Abstract
The emerging notion of emissions inequality expands the idea of sustainability by incorporating economic opportunity as well as social needs and rights into environmental costs and benefits. In China, increasing inequality among urban households in terms of both income and housing wealth establishes a pattern of social stratification. An understanding of the association of social and environmental inequality is thus critical for urban sustainability. Based on the Chinese urban household survey from 2002 to 2009, this article employs the lifestyle approach to calculate and analyse the inequality of households by direct and indirect carbon emissions. The correlations among carbon emission inequality with income and housing wealth inequality are estimated with a Heckman procedure. We find that not only income distribution but also housing wealth distributionis an important consideration in understanding environmental inequality in China.
This is a preview of subscription content, log in via an institution to check access.
Source: Calculated by the authors based on the household survey in urban China
Source: Calculated by the authors based on the regression results in Table 3, a non-linear model (3) of direct carbon emissions
Source: Calculated by the authors based on the regression results in Table 5, a non-linear model (3) of indirect carbon emissions
Similar content being viewed by others
Notes
EJ = 1018 Joules (J).
The Gini Index (Gastwirth 1972) is used in this test with the formula:
\({\text{GI}} = \left[ {\left( {2/N\mathop \sum \limits_{i = 1}^{N} y_{i} } \right)\mathop \sum \limits_{i = 1}^{N} i \cdot y_{i} } \right] - 1 - 1/N\)
where N represents the number of households in the sample, and \(y_{i}\) is the per household head income or housing wealth of each year, ordered by the per household head’s income or per household head’s housing wealth, respectively.
Head of the household refers to the individual in one family who is the representative of the family. In traditional China, normally the husband was the head of the household. Nowadays, the person who has the relatively higher income is likely to be the head of the family.
These include food, clothes, residence, housing facilities and services, medical care, transport and communication, education, cultural events and recreation, and miscellaneous goods and services.
Lambda can be expressed as
$$\lambda_{i} = \frac{{\phi \left( { - X_{2i} /\sigma_{2} } \right)}}{{\phi \left( {X_{2i} /\sigma_{2} } \right)}}$$where ϕ demonstrates the normal density function.
The consumer price index (CPI) was used as the deflator of the housing wealth and the disposable income, with the CPI of 2000 as the constant price.
The average disposable income of the top 10 % of urban Chinese households is 135,796 RMB in 2009.
References
Akbostancı, E., Türüt-Aşık, S., & Tunç, Gİ. (2009). The relationship between income and environment in Turkey: Is there an environmental Kuznets curve? Energy Policy, 37(3), 861–867.
Alfredsson, E. (2004). “Green” consumption—no solution for climate change. Energy, 29(4), 513–524.
Andrews, C. (2008). Greenhouse gas emissions along the rural-urban gradient. Journal of Environmental Planning and Management, 51(6), 847–870.
Baiocchi, G., Minx, J., & Hubacek, K. (2010). The impact of social factors and consumer behavior on carbon dioxide emissions in the United Kingdom. Journal of Industrial Ecology, 14(1), 50–72.
Baranzini, A., Goldemberg, J., & Speck, S. (2000). A future for carbon taxes. Ecological Economics, 32(3), 395–412.
Bin, S., & Dowlatabadi, H. (2005). Consumer lifestyle approach to US energy use and the related CO2 emissions. Energy Policy, 33(2), 197–208.
Boone, C. G., & Fragkias, M. (Eds.). (2013). Urbanization and sustainability: Linking urban ecology, environmental justice and global environmental change. Berlin: Springer Science & Business Media.
Börjesson, P., & Gustavsson, L. (2000). Greenhouse gas balances in building construction: wood versus concrete from life-cycle and forest land-use perspectives. Energy Policy, 28(9), 575–588.
Boyce, J. K. (1994). Inequality as a cause of environmental degradation. Ecological Economics, 11(3), 169–178.
Brännlund, R., & Ghalwash, T. (2008). The income–pollution relationship and the role of income distribution: An analysis of Swedish household data. Resource and Energy Economics, 30(3), 369–387.
Braubach, M., & Fairburn, J. (2010). Social inequities in environmental risks associated with housing and residential location—A review of evidence. European Journal of Public Health, 20(1), 36–42.
Brenner, M., Riddle, M., & Boyce, J. K. (2007). A Chinese sky trust?: Distributional impacts of carbon charges and revenue recycling in China. Energy Policy, 35(3), 1771–1784.
Bureau, C. S. (Various years). China statistical yearbook. Beijing: Statistical Press.
Chang, H., Lafrenz, M., Jung, I., Figliozzi, M., Platman, D., & Pederson, C. (2010). Potential impacts of climate change on flood-induced travel disruptions: A case study of Portland, Oregon, USA. Annals of the Association of American Geographers, 100(4), 938–952.
Clarke-Sather, A., Qu, J., Wang, Q., Zeng, J., & Li, Y. (2011). Carbon inequality at the sub-national scale: A case study of provincial-level inequality in CO2 emissions in China 1997–2007. Energy Policy, 39(9), 5420–5428.
Cole, M., Rayner, A., & Bates, J. (1997). The environmental Kuznets curve: An empirical analysis. Environment and Development Economics, 2(4), 401–416.
Copeland, B. R., & Taylor, M. S. (2004). Trade, tragedy, and the commons (No. w10836). Cambridge: National Bureau of Economic Research.
Cutter, S. L. (1995). Race, class and environmental justice. Progress in Human Geography, 19(1), 111–122.
Dissou, Y., & Siddiqui, M. S. (2014). Can carbon taxes be progressive? Energy Economics, 42, 88–100.
D’souza, R. M. (1997). Housing and environmental factors and their effects on the health of children in the slums of Karachi, Pakistan. Journal of Biosocial Science, 29(03), 271–281.
Du, Y. W., Huang, T. Z., & Kang, G. D. (2015). A study of urban household carbon emission characteristic and it’s impact factors from the micro perspective: From the survey data of the urban household activities in Jiangsu province. Population & Economics, 2, 30–39.
Duro, J., & Padilla, E. (2006). International inequalities in per capita CO2 emissions: A decomposition methodology by Kaya factors. Energy Economics, 28(2), 170–187.
European Environment Agency (EEA). (1996).
Feng, Z. H., Zou, L. L., & Wei, Y. M. (2011). The impact of household consumption on energy use and CO2 emissions in China. Energy, 36(1), 656–670.
Gastwirth, J. L. (1972). The estimation of the Lorenz curve and Gini index. The Review of Economics and Statistics, 54(3), 306–316.
Genjo, K., Tanabe, S., Matsumoto, S., Hasegawa, K., & Yoshino, H. (2005). Relationship between possession of electric appliances and electricity for lighting and others in Japanese households. Energy and Buildings, 37(3), 259–272.
Hammond, G., & Jones, C. (2008). Embodied energy and carbon in construction materials. Proceedings of the ICE—Energy, 161(2), 87–98.
Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica: Journal of the Econometric Society, 47, 153–161.
Heerink, N., Mulatu, A., & Bulte, E. (2001). Income inequality and the environment: aggregation bias in environmental Kuznets curves. Ecological Economics, 38(3), 359–367.
Heil, M. T., & Selden, T. M. (2001). International trade intensity and carbon emissions: A cross-country econometric analysis. The Journal of Environment & Development, 10(1), 35–49.
Herendeen, R. (1978). Total energy cost of household consumption in Norway, 1973. Energy, 3(5), 615–630.
Hill, R. J., & Magnani, E. (2002). An exploration of the conceptual and empirical basis of the environmental Kuznets curve. Australian Economic Papers, 41(2), 239–254.
Holtz-Eakin, D., & Selden, T. (1995). Stoking the fires? CO2 emissions and economic growth. Journal of Public Economics, 57(1), 85–101.
IEA. (2000). World energy outlook. Paris: IEA.
Intergovernmental Panel on Climate Change (IPCC). 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2006.
Jiang, Z., & Shao, S. (2014). Distributional effects of a carbon tax on Chinese households: A case of Shanghai. Energy Policy, 73, 269–277.
Kaza, N. (2010). Understanding the spectrum of residential energy consumption: A quantile regression approach. Energy Policy, 38(11), 6574–6585.
Kohlhuber, M., Mielck, A., Weiland, S. K., & Bolte, G. (2006). Social inequality in perceived environmental exposures in relation to housing conditions in Germany. Environmental Research, 101(2), 246–255.
Li, Z., Li, P., Guo, J., & Zeng, X. F. (2013). Impact factors estimation and research on the differences across cities of residential CO2 emissions in Chinese major cities. China Population, Resource and Environment, 23(10), 87–94.
Lindén, A., Carlsson-Kanyama, A., & Eriksson, B. (2006). Efficient and inefficient aspects of residential energy behaviour: What are the policy instruments for change? Energy Policy, 34(14), 1918–1927.
Lu, C., Tong, Q., & Liu, X. (2010). The impacts of carbon tax and complementary policies on Chinese economy. Energy Policy, 38(11), 7278–7285.
Ludwig, A., & Sløk, T. (2002). the impact of changes in stock prices and house priceson consumption in OECD countries. IMF Working Papers, 02(1), 1.
Ma, C. (2010). Who bears the environmental burden in China—An analysis of the distribution of industrial pollution sources? Ecological Economics, 69(9), 1869–1876.
Meng, X., Gregory, R., & Wang, Y. (2005). Poverty, inequality, and growth in urban China, 1986–2000. Journal of Comparative Economics, 33(4), 710–729.
Milanovic, B. (2005). Half a world: Regional inequality in five great federations. Journal of the Asia Pacific Economy, 10(4), 408–445.
Muellbauer, J., & Murphy, A. (2008). Housing markets and the economy: The assessment. Oxford Review of Economic Policy, 24(1), 1–33.
National Bureau of Statistics. (2003). China Statistical Yearbook. Beijing: China Statistical Press.
National Bureau of Statistics. (2010). China Statistical Yearbook. Beijing: China Statistical Press.
Organization for Economic Co-operation and Development (OECD). (1996).
Padilla, E., & Duro, J. (2013). Explanatory factors of CO2 per capita emission inequality in the European Union. Energy Policy, 62, 1320–1328.
Ravallion, M., Heil, M., & Jalan, J. (2000). Carbon emissions and income inequality. Oxford Economic Papers, 52(4), 651–669.
Reid, L. A., & Houston, D. (2013). Low carbon housing: A ‘Green’ wolf in sheep’s clothing? Housing Studies, 28(1), 1–9.
Reinders, A. H. M. E., Vringer, K., & Blok, K. (2003). The direct and indirect energy requirement of households in the European Union. Energy Policy, 31(2), 139–153.
Reusswig, F., Lotze-Campen, H., & Gerlinger, K. (2003). Changing global lifestyle and consumption patterns: The case of energy and food. In PERN workshop on population, consumption and the environment, vol. 19.
Shammin, M. R., & Bullard, C. W. (2009). Impact of cap-and-trade policies for reducing greenhouse gas emissions on US households. Ecological Economics, 68(8), 2432–2438.
Son, H. H. (2010). Human capital development. Asian Development Bank Economics. Working Paper Series, (225).
Soytas, U., Sari, R., & Ewing, B. T. (2007). Energy consumption, income, and carbon emissions in the United States. Ecological Economics, 62(3), 482–489.
Torras, M., & Boyce, J. (1998). Income, inequality, and pollution: A reassessment of the environmental Kuznets Curve. Ecological Economics, 25(2), 147–160.
Wang, Q. C. (2015). Effects of household size on energy consumption and carbon emissions. Resources Science, 37(2), 0299–0307.
Wang, Z., & Yang, L. (2014). Indirect carbon emissions in household consumption: Evidence from the urban and rural area in China. Journal of Cleaner Production, 78, 94–103.
Wei, Y. M., Liu, L. C., Fan, Y., & Wu, G. (2007). The impact of lifestyle on energy use and CO2 emission: An empirical analysis of China’s residents. Energy Policy, 35(1), 247–257.
Wei, Y., Liu, L., Wu, G., & Zou, L. (2011). Energy economics: CO 2 emissions in China. Berlin: Springer Science & Business Media.
World Bank. (2011). World development report 2011: Conflict, security, and development. Washington, DC: World Bank.
Yang, Z., & Chen, J. (2014). Housing affordability and housing policy in urban China. Berlin: Springer.
Yang, X. M., Ge, Y. S., & Zeng, H. Y. (2010). The household carbon emission analysis under individual consumer behaviour. China Population, Resource and Environment, 20(5), 35–40.
Yang, Z., & Wang, S. (2011). The impact of privatization of public housing on housing affordability in Beijing: An assessment using household survey data. Local Economy, 26(5), 384–400.
Young, W., Hwang, K., McDonald, S., & Oates, C. (2010). Sustainable consumption: Green consumer behaviour when purchasing products. Sustainable Development, 18(1), 20–31.
Zhang, Z., & Baranzini, A. (2004). What do we know about carbon taxes? An inquiry into their impacts on competitiveness and distribution of income. Energy Policy, 32(4), 507–518.
Zhang, X., Niu, S. W., Zhao, C. S., & Hu, L. L. (2011). The study on household Energy consumption and carbon emissions in China’s urbanization. China Soft Science, 9, 65–75.
Zhu, Z., Shen, Y., & Huang, M. (2011). Empirical study on low-carbon consumption and factors of carbon emission—Based on Hangzhou. Survey Resource Development & Market, 27(9), 831–834.
Zhu, C., & Wan, G. (2012). Rising inequality in china and the move to a balanced economy. China & World Economy, 20(1), 83–104.
Author information
Authors and Affiliations
Corresponding author
Appendices
Appendix 1: Coefficients of direct and indirect carbon emissions
Direct carbon emissions of households are calculated by the formula,
where \({\text{CE}}_{D}\) is direct carbon emissions, \(C_{{{\text{Coef}}_{D} }}\) is the carbon coefficient of each type of fuel, and \({\text{fuel}}_{D}\) refers to households’ indoor and outdoor direct energy consumption. The indoor direct energy includes electricity, coal, LPG and natural gas, and the outdoor direct energy is the vehicle fuel of gasoline. The data source and calculation formula of household direct carbon emissions is shown in Table 6.
The carbon coefficient of each type of fuel is calculated based on the IPCC (2006). The IPCC’s calculation of the coefficients follows a rigorous method with a comprehensive and transparent calculation process. To support the methodology used in obtaining carbon coefficients, the IPCC published ‘Good Practice Guidance and Uncertainty Management’, references for national calculations to prevent uncertainty and over- or under-estimations. The IPCC also considered regional variations by introducing regional differences in energy production into the calculation process. These coefficients are widely adopted in carbon emission research (Clarke-Sather et al. 2011; Wei et al. 2011) because of the high reliability of the data sources and the high applicability of the coefficients, and the IPCC (2006) is the most widely used by researchers in China.
Indirect carbon emissions of households are estimated based on the method from Wei et al. (2007). Indirect carbon emissions are calculated by the formula
where \({\text{CE}}_{I}\) refers to indirect carbon emissions, \(X_{I}\) refers to household consumption from the survey, and \(C_{{{\text{Coef}}_{I} }}\) is carbon intensity to proxy for the carbon coefficients of the eight major types of consumption. The eight-category classification of household consumption is the method used in the China Statistics Bureau, and this classification has been widely used in Chinese households’ carbon emissions (Wei et al. 2007; Zhang et al. 2011; Wang and Yang 2014). The data source and calculation formula of household indirect carbon emissions is shown in Table 7.
The carbon intensity is taken from the calculation of Wang and Yang (2014), estimated according to the method suggested by IPCC (2006) and Chinese households’ expenditure pattern. The specific method is to divide the sum of carbon emissions for the specific sectors by the sum of the value added of the sectors. Because improvements in technology lead to improvements in energy efficiency, the carbon intensity of each sector generally drops year by year. Residence, being the sector with the highest carbon intensity, reduces the most because of the increasing proportion of recycled materials adopted in industry. Clothing and miscellaneous goods and services only reduce a little because of the unchangeable production process with almost fixed carbon emissions.
Appendix 2: Robust tests: income, housing wealth and carbon emissions across three regions
As a robustness test, we estimated the correlation among income, housing wealth and carbon emissions across three regions: the eastern, central and western regions. Regional development in China presents significant differences in economy, income and housing wealth. The household survey found that the average housing wealth in the eastern area in 2002 was around 100,000 RMB, higher than that in the central and western areas, which averaged 50,000 RMB (China Statistical Yearbook 2003). The gap widened in 2009, with the housing wealth of each household in the eastern area being around 340,000 RMB and that of the western and central areas around 140,000 RMB (China Statistical Yearbook 2010). Annual disposable income of each household in the eastern area, which increased from 30,000 RMB to 60,000 RMB from 2002 to 2009, is also higher than that in the western and central areas, which increased from 19,000 RMB to 40,000 RMB (China Statistical Yearbook 2003–2010).
Housing wealth and income had significant effects in the regression across the three regions (Table 8). Consistent with what we found on the marginal effect of income and housing wealth on emissions, on average, we found the lowest marginal effect of income on direct carbon, the highest marginal effect of housing wealth on direct carbon and the highest marginal effect of both income and housing wealth on indirect carbon in the eastern region. The effects of inequality in housing and income on inequality of carbon emissions are highlighted.
Rights and permissions
About this article
Cite this article
Yang, Z., Wu, S. & Cheung, H.Y. From income and housing wealth inequalities to emissions inequality: carbon emissions of households in China. J Hous and the Built Environ 32, 231–252 (2017). https://doi.org/10.1007/s10901-016-9510-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10901-016-9510-9