly contribute to the rising emissions. (Recall, e.g. more than 80% of the energy use and the CO 2 emission in US are a consequence of consumer demands and their supporting activities (Bin and Dowlatabad (2005)). “Aggregation theory” argues that aggregation over households with unequal incomes leads to rising emissions with rising incomes (Heerink, Mulato and Bulte, 2001). However, the literature glance shows that there are relatively more studies on household carbon footprint were conducted in the developed countries compared to developing countries, including Indonesia and the Philippines.

4. Introduction Environmental Kuznets Curve (EKC) Hypothesis – Proposes an inverse U formed relationship between per- capita income and environmental degradation. – For a given society, the environmental pressure is expected to increase in the early stages of growth, but eventually to reach a peak and decrease as income exceeds a certain level (the turning point). Income Emissions

5. Research Objectives 1.What is the characteristic of HH carbon consumption in Indonesia and the Philippines? How does it differ in term of income, location, etc? 2.What are the main determinants of the growing HH carbon footprint in Indonesia and the Philippines? 3.Which consumption categories are the most carbon intensive, and vice versa? 4.How will carbon emissions develop over time when HH income increases?

6. Fill the gap by estimating the detailed view on HH carbon footprint in two developing (emerging ) economies: Indonesia and the Philippines Attempt to account the HH emission of developing countries from the micro perspective. Novelties

7. Related Literatures Parikh et al. (1997) estimate carbon footprint of HH in India with different incomes, rural and urban, estimations for 1990 and 2010 –The rich have a more carbon intensive lifestyle compared to the poor. –Differing energy intensities enable CO2 reductions by changing consumption patterns Lenzen (1998) analyzes energy and carbon footprint of HH in Australia –The indirect energy and GHG demand matters more (65%) Pachauri and Spreng (2002) analyze energy requirements of households in India for 1983-1994 –Higher energy requirements due to rising income and population

8. Related Literatures Pachauri (2004) analyzes energy requirements of HHs in India controlling for household characteristics in 1993 and 1994 and divide 9 consumption categories –Income matters but characteristics as well Bin and Dowlatabad (2005) analyze energy and CO2 requirements of households in the US in 1997 and divide 10 consumption categories –More than 80% of the US energy and CO2 emitted are a consequence of consumer demand and its economic activities supporting these demand –Indirect energy requirements represent the larger share

9. Methodology Computing carbon intensities for all national IO sectors: incorporating emission data and IO Apply single region IO model based on the GTAP. Account for direct and indirect emissions (from goods produced and consumed) and emissions from imported goods. Matching GTAP emission database (57 sectors) with IO table to get (sectoral) emission intensities. Computing household carbon footprint: Combine carbon intensities with household demand structure: Direct and as indirect emissions from consumption. Match the sectoral emission intensities (the same sector as IO table) with corresponding expenditure categories (from the HH expenditure survey).

10. CO2 Emission (GTAP) Coefficient Matrix (I-O Table) Expenditure Categories (Household Surveys) 111 222 333...... 57 175 (INDO) or 240 (PHI) 200+ Matching scheme Source: Kok et al (2006), modified IO-Expenditure approach Methodology

11. Data CO2 emission data GTAP’s “Emission Data Base for Integrated Assessment of Climate Change Policy “ https://www.gtap.agecon.purdue.edu/resources/search.asp https://www.gtap.agecon.purdue.edu/resources/search.asp Data of CO2 emissions from fossil fuel combustion (coal + oil + gas + petroleum products + electricity + gas manufacture and distribution), based on GTAP version 7 (2004 database). Indonesia  National Socio-economic Household Survey (SUSENAS), 2005 and 2010  278,352 Households.  More than 350 items of HH expenditure  Indonesian IO Tables (Indonesian statistical board/BPS, 2005)  175 sectors The Philippines  Family Income and Expenditure Survey (FIES), 2003  39,615 Households.  More than 200 items of HH expenditure  Philippine IO Tables (National Statistics Office/NSO, 2000)  240 sectors

12. The Determinants of HHs Carbon Footprint where: CO2 i is the (log) carbon emissions by each household i Income i is measured as total expenditure of household i and in the following results it is (also) represented by income quintiles HH_char i are the household characteristics such as location (urban- rural), gender of the household head, educational attainment, household size, share of fuel expenditure u i is the error term

13. Preliminary findings

14. Results: Highest 20 CO 2 emission intensive economic sectors Top 20 Sectors (INDONESIA) CO 2 Emission intensity (kt CO2/Rp 1000000) Electricity and gas0.00104962 Cement0.00044619 Other items of non-metallic materials0.00039552 Glass and glass products0.00038542 Ceramics and building materials from clay0.00037331 Ceramics and items made of clay0.00036825 Air transport services0.00020421 Railway services0.00017156 Marine transportation services0.00016338 River and lake transport services0.00016153 Clean water0.00015220 Iron and steel basic0.00013891 Items of basic iron and steel0.00013308 Liquefied natural gas (LNG)0.00012828 Road transport services0.00011149 The products of oil refinery0.00011093 Musical instruments0.00010874 Transport support services0.00010795 Other industry products0.00010044 Jewelry0.00009785 Top 20 Sectors (PHILIPPINES) CO 2 Emission intensity (kt CO 2 / 1000 Pesos) Manufacture of structural concrete products0.00021953 Manufacture of other glass and glass products0.00020357 Manufacture of glass container0.00019960 Manufacture of other non-metallic mineral prod0.00019859 Cement manufacture0.00019632 Chromite mining0.00019439 Electricity0.00018682 Manufacture of structural clay products0.00017893 Manufacture of pottery,china and earthenwares0.00016653 Manufacture of flat glass0.00016092 Coal mining0.00015539 Steam0.00011048 Air transport0.00010073 Manufacture of ice, except dry ice0.00006080 Tour and travel agencies0.00005810 Activities of other transport agencies0.00005700 Railway transport0.00005522 Road freight transport0.00005391 Jeepney, tricycles and other road transport0.00005232 Public utility cars and taxicab operation0.00005173 Source: authors‘ computation based on GTAP-EA and National I-O tables

15. Lowest 20 CO 2 emission intensive economic sectors Top 20 Sectors (INDONESIA) CO2 Emissiton Intensity (kt CO2/Rp 1000000) Fiber crops0.000000312 Grains and other foodstuffs0.000000781 Sweet potato0.000001018 Fruits0.000001849 Bean0.000002180 Vegetables0.000002658 Cassava0.000002798 Soybean0.000002865 Other animal products0.000003744 Other nuts0.000003795 Corn0.000004498 Rice0.000004673 Building and land rent0.000005083 Livestock & their products except fresh milk0.000005672 Film and distribution services of private0.000006331 Meat, offal and the like0.000006418 Machinery and equipment nec0.000006756 Paddy0.000006819 Rubber0.000007482 Poultry and their products0.000009136 Top 20 Sectors (PHILIPPINES) CO 2 Emission intensity (kt CO 2 / 1000 Pesos) Other agricultural crops0.00000102 Other vegetables, tubers and root crops0.00000123 Coconut including copra making in the farm0.00000183 Other fruits and nuts0.00000194 Manufacture of wood-working machinery0.00000210 Cattle0.00000247 Mango0.00000259 Pineapple0.00000266 Leafy and stem vegetables0.00000307 Ownership of dwellings0.00000312 Tobacco0.00000320 Other fibercrops0.00000330 Manufacture of records and tapes0.00000342 Carabao0.00000348 Public Education Services0.00000353 Manufacture of parts for radio, TV0.00000363 Forestry0.00000377 Citrus fruits0.00000393 Palay0.00000412 Other livestock including dairy production0.00000416 Source: authors‘ computation based on GTAP-EA and National I-O tables

16. Emission in Expenditure Subgroup IndonesiaPhilippines Source: authors‘ computation based on GTAP-EA, National I-O tables, and National Household Surveys

17. Carbon emission of expenditure subgroup by location Indonesia Philippines Source: authors‘ computation based on GTAP-EA, National I-O tables, and National Household Surveys

18. Carbon emission by region Indonesia Philippines Source: authors‘ computation based on GTAP-EA, National I-O tables, and National Household Surveys

19. Carbon emission by income group Indonesia Philippines Source: authors‘ computation based on GTAP-EA, National I-O tables, and National Household Surveys

20. The determinants of HH Carbon Footprints: PHILIPPINES Variables reg1reg2reg3reg4 coefsecoefsecoefsecoefse log exp1.074***0.0021.794***0.041 log exp_sq -0.032***0.002 log income 0.866***0.0031.692***0.057 log income_sq -0.037***0.002 male0.010**0.0050.0070.005-0.0010.0070.0020.007 age0.002***0.0010.002***0.0010.003***0.0010.004***0.001 age_sq-0.000***0.000-0.000***0.000-0.000***0.000-0.000***0.000 married-0.025***0.005-0.024***0.005-0.0040.0070.0010.007 household size-0.006**0.003-0.017***0.0030.060***0.0040.054***0.004 household size_sq-0.0000.0000.001***0.000-0.004***0.000-0.003***0.000 urban0.051***0.0020.047***0.0020.093***0.0040.067***0.004 elementary level 0.014**0.0060.0080.0060.052***0.0080.025***0.009 elementary grad 0.023***0.0060.016**0.0070.099***0.0090.065***0.010 high school level 0.0090.0070.0100.0070.109***0.0100.100***0.011 high school grad 0.0030.0130.0080.0130.098***0.0190.079***0.019 college level -0.036**0.015-0.026*0.0150.0340.0260.046*0.025 college graduate -0.055***0.007-0.034***0.0080.086***0.0110.120***0.011 Region dummies NO YES NO YES _cons-10.650***0.027-14.684***0.232-8.746***0.039-13.329***0.324 R20.93480.93700.84090.8499 n39,614 note: Robust standard errors are used, *** p<0.01, ** p<0.05, * p<0.1

21. The determinants of HH Carbon Footprints: PHILIPPINES Variables reg5reg6reg7reg8 coefsecoefsecoefsecoefse above pov line0.620***0.010 2nd income quint 0.469***0.0060.418***0.009 3rd income quint 0.796***0.0080.800***0.009 4th income quint 1.109***0.0121.179***0.009 5th income quint 1.605***0.0241.745***0.010 male-0.034***0.011-0.0130.008 -0.0090.008 age0.014***0.0010.008***0.001 0.007***0.001 age_sq-0.000***0.000-0.000***0.000 -0.000***0.000 married0.048***0.0110.041***0.008 0.041***0.008 household size0.265***0.0060.353***0.006 0.371***0.005 household sizesq-0.014***0.000-0.018***0.000 -0.019***0.000 urban0.193***0.0070.083***0.005 0.073***0.005 elementary level 0.109***0.0120.065***0.010 0.057***0.010 elementary grad 0.216***0.0130.116***0.011 0.096***0.011 high school level 0.358***0.0170.172***0.012 0.134***0.014 high school grad 0.246***0.0240.142***0.020 0.121***0.021 college level 0.349***0.0370.128***0.029 0.087***0.029 college graduate 0.618***0.0320.286***0.017 0.222***0.021 Region dummies YES NO YES Fuel typesYES NO YES _cons-0.838***0.041-1.119***0.0340.664***0.006-1.794***0.031 R20.71990.80350.49720.6030 n39,614 39,61539,614 note: Robust standard errors are used, *** p<0.01, ** p<0.05, * p<0.1

22. The determinants of HH Carbon Footprints: INDONESIA reg1reg2reg3reg4reg5 reg6 coefsecoefsecoefsecoefsecoefsecoefse lnexp1.072***0.0002.077***0.018 lnexpsq -0.031***0.001 Below poverty line -0.941***0.002 Income quintiles 2 nd income quintiles 0.479***0.0010.528***0.001 3 rd income quintiles 0.777***0.0010.863***0.001 4 th income quintiles 1.102***0.0011.249***0.001 5 th income quintiles 1.733***0.0011.961***0.001 Members0.002***0.000-0.008***0.0000.118***0.0010.044***0.001 0.022***0.001 Members sq-0.001***0.000-0.000***0.000-0.003***0.000-0.003***0.000 -0.002***0.000 Age0.001***0.0000.001***0.0000.005***0.0000.003***0.000 0.002***0.000 Age sq-0.000***0.000-0.000***0.000-0.000***0.000-0.000***0.000 -0.000***0.000 Female HH -0.005***0.000-0.009***0.001-0.008***0.001 -0.007***0.001 Rural 0.012***0.000-0.431***0.001-0.054***0.001 Share of fuel and light exp3.301***0.0053.336***0.0052.048***0.0142.981***0.008 3.141***0.009 Share of transport exp3.848***0.0053.848***0.0056.668***0.0154.259***0.008 3.876***0.008 Constant-12.073***0.006-20.308***0.1435.007***0.0044.325***0.0034.686***0.001-0.404***0.003 R-sq 0.96080.96150.53690.86440.76770.3845 n233954 note: *** p<0.01, ** p<0.05, * p<0.1 Reg 1- 5, log CO2 is the dependent variable Reg 6, residuals from Reg6 were used as dependent variable

23. Conclusion Income is the main determinant of household carbon footprint and it has a nonlinear effect. Fuel and light, transportation are the two most CO2 intensive emitting household consumption category Varying income groups differ significantly in terms of carbon footprint. Other household characteristics significantly affects carbon footprint. Urbanity, and highly educated household heads emits more CO2. Age and household size has a nonlinear effect on household carbon footprint.

24. Further work: Income elasticities estimation (Engel curves) As income matters as major factor of CF, we will try to figure out which consumption categories are the driving the rising CF (vice versa)? Estimate income elasticities for different consumption items w ij is the share of total expenditures allocated to the j-th consumption category by i-th HH; y i is the HH income (in logs) X i is the vector of HH characteristics

25. Income elasticities, concerns Dealing with endogeneity issue, as independent variable (income) are not independent, we need to find the (valid) instrument. o Possible candidate: Asset index (reflects: the ownership of durable goods, land, dwelling characteristics, etc) 2 SLS regression which use the AI as the instrument for total expenditure.

26. Further works Demand analysis Extending the analysis to two-three further waves (2010 and/or 1997); panel data analysis Household emission inequality? Household emission efficiency (frontier analysis)?

27. Thank you very much for your attention!