The genesis of Environmental Kuznets curve (EKC) of “grow now clean later” has led to a substantial deterioration of local as well as the global environment. India has not been spared of this malaise and accounts for the third-largest carbon dioxide emitter in the world. Thus, the present study revisits the curvilinear relationship between economic growth and environmental pollution in case of India over the period of 1971-2014.
Dickey–Fuller generalised least square (DF-GLS) test developed by Elliott et al. is used to ensure that none of the variables is I(2). The study applies the autoregressive distributed lag (ARDL) bounds estimation technique to test for the existence of cointegration among variables and estimate long-run and short-run parameters. The study also applies the Bai–Perron structural break test with unknown break date to determine the threshold point. The study further uses the vector error correction model (VECM) Granger causality test to check the direction of causality between variables.
The ARDL bounds estimation technique confirms the cointegration among variables. The long-run coefficients of energy consumption, economic growth and financial development are found to have an adverse impact on environmental quality. The results also validate the existence of conventional EKC hypothesis. Bai–Perron structural break test, along with t-test and scatter graph, shows that inverted U-shaped relationship between environmental pollution and economic growth holds true. The VECM-based causality results support “growth hypothesis” both in the long run and short run.
This study refrained from considering a variety of variables, as the main intention of the study is to investigate whether any threshold or turnaround point exists for India. The future studies should consider a new set of variables (e.g. population, corruption index, social indicators, political scenario, energy research and development expenditures, foreign capital inflows, public investment towards alternate energy exploration, etc.) in the estimation of EKC hypothesis.
The results validate the existence of conventional EKC hypothesis. Thereby the study argues that instead of being a threat to environmental quality, economic growth is observed to generate a sustainable environment to live in. Further, bi-directional causality is found between carbon emissions and economic growth. Thus, any effort to mitigate CO2 or environment conservation policy will impede economic growth. Consequently, controlling primary energy consumption and supply and replacing it with renewable and clean energy could be desirable for climate change mitigation.
The data set has been refined so that the EKC estimation issues raised by Stern (2004) are addressed. In particular, statistical properties of the data set such as serial correlation, presence of a stochastic or deterministic trend, has been adequately taken care of to remove any spurious correlation. Finally, various control variables have been included to provide consideration to issues of model adequacy, such as the possibility of omitted variables bias. To the authors’ best knowledge, there is no India-specific study which has taken care of data-related issues, as suggested by Stern, in the estimation of a curvilinear relationship between environmental degradation and economic growth in India. Further, this is the first study which has used Bai–Perron structural break test with unknown break date to identify the threshold point while estimating EKC in India.
The authors thank three anonymous referees of this journal, for useful inputs. The authors would also like to thank Editor Dr Prasanta Dey for editorial assistance as well as inputs on an earlier version of the paper.
Disclosure statement: No potential conflict of interest was reported by the authors.
Funding: This research has received no specific funding.
Rasool, H., Malik, M. and Tarique, M. (2020), "The curvilinear relationship between environmental pollution and economic growth: Evidence from India", International Journal of Energy Sector Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJESM-04-2019-0017Download as .RIS
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