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Is skewed income distribution good for environmental quality? A comparative analysis among selected BRICS countries

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Abstract

A large number of studies have examined the linkage between income inequality and environmental quality at the individual country levels. This study attempts to examine the linkage between the two factors for the individual BRICS economies from a comparative perspective, which is scarce in the literature. It examines the selected countries (Brazil, India, China and South Africa) by endogenising the patterns of primary energy consumption (coal use and petroleum use), total primary energy consumption, economic growth, and urbanisation as key determining factors in CO2 emission function. The long-run results based on ARDL bounds testing revealed that income inequality leads to increase in CO2 emissions for Brazil, India and China, while the same factor leads to reduction in CO2 emissions for South Africa. However, it observes that while coal use increases CO2 emissions for India, China and South Africa, it has no effect for Brazil. In contrast, the use of petroleum products contributes to CO2 emissions in Brazil, while the use of the same surprisingly results in reduction of carbon emissions in South Africa, India and China. The findings suggest that given the significance of income inequality in environmental pollution, the policy makers in these emerging economies have to take into consideration the role of income inequality, while designing the energy policy to achieve environmental sustainability.

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Notes

  1. Unmitigated climate change poses great risks to human health, global food security and economic development. Considering climate change as a global public good, the international society as a whole, therefore, needs to take measures to adapt to these unavoidable impacts while taking action to cut down the greenhouse gas emissions that are contributing to climate change and global warming. Otherwise, countries are always carrying the unavoidable risks not only in the present but also into the future.

  2. After China registered as the world’s largest ever CO2 emitter since 2008, its share of carbon emissions has reached 25% of the global greenhouse gas emissions in 2012, and had further risen to 29% of the total greenhouse gas emissions in 2015. Air pollution has become one of the most common and pressing environmental issues not only in populated countries viz. China and India but also in most of the countries due to heavy reliance on pollution-intensive energy sources. China’s environmental deterioration and vulnerability have seriously threatened the physical and psychological health of the Chinese citizens and have seriously dented China’s international image.

  3. As far as the structural feature of these BRICS is concerned, Brazil specialises in agriculture, India and South Africa specialise in services, China specialises in manufacturing and Russia specialises in commodities.

  4. The inverted U-shaped hypothesis shows the non-linear relationship between the series, indicating that economic growth initially increases income inequality and reduces it after reaching a threshold level.

  5. The Bhopal tragedy in India was a case of evidence, indicating not only that gas leaks from the chemical factory claimed thousands of lives (maximum 16,000) but also its adverse effects on the lives and health of the poor community are unbearable via socio-economic support of the government. This true incidence occurred in the 2nd of December 1984 of the Indian soil as one of the “world’s worst industrial disaster” which is primarily consistent with the theoretical argument of Hamilton (1995) who reported that environmentally hazardous facilities created by the rich community are harmful to the lives of many poor people.

  6. Indira Gandhi also argues that poverty and need of the poor community are the biggest polluters in developing countries like India.

  7. Deforestation has been ranked as the third largest source of greenhouse gas emissions which generates between 15 and 20% of overall carbon emissions (Wolde-Rufael and Idowu, 2017).

  8. \( {I}_t=\alpha \kern0em {P}_t^a{A}_t^b\;{T}_t^c\;{\varepsilon}_t \)\( {I}_t=\alpha \kern0em {P}_t^a{A}_t^b\;{T}_t^c\;{\varepsilon}_t \), where It denotes environmental impacts of population (Pt  ), affluence (At) and technology (Tt  ) at the time period t. The explanatory variable coefficients are represented by a, b, and c, and ε also represents the random error. Though the Impacts of Population, Affluence and Technology (IPAT) model has been proposed by Ehrlich and Holdren (1970), it has some limitation that no one factor can be held singularly responsible for measuring environmental impacts. This model is only limited to the consideration of three variables in the carbon emissions function. To overcome these limitations, Dietz and Rosa (1994, 1997) reformulated IPAT into a STIRPAT model which has its own flexible capability in adding factors other than population, affluence and technology in order to understand the effects of each and every factor independently (York et al. 2003). Hence, our study not only considers carbon emissions (CO2) for measuring environmental quality and followed by urbanisation for population growth, GDP per capita for affluence and total energy use for technology but also incorporates income inequality as one of the explanatory variables in the carbon emissions function because of its theoretical and empirical effects on the natural environment.

  9. The reason for using carbon dioxide emissions (CO2) is because CO2 emissions account for 90% of the increases in global anthropogenic greenhouse gas emissions (GHGs) in 2010 followed by the respective contribution of other carbon emission indicators, such as CH4 (9%) and NO2 (1%). From such evidence, it is clear that CO2 emissions are global indicators of carbon dioxide emissions and followed by local emissions such as CH4 and NO2 (https://www.eia.gov/).

  10. The data on post-tax/transfer income inequality called “net Gini coefficient” sourced from SWIID are not available uniformly for BRICS countries (1980–2012 for Brazil, 1980–2013 for China, 1980–2009 for India and 1980–2011 for South Africa). Since the data on other control variables are consistently available for 1980–2013, we have used the extrapolation method to obtain the data points on GINI coefficients uniformly, so as to match this periodic information with the available information on other variables used in the estimation. This is done in order to observe the relationship over a long run in a contrasting perspective.

  11. According to Solt (2016), the advantage of this data set is to provide researchers with comprehensive data that maximise for the broadest possible sample of countries and years.

  12. Both primary oil use and coal consumption data sourced from EIA are available in terajoule units. To get per-head energy consumption, we have divided each category of energy consumption by total population of the respective countries. We have also used a standardised formula of “1 Terajoule = 23,884.6 kg of oil equivalent” for our analysis. http://extraconversion.com/energy/tonnes-of-oil-equivalent/tonnes-of-oil-equivalent-to-terajoules.html

  13. The plausible reason for considering BRICS countries in our analysis is that they are the largest contributor to global emissions, accounting for 38.98% of global CO2 emissions in 2016. Further, the highest contribution to global CO2 emissions is mainly China (28.2%), followed by other countries such as India (6.24%) and Russia (4.53%) (see at https://www.statista.com/statistics/271748/the-largest-emitters-of-co2-in-the-world/) among the BRICS. Another reason for considering the selected BRICS countries is that these are the countries, China (0.43), India (0.33) and Russia (0.40), which have low income distribution (http://www.hsrc.ac.za/en). According to BRICS Energy Indicators (2016), the BRICS region almost account for 37% of the world energy demand. Given a rising population and faster economic prosperity in terms of economic growth, it is significant to study the carbon emissions-income inequality nexus for these selected BRICS region within a time series framework. Any suggested policy implications could be drawn from our findings that would be beneficial for the BRICS countries in minimising the environmental consequences of climate change and global warming.

  14. Our analysis excluded the Russian Federation from the BRICS region due to limited availability of annual time series data points. Although the data on CO2 emissions, GDP per capita and urbanisation for Russia along with other key variables were available from the year 1989 onwards from the World Development Indicators (CD-ROM, 2013), the number of observations was not sufficient to establish a robust statistical relationship among the variables in the model due to over-parameterization of the model. Although the study was initially interested in having a panel analysis along with time series analysis for the individual countries, the panel cointegration results were not promising as it failed to establish cointegration among the variables. Therefore, the cointegration test result is not reported here purely due to space constraint. However, it can be available upon request from the authors. Therefore, our analysis surrounds analysing selected BRICS countries exclusive of the Russian Federation.

  15. The primary reason for using both primary coal and petroleum oil as pattern of energy consumption for the selected BRICS region (Brazil, India, China and South Africa) is that components of energy consumption are major driving factors for rising total energy consumption. This is evidenced for the Brazilian economy where petroleum oil accounts for 39% of total primary energy consumption in 2014 followed by primary coal use (6%) among the rest of the energy consumption indicators (e.g. natural gas, nuclear, hydroelectric and non-hydro RE-biomass, wind, solar, waste and geothermal). In the case of the Indian economy, both primary coal and petroleum oil are also major energy sources in total primary energy consumption in 2014 as they account for 46 and 22% in total energy consumed. Similarly, both primary coal use and petroleum use also account for 66 and 16% of total primary energy consumption during the same period in China. Finally, for South Africa, primary coal use and petroleum oil consumption constitute around 66 and 17% in proportion to the total primary energy (www.greenpeace.de). Russia is a major exporter of oil, natural gas and coal to the world. Carbon emissions in Russia have dropped mainly due to the collapse of the economy in the 1990s after the dissolution of the Soviet Union. And, there is no policy guidance to reduce dependence on oil, gas and coal in Russia.

  16. Brazil has a less share of coal use to the total primary energy consumption because of the fact that the Brazilian economy is one of the giants in the production of renewable energy among all BRICS region, as the share of renewable energy power to total energy supply in 2014 was 75%, followed by China (22%), Russia (16%), India (16%) and South Africa (2%) (see at www.greenpeace.de).

  17. The Engle and Granger (1987) test is used for two variables; the Johansen (1991) technique is used for more than two variables. Johansen and Juselius (1990) extended the vector autoregression (VAR) model. Moreover, it is only applicable in the case of a large sample size and all variables should be integrated of the same order for the cointegrated VAR model.

  18. Pesaran et al. (2001) initially reported two types of critical values, i.e. lower and upper bounds. If variables are I(0), then the decision on cointegration follows the lower bound and if the variables are I(1), the decision hinges on the upper bound.

  19. The critical lower and upper bound values as suggested by Pesaran et al. (2001) can be useful for large sample size (t = 500–40,000). Hence, the use of these bounds values can provide biased results in the case of a small sample size.

  20. Country-wise correlation has been found and not reported here due to the lack of space. This can be available upon request from the authors of this study.

  21. The carbon emission model is estimated in the analysis in its two variants. Once it is estimated with total energy consumption, income inequality, economic growth and urbanisation (model 1), and alternatively, it is estimated with two different components of energy viz. coal use, petroleum use, income inequality, economic growth and urbanisation (model 2) by replacing the total energy consumption in model 1. This is intended to assess the relative effects of total energy consumption against the effects of two major pollution-intensive components of total energy such as coal and petroleum on the levels of carbon emissions.

  22. According to the World Bank (2009), South Africa is a developing region because of its low life expectancy, poverty and monetary and racial inequalities. The World Bank further noted that South Africa stands at 63.1 Gini index which seems to be the highest in the world during 2009. Moreover, May (1998) noted that the gap in South African society is not just a monetary one but also a racial one. Though inequality has historically been an issue in South Africa from the beginning of the Dutch colonisation period (1600–1990), it is beneficial for environmental quality via reducing the carbon emissions. This may be because of that fact that most of the poor black people live in a carbonless economy of South Africa where they often consume greater amounts of biomass energy than modern sources of energy (e.g. coal and petrol) and thereby contributing less to greenhouse gas emissions. This may be one of the underlying reasons behind the driving role of income inequality towards improving environmental quality in South Africa.

  23. The rest of the short-run results are not discussed here due to space constraint.

  24. Thus, while there are scale effects associated with intense urbanisation (induced mainly by industrialisation and population growth), there are also technical effects which could result in economies of scale in the provision and protection of environmental services in the urban centres by creating environmentally conscious citizens and encouraging them to use energy-saving technology in city locations. Therefore, whether the effect of urbanisation is positive or negative on emissions levels in economies, it would largely hinge on which of these two effects dominate. This could be one of the research gaps for the future research studies.

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Acknowledgements

We acknowledge the comments received from the conference participants and paper panellists in the 1st International Conference on “Energy, Finance and the Macroeconomy (ICEFM)” during 22–24 November 2017, held at Montpellier Business School, France. We also acknowledge the comments received from the workshop participants and paper panellists on “Multi-scale Climate Governance in India: Understanding the Challenges and Opportunities” during 18–19 January 2018, held at TERI School of Advanced Studies, New Delhi. We would also like to thank the editor and three anonymous referees for constructive and useful comments.

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Authors and Affiliations

  1. Department of Humanities and Social Sciences, Indian Institute of Technology (IIT) Kharagpur, Medinipur, West Bengal, 721302, India

    Mantu Kumar Mahalik

  2. Centre for Development Studies (CDS), Prasanth Nagar, Ulloor, Trivandrum, Kerala, 695011, India

    Hrushikesh Mallick

  3. Department of Humanities and Social Sciences, National Institute of Technology (NIT), Rourkela, Sundargarh, Odisha, 769008, India

    Hemachandra Padhan

  4. Anandapur College, Anandapur, Keonjhar, Odisha, 758021, India

    Bhagaban Sahoo

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Mahalik, M.K., Mallick, H., Padhan, H. et al. Is skewed income distribution good for environmental quality? A comparative analysis among selected BRICS countries. Environ Sci Pollut Res 25, 23170–23194 (2018). https://doi.org/10.1007/s11356-018-2401-8

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