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LPG consumption and environmental Kuznets curve hypothesis in South Asia: a time-series ARDL analysis with multiple structural breaks

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Abstract

This paper aims to scrutinize the validity of the greenhouse emissions-induced environmental Kuznets curve (EKC) hypothesis, controlling for liquefied petroleum gas (LPG) consumption, FDI inflows, and trade openness, in the context of six South Asian economies. Besides, the impacts of LPG use on both aggregate and disaggregated emissions of greenhouse gases are also evaluated. Using annual data from 1980 to 2016, the elasticity estimates from the autoregressive distributed lag (ARDL) regression analysis confirms the authenticity of the EKC hypothesis for Bangladesh, India, Sri Lanka, and Bhutan. In the cases of Pakistan and Nepal, economic growth, in the long-run, is evidenced to monotonically increase and decrease the greenhouse emissions, respectively. However, LPG consumption is found to homogenously reduce all types of greenhouse emissions in each of the selected South Asian nations. Moreover, in majority of the cases, statistical evidence of joint favorable impacts of economic growth and LPG consumption on the environment are ascertained. Furthermore, the Hacker and Hatemi-J bootstrapped causality analysis finds causal relationships between economic growth, greenhouse emissions, and LPG consumption. However, the causality estimates are found to be heterogeneous across the different South Asian nations considered in the analysis. The results, in a nutshell, denote that economic growth is both the cause and the solution to the greenhouse emission problems faced by the South Asian economies. Moreover, the results also assert that LPG can be a transitional fuel to reduce these emissions before the South Asian nations are ready to undergo transition from non-renewable to renewable energy consumption. Hence, the findings impose key fuel-diversification policy implications for the South Asian governments.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request

Notes

  1. For more information on the environmental aspects of the SDG see UNDP (2018, 12 March) and Murshed et al. (2020a).

  2. For an in-depth understanding of the scale, composition and technique effects under the EKC hypothesis see Liobikienė and Butkus (2019).

  3. The selection of the South Asian countries is based on data availability which led to leaving out Afghanistan and Maldives from the empirical analyses.

  4. The BRICS countries include Brazil, Russia, India, China, and South Africa.

  5. For more information on the PHH, see Shao et al. (2019) and Guzel and Okumus (2020).

  6. In this paper, the unit root estimates from the Lee and Strazicich (2013) are based on model A in which the SB is assumed to be in the intercept.

  7. It is admitted that using an instrumental variable approach to perform the regression analysis would have been ideal to account for the possible endogeneity issues stemming from the potential omitted variable bias and reverse causation among the dependent and independent variables. However, finding credible instruments is a cumbersome task whereby such an approach could not be adopted. Besides, it could have been better research questions asked in this paper were answered by performing the econometric analysis within a natural experimental setting.

  8. It must be noted that if the line plots of any variable over the time period of the study depict a trend, then the series can be referred to as non-stationary. In contrast, if the line plot depicts fluctuations, it implies that the series is mean-reverting which confirms that the series is trend stationary. In Figs. 1, 2, and 3, there are graphical illustrations of fluctuations which imply that all the variables are stationary at their first differences.

  9. The pollution halo effect refers to a reduction in greenhouse emissions following a rise in the volume of FDI inflows. For a more in-depth understanding of the pollution halo effect of international FDI inflows, see Mert and Caglar (2020).

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Correspondence to Muntasir Murshed.

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Appendix

Appendix

Fig. 1
figure 1

The line plots of all the variables in the context of Bangladesh and India. The line plots of the variables are in their respective first difference form

Fig. 2
figure 2

The line plots of all the variables in the context of Pakistan and Sri Lanka. The line plots of the variables are in their respective first difference form

Fig. 3
figure 3

The line plots of all the variables in the context of Nepal and Bhutan The line plots of the variables are in their respective first difference form

Fig. 4
figure 4

The SUSUM and CUSUMSQ plots in the context of Bangladesh

Fig. 5
figure 5

The SUSUM and CUSUMSQ plots in the context of India

Fig. 6
figure 6

The CUSUM and CUSUMSQ plots in the context of Pakistan

Fig. 7
figure 7

The CUSUM and CUSUMSQ plots in the context of Sri Lanka

Fig. 8
figure 8

The CUSUM and CUSUMSQ plots in the context of Nepal

Fig. 9
figure 9

The CUSUM and CUSUMSQ plots in the context of Bhutan

Table 11 Time-series long-run elasticity estimates from the FMOLS regression analyses

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Murshed, M. LPG consumption and environmental Kuznets curve hypothesis in South Asia: a time-series ARDL analysis with multiple structural breaks. Environ Sci Pollut Res 28, 8337–8372 (2021). https://doi.org/10.1007/s11356-020-10701-7

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