Estimating the Railways Kuznets Curve for high income nations: A GMM approach for three pollution indicators - Syeda Anam Hassan; Misbah Nosheen (Energy Reports, 2019)
Railways transportation is still taken as an account of economic growth without considering the cost of environmental damages. Therefore, it is an urgent need at the national and global level to take into account the sustainability of environment by key sector. So this analysis has contributed in unveiling the striking facts and figures for environment with regard to railways transportation sector. The aim of this study is to investigate the "Railways Kuznets Curve" in context of three key pollution emissions Carbondioxide emission, Nitrous oxide emissions and Methane emissions for 37 High income nations during 1990 to 2017. The econometric results of Panel GMM shows that there exists "U shaped" Railways Kuznets curve for carbondioxide and methane emissions indicating increased level of pollution. While nitrous oxide emissions exhibit the "inverted U shaped curve" validating the Railways Kuznets curve. The Pairwise Dumitrescu Hurlin Panel Causality shows a bidirectional relation of nitrous and methane emissions and uni-directional association of CO2 emissions to railways transportation. The Impulse Response function and Variance decomposition indicates a diverging drift of CO2 and CH4 emissions while converging trend of ~N2O emissions to railways transport sector. The diagnostic test of ARCH-effect and serial correlation shows the homoscedasticity and no autocorrelation along with CUSUM test that shows stability of all the three models. On the basis of econometric results, this analysis suggests that developed nations should focus on exploiting renewable energy resources along with adopting fuel-saving traveler and freight technologies including hybrid switch trains and hydrogen-powered steam engines that thorough cuts the diesel fuel.
Keywords: 330; Impulse response; Panel GMM; Pollution emissions; Railways Kuznets Curve; Railways transportation; Variance decomposition