Abstract
We examine the time-varying relationship between income inequality and carbon dioxide emissions at the sub-national regional and sectoral levels in Australia from 1990 to 2017. We find the relationship between income inequality and carbon dioxide emissions to be non-linear and either negative or statistically insignificant. We also find significant cross-industry heterogeneity in the income inequality-carbon dioxide emissions nexus, exposing how sector-specific events, such as mining business cycles, are exemplified through the relationship.
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Notes
CO2−e reports the aggregate amount of different greenhouse gas emissions as one numeric value. Different greenhouse gases are converted to an equivalent amount of CO2 based on their global warming potential—i.e., their relative molecular capacity to warm the atmosphere.
Stationary energy is defined as “greenhouse gas emissions from the production of electricity and other direct combustion of fossil fuels” (DISER, 2020d, p. 13).
Fugitive emissions are defined as “greenhouse gas emissions from the extraction and distribution of coal, oil and natural gas” (DISER, 2020d, p. 13).
Gini coefficient data is only available up to 2013. Thus, we used linear extrapolation for the period 2014 to 2017.
\(URBAN\) is only available up to 2016. Thus, we linearly extrapolated the variable for 2017.
\({CO}_{2}{PC}_{TAS, 2016}\) is negative. As we are modelling emissions, we treat this observation as a value of 0. Therefore, when applying the natural logarithm filter to \({CO}_{2}PC\), we drop the TAS 2016 observation.
The check is also performed with respect to sector \(C{O}_{2}PC\) and the results are consistent with those in Fig. 5. To preserve space, we do not report these results. Nonetheless, they are available upon request.
References
Australian Bureau of Statistics. (2019a). Australian historical population statistics, 2019a (Catalogue no. 3105.0.65.001). https://www.abs.gov.au/.
Australian Bureau of Statistics. (2019b). Australian national accounts: State accounts (Catalogue no. 5220.0). https://www.abs.gov.au/.
Australian Bureau of Statistics. (2020a). Australian national accounts: National income, expenditure and product (Catalogue no. 5206.0). https://www.abs.gov.au/.
Australian Bureau of Statistics. (2020b). Employment in renewable energy activities, Australia (Catalogue no. 4631.0). https://www.abs.gov.au/.
Australian Bureau of Statistics. (2020c). International trade in goods and services, Australia (Catalogue no. 5368.0). https://www.abs.gov.au/.
Australian Bureau of Statistics. (2020d). Labour force, Australia (Catalogue no. 6202.0). https://www.abs.gov.au/.
Australian Bureau of Statistics. (2020e). Labour force, Australia, detailed (Catalogue no. 6291.0.55.003). https://www.abs.gov.au/.
Australian Bureau of Statistics. (2020f). National, state and territory population (Catalogue no. 3101.0). https://www.abs.gov.au/.
Australian Bureau of Statistics. (2020g). Private new capital expenditure and expected expenditure, Australia (Catalogue no. 5625.0). https://www.abs.gov.au/.
Australian Bureau of Statistics & Statistics New Zealand. (2006). Australian and New Zealand standard industrial classification (ANZSIC). Author.
Australian Council of Social Service & University of New South Wales. (2018). Inequality in Australia 2018. Author.
Borghesi, S. (2006). Income inequality and the environmental Kuznets curve. In M. Basili, M. Franzini, & A. Vercelli (Eds.), Environment, inequality and collective action (pp. 33–52). Routledge.
Bowles, S., & Park, Y. (2005). Emulation, inequality, and work hours: Was Thorsten Veblen right? The Economic Journal, 115, F397–F412. https://doi.org/10.1111/j.1468-0297.2005.01042.x
Boyce, J. K. (1994). Inequality as a cause of environmental degradation. Ecological Economics, 11, 169–178. https://doi.org/10.1016/0921-8009(94)90198-8
Boyce, J. K., Klemer, A. R., Templet, P. H., & Willis, C. E. (1999). Power distribution, the environment, and public health: A state-level analysis. Ecological Economics, 29, 127–140. https://doi.org/10.1016/S0921-8009(98)00056-1
Chen, J., Gao, J., & Li, D. (2012). Semiparametric trending panel data models with cross-sectional dependence. Journal of Econometrics, 171, 71–85. https://doi.org/10.1016/j.jeconom.2012.07.001
Chudik, A., & Pesaran, M. H. (2015). Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors. Journal of Econometrics, 188, 393–420. https://doi.org/10.1016/j.jeconom.2015.03.007
Coondoo, D., & Dinda, S. (2008). Carbon dioxide emission and income: A temporal analysis of cross-country distributional patterns. Ecological Economics, 65, 375–385. https://doi.org/10.1016/j.ecolecon.2007.07.001
Connolly, E., & Orsmond, D. (2011). The mining industry: From bust to boom (Research Discussion Paper 2011-08). Reserve Bank of Australia, Economic Analysis Department.
Dawkins, P. (2000). The Australian labour market in the 1990s. In D. Gruen & S. Shrestha (Eds.), The Australian economy in the 1990s (pp. 316–352). Reserve Bank of Australia.
Deltas, G. (2003). The small-sample bias of the Gini coefficient: Results and implications for empirical research. Review of Economics and Statistics, 85(1), 226–234.
Department of Industry, Science, Energy and Resources. (2020a). Australian energy statistics, table O, Australian electricity generation by fuel type 2018–19 and 2019. https://www.energy.gov.au/.
Department of Industry, Science, Energy and Resources. (2020b). Australian greenhouse emissions information system [Database]. https://ageis.climatechange.gov.au/UNFCCC.aspx.
Department of Industry, Science, Energy and Resources. (2020c). National inventory by economic sector 2018. Australian Government.
Department of Industry, Science, Energy and Resources. (2020d). National inventory report 2018 volume 1. Australian Government.
Department of Industry, Science, Energy and Resources. (2020e). State and territory greenhouse gas inventories 2018. Australian Government.
Drabo, A. (2011). Impact of income inequality on health: Does environment quality matter? Environment and Planning A, 43, 146–165. https://doi.org/10.1068/a43307
Fitzgerald, J. B., Jorgenson, A. K., & Clark, B. (2015). Energy consumption and working hours: A longitudinal study of developed and developing nations, 1990–2008. Environmental Sociology, 1(3), 213–223. https://doi.org/10.1080/23251042.2015.1046584
Fletcher, M., & Guttmann, B. (2013). Income inequality in Australia. Economic roundup, issue 2, 2013 (pp. 35–54). Treasury, Australian Government.
Golley, J., & Meng, X. (2012). Income inequality and carbon dioxide emissions: The case of Chinese urban households. Energy Economics, 34, 1864–1872. https://doi.org/10.1016/j.eneco.2012.07.025
Gore, T. (2020). Confronting carbon inequality: Putting climate justice at the heart of the COVID-19 recovery. Oxfam.
Greenville, J., Pobke, C., & Rogers, N. (2013). Trends in the distribution of income in Australia (Staff Working Paper). Productivity Commission.
Grossman, G. M., & Krueger, A. B. (1995). Economic growth and the environment. Quarterly Journal of Economics, 110(2), 353–377. https://doi.org/10.2307/2118443
Grunewald, N., Klasen, S., Martínez-Zarzoso, I., & Muris, C. (2017). The trade-off between income inequality and carbon dioxide emissions. Energy Economics, 142, 249–256. https://doi.org/10.1016/j.ecolecon.2017.06.034
Hailemariam, A., Dzhumashev, R., & Shahbaz, M. (2019a). Carbon emissions, income inequality and economic development. Empirical Economics, 59, 1139–1159. https://doi.org/10.1007/s00181-019-01664-x
Hailemariam, A., Smyth, R., & Zhang, X. (2019b). Oil prices and economic policy uncertainty: Evidence from a nonparametric panel data model. Energy Economics, 83, 40–51. https://doi.org/10.1016/j.eneco.2019.06.010
Hao, Y., Chen, H., & Zhang, Q. (2016). Will income inequality affect environmental quality? Analysis based on China’s provincial panel data. Ecological Indicators, 67, 533–542. https://doi.org/10.1016/j.ecolind.2016.03.025
Heerink, N., Mulatu, A., & Bulte, E. (2001). Income inequality and the environment: Aggregation bias in environmental Kuznets curves. Ecological Economics, 38, 359–367. https://doi.org/10.1016/S0921-8009(01)00171-9
Henderson, D. J., Kumbhakar, S. C., & Parmeter, C. F. (2012). A simple method to visualise results in non-linear regression models. Economics Letters, 117, 578–581. https://doi.org/10.1016/j.econlet.2012.07.040
Hogan, L., Harman, J., Maritz, A., Thorpe, S., Simms, A., Berry, P., & Copeland, A. (2002). Mineral exploration in Australia: Trends, economic impacts and policy issues. Australian Commodities, 9(4), 601–607.
Huang, Z., & Duan, H. (2020). Estimating the threshold interactions between income inequality and carbon emissions. Journal of Environmental Management, 263, 110393. https://doi.org/10.1016/j.jenvman.2020.110393
Ivanovski, K., Awaworyi Churchil, S., & Inekwe, J. (2020). Convergence in income inequality across Australian states and territories. Social Indicators Research, 148, 127–142. https://doi.org/10.1007/s11205-019-02201-0
Ivanovski, K., Hailemariam, A., & Smyth, R. (2021). The effect of renewable and non-renewable energy consumption on economic growth: Non-parametric evidence. Journal of Cleaner Production, 286, 124956.
Jorgenson, A., Schor, J. B., & Huang, X. (2017). Income inequality and carbon emissions in the United States: A state-level analysis, 1997–2012. Ecological Economics, 134, 40–48. https://doi.org/10.1016/j.ecolecon.2016.12.016
Kearns, J., & Lowe, P. (2011). Australia's prosperous 2000s: Housing and the mining boom (Research Discussion Paper 2011-07). Reserve Bank of Australia, Economic Group.
Kendall, M. (1948). Rank correlation methods. Griffin.
Kennedy, T., Smyth, R., Valadkhani, A., & Chen, G. (2017). Does income inequality hinder economic growth? New evidence using Australian taxation statistics. Economic Modelling, 65, 119–128. https://doi.org/10.1016/j.econmod.2017.05.012
Knight, K. W., Rosa, E. A., & Schor, J. B. (2013). Could working less reduce pressures on the environment? A cross-national panel analysis of OECD countries, 1970–2007. Global Environmental Change, 23, 691–700. https://doi.org/10.1016/j.gloenvcha.2013.02.017
Li, D., Chen, J., & Gao, J. (2011). Non-parametric time-varying coefficient panel data models with fixed effects. The Econometrics Journal, 14, 387–408. https://doi.org/10.1111/j.1368-423X.2011.00350.x
Liu, C., Jiang, Y., & Xie, R. (2019a). Does income inequality facilitate carbon emission reduction in the U.S.? Journal of Cleaner Production, 217, 380–387. https://doi.org/10.1016/j.jclepro.2019.01.242
Liu, Q., Wang, S., Zhang, W., Li, J., & Kong, Y. (2019b). Examining the effects of income inequality on CO2 emissions: Evidence from non-spatial and spatial perspectives. Applied Energy, 236, 163–171. https://doi.org/10.1016/j.apenergy.2018.11.082
Lowy Institute. (2020). Lowy Institute poll 2020. https://poll.lowyinstitute.org/files/lowyinsitutepoll-2020.pdf.
Magnani, E. (2000). The environmental Kuznets curve, environmental protection policy and income distribution. Ecological Economics, 32, 431–443. https://doi.org/10.1016/S0921-8009(99)00115-9
Mammen, E. (1993). Bootstrap and wild bootstrap for high dimensional linear models. The Annals of Statistics, 21(1), 255–285.
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245–259. https://doi.org/10.2307/1907187
Marsiliani, L., & Renström, T. I. (2000). Inequality, Environmental Protection and Growth (Centre for Economic Research Discussion Paper; Vol. 2000-34). Microeconomics.
Mittmann, Z., & Mattos, E. J. (2020). Income inequality and carbon dioxide emissions: Evidence from Latin America. Journal of International Development, 32, 389–407. https://doi.org/10.1002/jid.3459
Norris, K., & McLean, B. (1999). Changes in earnings inequality, 1975 to 1998. Australian Bulletin of Labour, 25(1), 23–31.
Organisation for Economic Co-operation and Development. (2020a). Air and GHG emissions [Data file]. https://data.oecd.org/air/air-and-ghg-emissions.htm.
Organisation for Economic Co-operation and Development. (2020b). Income inequality [Data file]. https://data.oecd.org/inequality/income-inequality.htm.
Padilla, E., & Serrano, A. (2006). Inequality in CO2 emissions across countries and its relationship with income inequality: A distributive approach. Energy Policy, 34, 1762–1772. https://doi.org/10.1016/j.enpol.2004.12.014
Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels (Working Paper No. 1229). University of Cambridge, Centre for Economic Studies and Ifo Institute for Economic Research.
Pesaran, M. H. (2006). Estimation and inference in large heterogenous panels with a multifactor error structure. Econometrica, 74(4), 967–1012. https://doi.org/10.1111/j.1468-0262.2006.00692.x
Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22, 265–312. https://doi.org/10.1002/jae.951
Pesaran, M. H., & Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68, 79–113. https://doi.org/10.1016/0304-4076(94)01644-F
Phillips, P. C. B. (2001). Trending time series and macroeconomic activity: Some present and future challenges. Journal of Econometrics, 100, 21–27. https://doi.org/10.1016/S0304-4076(00)00048-8
Productivity Commission. (2018). Rising inequality? A stocktake of the evidence (Research Paper). Australian Government.
Ravallion, M., Heil, M., & Jalan, J. (2000). Carbon emissions and income inequality. Oxford Economic Papers, 52, 651–669. https://doi.org/10.1093/oep/52.4.651
Renewable Energy (Electricity) Act 2000 (Cth.).
Scruggs, L. A. (1998). Political and economic inequality and the environment. Ecological Economics, 26, 259–275. https://doi.org/10.1016/S0921-8009(97)00118-3
Silvapulle, P., Smyth, R., Zhang, X., & Fenech, J. P. (2017). Nonparametric panel data model for crude oil and stock market prices in net oil importing countries. Energy Economics, 67, 255–267. https://doi.org/10.1016/j.eneco.2017.08.017
St John, A. (2014). The renewable energy target: A quick guide (Research Paper Series 2013–14). Department of Parliamentary Services, Australian Government.
Su, L., & Ullah, A. (2008). Local polynomial estimation of nonparametric simultaneous equations models. Journal of Econometrics, 144, 193–218. https://doi.org/10.1016/j.jeconom.2008.01.002
Sun, Y., Carroll, R. J., & Li, D. (2009). Semiparametric estimation of fixed-effects panel data varying coefficient models. Advances in Econometrics, 25, 101–129. https://doi.org/10.1108/S0731-9053(2009)0000025006
Uddin, M. M., Mishra, V., & Smyth, R. (2020). Income inequality and CO2 emissions in the G7, 1870–2014: Evidence from non-parametric modelling. Energy Economics, 88, 104780. https://doi.org/10.1016/j.eneco.2020.104780
Uzar, U., & Eyuboglu, K. (2019). The nexus between income inequality and CO2 emissions in Turkey. Journal of Cleaner Production, 227, 149–157. https://doi.org/10.1016/j.jclepro.2019.04.169
Veblen, T. (1899). The theory of the leisure class: An economic study of institutions. Macmillan.
Wolde-Rufael, Y., & Idowu, S. (2017). Income distribution and CO2 emission: A comparative analysis for China and India. Renewable and Sustainable Energy Reviews, 74, 1336–1345. https://doi.org/10.1016/j.rser.2016.11.149
World Bank. (1992). World development report 1992: Development and the environment. Oxford University Press.
World Coal Association. (2013). Coal facts 2013. Author.
Wu, C. F. J. (1986). Jackknife, bootstrap and other resampling methods in regression analysis. The Annals of Statistics, 14(4), 1261–1295.
Zhang, Y., Su, L., & Phillips, P. C. B. (2012). Testing for common trends in semi-parametric panel data models with fixed effects. The Econometrics Journal, 15, 56–100. https://doi.org/10.1111/j.1368-423X.2011.00361.x
Zhang, C., & Zhao, W. (2014). Panel estimation for income inequality and CO2 emission: A regional analysis in China. Applied Energy, 136, 382–392. https://doi.org/10.1016/j.apenergy.2014.09.048
Zhu, H., Xia, H., Guo, Y., & Peng, C. (2018). The heterogeneous effects of urbanisation and income inequality on CO2 emissions in BRICS economies: Evidence from panel quantile regression. Environmental Science and Pollution Research, 25, 17176–17193. https://doi.org/10.1007/s11356-018-1900-y
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Marinucci, N., Ivanovski, K. Does Inequality Affect Climate Change? A Regional and Sectoral Analysis. Soc Indic Res 166, 705–729 (2023). https://doi.org/10.1007/s11205-023-03085-x
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DOI: https://doi.org/10.1007/s11205-023-03085-x