Abstract
Although the literature on emission inequality is abundant, this study differentiates itself by focusing on emission inequality at the household-level. We further separate measures on emission inequality based on household characteristics as well as decompose it into sources of emission. The results show that as per capita expenditure increases, within group emission inequality tends to decline until the middle-income group but then further increase in expenditure worsens emission inequality. We also find that expenditure inequality is the predominant driver of emission inequality although recent increases in expenditure inequality have not lead to a commensurate increase in emission inequality. The decomposition of inequality based on emission sources suggests that energy-transportation predominantly contributes of the overall emission inequality; reducing the emission intensity of these sources would serve to lower emission inequality substantially.
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Notes
Overall, it is hypothesized that if emission is more unequal than income, one could suggest that (richer) households should have more carbon intensive lifestyle. It is also hypothesized that if households are ordered based on income and under this circumstance emission inequality is dominated by between-group component; then the income is considered as important driver of emission inequality. This is also comparable with the case households are ordered based on non-income characteristics. For instance, in the case that most inequality is between group component (if households are ranked based on their income) and an opposite findings if they are ranked based on non-income characteristics; one could suggest that income has a strong influence on emission inequality. Finally, the decomposition of emission inequality by income source hypothesizes that apart from individual emission source inequality, overall emission inequality should be largely attributed to any emission (income) source that highly dominates to overall emission, and/or which highly correlated to overall emission inequality.
Per capita emission is about 1239 kg (without deflated expenditure). In our survey, expenditure mean is scaled up to national account expenditure means.
It is also partly related to the slightly lower share of urban populations in 2009 which is related to slightly difference classification systems used in the two surveys.
The middle-income groups are more homogenous in term of the emission distribution. A factor affecting this lower inequality within the middle-income quintiles is due to the boundaries of those quintiles.
There are other ways how one could decompose emission inequality, including using the drivers of emissions discussed in the previous section. One approach could be a regression-based decomposition proposed by Fields (2003). We do not do this here as we are particularly interested in the decomposition by source; but such a regression-based decomposition would be very valuable in future work.
“Food” refers to emissions from cereals, vegetables and fruits, oil and fats, eggs fish, meat and dairy, and tobacco; “Energy and transportation” captures the emissions from fuel-light and transportation; “Housing operations and durables” represents emissions from house operation and durables, toiletry, and telecommunication; “Services” represents emissions from health, education, services sectors and rent, tax and redistribution, and recreation and ceremony.
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Acknowledgments
The authors thank to EXPERTS Erasmus Mundus (the European Union) and Göttinger Graduiertenschule Gesellschaftswissenschaften (GGG) for research funding. They also thank two anonymous referees and participants at workshops and conferences in HDCA Conference 2012 (Jakarta); Green Growth Young Researcher Conference 2012 (Kiel); Low Carbon Development and Poverty Reduction Conference 2013 (New Delhi), Poverty Reduction, Equity and Growth Network Conference 2013 (Copenhagen); among others, for helpful comments and discussion.
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Irfany, M.I., Klasen, S. Inequality in emissions: evidence from Indonesian household. Environ Econ Policy Stud 18, 459–483 (2016). https://doi.org/10.1007/s10018-015-0119-0
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DOI: https://doi.org/10.1007/s10018-015-0119-0