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The Causal Factors of International Inequality in \(\hbox {CO}_{2}\) Emissions Per Capita: A Regression-Based Inequality Decomposition Analysis


This paper uses the possibilities provided by the regression-based inequality decomposition (Fields in Res Labor Econ 22:1–38, 2003) to explore the contribution of different explanatory factors to international inequality in \(\hbox {CO}_{2}\) emissions per capita. In contrast to previous emissions inequality decompositions, which were based on identity relationships, this methodology does not impose any a priori specific relationship. Thus, it allows an assessment of the contribution to inequality of different relevant variables. In short, the paper appraises the relative contributions of affluence, sectoral composition, demographic factors and climate. The analysis is applied to selected years of the period 1993–2007. The results show the important (though decreasing) share of the contribution of demographic factors, as well as a significant contribution of affluence and sectoral composition.

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  1. York et al. (2003) turned that accounting equation into a stochastic regression model, allowing them to make a test hypothesis and also to introduce further determinants of the environmental impact.

  2. These analytical decomposition methods have been applied to ecological footprint in White (2007), Teixidó-Figueras and Duro (2015) and Duro and Teixidó-Figueras (2013). For the case of \(\hbox {CO}_{2}\) emissions, Duro and Padilla (2006) made a multiplicative decomposition of the contribution of Kaya (1989) factors.

  3. Most RBID applications analyse income inequality from a micro-approach, so there is an income-generating function, and income inequality is decomposed in terms of the typical explanatory variables of those models: race, education level, gender, age, etc. (e.g. Cowell and Fiorio 2011; Fields 2003; Gunatilaka and Chotikapanich 2009; Morduch and Sicular 2002; Wan 2004).

  4. There are several empirical applications to income inequality comparing results obtained according to the different methods of RBID. Very often they conclude that there are no significant differences (Cowell and Fiorio 2011; Fields 2003; Gunatilaka and Chotikapanich 2009; Morduch and Sicular 2002; Wan 2004).

  5. The semi-log model \(Ln(E)=\beta _0 +\beta _1 F_1 +\beta _2 F_2 +\cdots +\beta _k F_k +\varepsilon _i \)is equivalent to \(E=\exp (\beta _0 +\beta _1 F_1 +\beta _2 F_2 +\cdots +\beta _k F_k +\varepsilon _i )=\exp (\beta _0 )\cdot \prod \nolimits _{k=1}^k {\exp ( \beta _k F_k )}\cdot \exp (\varepsilon _i )\). Then, the contribution \({\beta }_{0}\) is null since it is a constant to each observation.

  6. Independently of the index chosen by the researcher to assess inequality, the natural decomposition of the variance is the unique unambiguous rule given that it is the only decomposition rule that allocates indirect effects among components in a non-arbitrary way. In other words, the contribution of factor k is independent of the inequality index used (see Cowell 2000; Shorrocks 1982, 1983).

  7. GE(2) corresponds to the Theil index with the sensitivity parameter equal to 2. It can be expressed as a linear transformation of CV. The CV is in fact a statistical dispersion index which is scale invariant and that considers all observations uniformly, regardless of its position in the distributive ranking (Duro 2012).

  8. As can be expected the higher correlations belong to cubic and quadratic terms of GDP per capita; however, it must be taken into account that the non-collinearity assumption is about linear relationships among regressors, and despite its high correlation with linear GDP per capita, the cubic and quadratic terms are a non-linear relationship. Hence, it does not violate the basic assumption (Gujarati and Porter 2009). Nevertheless, the results suggest that a non-linear relationship between GDP and \(\hbox {CO}_{2}\) fits better, although the linear term is clearly predominant. Regarding the rest of the explanatory factors, their Variation Inflation Factors (VIF) are well within accepted standards. As a robustness check, other models have been estimated with different regressors than those in Table 3. Results obtained were virtually equivalent.

    Table 3 Results from auxiliary OLS regressions on \(\hbox {CO}_{2}\) per capita and explanatory factors, 1993–2007
  9. Climatologists define a climatic normal as the arithmetic average of a climate element (such as temperature) over a prescribed 30-year interval in order to filter out many of the short-term fluctuations and other anomalies that are not truly representational of the real climate. The last climatic normal available is for the period 1971–2000.

  10. This weight is clearly lower than the obtained by Duro and Padilla (2006) with a different methodology. Their study decomposed per capita \(\hbox {CO}_{2}\) emissions inequality by a multiplicative identity (Kaya factors) using the Theil index. As a result, they obtained an affluence net contribution close to 60 %, being the main contributor to \(\hbox {CO}_{2}\) inequality. However, this difference can be explained by some methodological factors. First, the Kaya identity used in Duro and Padilla (2006) assumes elasticity proportionality by construction, while in our regression model the elasticities are allowed to vary among factors (see York et al. 2003). Second, the affluence contribution is more precisely defined and isolated in our paper, given the more detailed list of potential factors. Their study can therefore be gathering effects that in our case are separated, such as the ones associated with demographic and structure factors.


  • Alcántara V, Duro JA (2004) Inequality of energy intensities across OECD countries: a note. Energy Policy 32(11):1257–1260

    Article  Google Scholar 

  • Alcántara V, Padilla E (2009) Input–output subsystems and pollution: an application to the service sector and \({\rm CO}_{2}\) emissions in Spain. Ecol Econ 68(3):905–914

  • Aldy J (2006) Per capita carbon dioxide emissions: convergence or divergence? Environ Resour Econ 33(4):533–555

    Article  Google Scholar 

  • Atkinson A (1970) On the measurement of inequality. J Econ Theory 3:244–263

    Article  Google Scholar 

  • Barassi MR, Cole MA, Elliott RJR (2011) The stochastic convergence of \({{\rm CO}}_{2}\) emissions: a long memory approach. Environ Resour Econ 49:367–385

  • Barro RJ, Sala-i-Martin X (1992) Convergence. J Politl Econ 100(2):223–251

  • Cantore N (2011) Distributional aspects of emissions in climate change integrated assessment models. Energy Policy 39(5):2919–2924

    Article  Google Scholar 

  • Cantore N, Padilla E (2010) Equality and \({\rm CO}_{2}\) emissions distribution in climate change integrated assessment modelling. Energy 35(1):298–313

  • Cole MA, Neumayer E (2004) Examining the impact of demographic factors on air pollution. Popul Environ 26(1):5–21

    Article  Google Scholar 

  • Cowell F (2000) Chapter 2 measurement of inequality. In: Atkinson AB, Bourguignon F (eds) Handbook of income distribution. Elsevier, Ansterdam, pp 87–166

    Chapter  Google Scholar 

  • Cowell F (2011) Measuring inequality. Oxford University Press, Oxford

    Book  Google Scholar 

  • Cowell FA, Fiorio CV (2011) Inequality decompositions –a reconciliation. J Econ Inequal 2011(9):509–528

  • Criado CO, Grether J (2010) Convergence in per capita \({\rm CO}_{2}\) emissions: a robust distributional approach CEPE Center for Energy Policy and Economics, ETH Zürich

  • Dietz T, Rosa EA, York R (2007) Driving the human ecological footprint. Front Ecol Environ 5(1):13–18

    Article  Google Scholar 

  • Dinda S (2004) Environmental Kuznets curve hypothesis: a survey. Ecol Econ 49(4):431–455

    Article  Google Scholar 

  • Duro JA (2012) On the automatic application of inequality indexes in the analysis of the international distribution of environmental indicators. Ecol Econ 76:1–7

    Article  Google Scholar 

  • Duro JA, Padilla E (2006) International inequalities in per capita \({\rm CO}_{2}\) emissions: a decomposition methodology by Kaya factors. Energy Econ 28(2):170–187

  • Duro JA, Teixidó-Figueras J (2013) Ecological footprint inequality across countries: the role of environment intensity, income and interaction effects. Ecol Econ 93:34–41

  • Ehrlich P, Holdren J (1971) Impact of population growth. Science 171(3977):1212–1217

  • Ezcurra R (2007) Is there cross-country convergence in carbon dioxide emissions? Energy Policy 35(2):1363–1372

    Article  Google Scholar 

  • Fields GS (2003) Accounting for income inequality and its change: a new method, with application to the distribution of earnings in the United States. Res Labor Econ 22:1–38

    Article  Google Scholar 

  • Fourcroy C, Gallouj F, Decellas F (2012) Energy consumption in service industries: challenging the myth of non-materiality. Ecol Econ 81:155–164

    Article  Google Scholar 

  • Friedl B, Getzner M (2003) Determinants of \({\rm CO}_{2}\) emissions in a small open economy. Ecol Econ 45(1):133–148

  • Grossman G (1993) Pollution and growth: What do we know?. C.E.P.R, Discussion Papers

  • Gujarati DN, Porter DC (2009) Basic econometrics, 5th edn. McGraw-Hill, Boston

    Google Scholar 

  • Gunatilaka R, Chotikapanich D (2009) Accounting for Sri Lanka’s expenditure inequality 1980–2002: regression-based decomposition approaches. Rev Income Wealth 55(4):882–906

    Article  Google Scholar 

  • Hedenus F, Azar C (2005) Estimates of trends in global income and resource inequalities. Ecol Econ 55(3):351–364

    Article  Google Scholar 

  • Heil MT, Wodon QT (1997) Inequality in \({\rm CO}_{2}\) emissions between poor and rich countries. J Environ Dev 6(4):426–452

  • Heil MT, Wodon QT (2000) Future inequality in \({\rm CO}_{2}\) emissions and the impact of abatement proposals. Environ Resour Econ 17(2):163–181

  • Jiang L, Hardee K (2011) How do recent population trends matter to climate change. Popul Res Policy Rev 30(2):287–312

    Article  Google Scholar 

  • Jobert T, Karanfil F, Tykhonenko A (2010) Convergence of per capita carbon dioxide emissions in the EU: legend or reality? Energy Econ 32:1364–1373

    Article  Google Scholar 

  • Jones DW (1989) Urbanization and energy use in economic development. Energy J 10:29–44

    Article  Google Scholar 

  • Kaya Y (1989) Impact of carbon dioxide emission control on GNP growth: interpretation of proposed scenarios. Paper presented to the Energy and Industry subgroup, Response strategies working group. Intergovernmental Panel on Climate Change, París, Francia

  • List J, Gallet C (1999) The environmental Kuznets curve: does one fits all? Ecol Econ 31:409–423

    Article  Google Scholar 

  • Liu J, Daily GC, Ehrlich P, Luck GW (2003) Effects of household dynamics on resource consumption and biodiversity. Nature 421:530–533

    Article  Google Scholar 

  • Martinez-Zarzoso I, Bengochea-Morancho A (2003) Testing for an environmental Kuznets curve in latin-american countries. Revista de Análisis Económico 18:3–26

    Google Scholar 

  • Martínez-Zarzoso I, Bengochea-Morancho A (2004) Pooled mean group estimation of an environmental Kuznets curve for \({\rm CO}_{2}\). Econ Lett 82:121–126

  • Morduch J, Sicular T (2002) Rethinking inequality decomposition with evidence from rural China. Econ J 112(476):93–106

    Article  Google Scholar 

  • Nansai K, Kagawa S, Suh S, Fujii M, Inaba R, Hashimoto S (2009) Material and energy dependence of services and its implications for climate change. Environ Sci Technol 43:4241–4246

    Article  Google Scholar 

  • Neumayer E (2004) National carbon dioxide emissions: geography matters. Area 36(1):33–40

    Article  Google Scholar 

  • Padilla E, Serrano A (2006) Inequality in \({\rm CO}_{2}\) emissions across countries and its relationship with income inequality: a distributive approach. Energy Policy 34(14):1762–1772

  • Parikh J, Shukla V (1995) Urbanization, energy use and greenhouse effects in economic development: results from a cross-national study of developing countries. Global Environ Change 5:87–103

    Article  Google Scholar 

  • Perman R, Stern D (1999) The environmental Kuznets curve: implications of non-stationarity. The Australian National University, Centre for Resource and Environmental Studies, Working Paper in Ecological Economics No 9901

  • Perman R, Stern D (2003) Evidence from panel unit root and cointegration test that the environmental Kuznets curves does not exist. Aust J Agric Resour Econ 47:325–347

    Article  Google Scholar 

  • Piaggio M, Alcántara V, Padilla E (2015) The materiality of the immaterial. Services sectors and \({\rm CO}_{2}\) emissions in Uruguay. Ecol Econ 110:1–10

  • Piaggio M, Padilla E (2012) \({\rm CO}_{2}\) emissions and economic activity: heterogeneity across countries and non-stationary series. Energy Policy 46:370–381

  • Quah DT (1996) Empirics for economic growth and convergence. Eur Econ Rev 40(6):1353–1375

  • Romero-Ávila D (2008) Convergence in carbon dioxide emissions among industrialized countries revisited. Energy Econ 30:2265–2282

    Article  Google Scholar 

  • Sen A (1973) On economic inequality. Clarendon Press, Oxford

    Book  Google Scholar 

  • Sengupta R (1996) Economic development and \({\rm CO}_{2}\) emission: economy-environment relation and policy approach to choice of emission standard for climate control. Boston University, Institute for Economic Development, Boston University, Institute for Economic Development

  • Sharma SS (2011) Determinants of carbon dioxide emissions: empirical evidence from 69 countries. Appl Energy 88(1):376–382

    Article  Google Scholar 

  • Shorrocks AF (1982) Inequality decomposition by factor components. Econometrica 50(1):193–211

    Article  Google Scholar 

  • Shorrocks AF (1983) The impact of income components on the distribution of family incomes. Q J Econ 98(2):311–326

    Article  Google Scholar 

  • Stern DI, Common MS, Barbier EB (1996) Economic growth and environmental degradation: the environmental Kuznets curve and sustainable development. World Dev 24(7):1151–1160

    Article  Google Scholar 

  • Strazicich MC, List JA (2003) Are \({\rm CO}_{2}\) emission levels converging among industrial countries? Environ Resour Econ 24(3):263–271

  • Suh S (2006) Are services better for climate change. Environ Sci Technol 40(21):6555–6560

    Article  Google Scholar 

  • Suri V, Chapman D (1998) Economic growth, trade and energy: implications for the environmental Kuznets curve. Ecol Econ 25(2):195–208

    Article  Google Scholar 

  • Taskin F, Zaim O (2000) Searching for a Kuznets curve in environmental efficiency using kernel estimation. Econ Lett 68(2):217–223

    Article  Google Scholar 

  • Teixidó-Figueras J, Duro JA (2015) International ecological footprint inequality: a methodological review and some results. Environ Resour Econ 60(4):607–631

  • Torras M, Boyce JK (1998) Income, inequality, and pollution: a reassessment of the environmental Kuznets curve. Ecol Econ 25(2):147–160

    Article  Google Scholar 

  • Van Nguyen P (2005) Distribution dynamics of \({\rm CO}_{2}\) emissions. Environ Resour Econ 32(4):495–508

  • Wan G (2004) Accounting for income inequality in rural China: a regression-based approach. J Comp Econ 32(2):348–363

    Article  Google Scholar 

  • White TJ (2007) Sharing resources: the global distribution of the ecological foot-print. Ecol Econ 64(2):402–410

  • World Bank (2013) World development indicators and climate change knowledge portal. Accessed on Feb 2013

  • York R, Rosa EA, Dietz T (2003) STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecol Econ 46(3):351–365

    Article  Google Scholar 

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We are grateful for the helpful comments from three anonymous reviewers. We acknowledge the support from projects ECO2013-45380-P and ECO2012-34591 (Spanish Ministry of Economy and Competitiveness), 2014SGR950, XREPP, and XREAP (DGR).

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Correspondence to Emilio Padilla.

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Duro, J.A., Teixidó-Figueras, J. & Padilla, E. The Causal Factors of International Inequality in \(\hbox {CO}_{2}\) Emissions Per Capita: A Regression-Based Inequality Decomposition Analysis. Environ Resource Econ 67, 683–700 (2017).

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  • \(\hbox {CO}_{2}\) emissions
  • International emissions inequality
  • Regression-based decomposition

JEL Classification

  • C19
  • D39
  • Q43