Analyzing Environmental Kuznets Curve and Pollution Haven Hypothesis in India in the Context of Domestic and Global Policy Change

While the notion of Environmental Kuznets Curve (EKC), which relates economic development to pollution, is well established, there is controversy about its shape, incidence and determinants. Moreover, there is an avowed relationship between economic development and international trade. This leads to the conceptualization of the trade-environment triangle between type of economic development, environment and trade and investment. Therefore, the study of EKC is incomplete without accounting for the pollution haven hypothesis (PHH). The original EKC literature restricts itself to a quadratic form whereas the new literature establishes that a cubic form is more appropriate. Also, the traditional EKC literature with few exceptions (Murthy and Bhasin, 2016) is stand-alone and does not incorporate PHH. Against this backdrop, we evolve a framework, which is based on both the critical evaluation of extant studies and an extension of these studies to the Indian context. We model EKC using alternative model specifications to bridge the gap between conventional and modern EKC literature. We further synthesize a model that combines the effect of EKC as well as PHH in the Indian context. We substantiate a cubic form of EKC for India for the time period 1991 to 2014. With aggregate CO2 emissions as the dependent variable, the linear (2.34E-06), quadratic (-1.2E-18) and cubic (2.64E-31) terms are all significant with the right signs, which confirms an N-shaped EKC for India. We also validate PHH for India in our model integrating EKC and PHH. Even with per capita emissions as the dependent variable, existence of an N-shaped EKC is established. In this case however, evidence on the cubic term is rather weak (p-value = 0.1250), which points towards the difference in the socio-psychological factors that influence the revival of the upturn in the case of India. Also, FDI has a smaller influence in per capita terms, but its coefficient is more significant, which means that we cannot ign