An empirical analysis of output volatility and environmental degradation: A spatial panel data approach

This study examines the effect of environmental degradation on output volatility using spatial panel data models namely spatial lag model (SLM), spatial error model (SEM) and spatial Durbin model (SDM). For a comparative analysis, non-spatial models namely pooled OLS, fixed effects and random effects are also employed. Environmental degradation is proxied through carbon dioxide (CO2) emissions. The analysis is performed for 127 nations covering the period from 1996 to 2014. The findings reveal that environmental degradation increases output volatility in both non-spatial and spatial models. Furthermore, spillover effects of carbon emissions are positive and significant suggesting that national output volatility increases with the increase in adjacent economy’s carbon emissions. Further, the spatial parameters spatial rho and lambda are also positive and significant proving the arguments that output volatility in one specific location spillovers to the nearby neighbor and to the far away trading partner. These findings are robust to the use of methane emissions as an alternative environmental indicator. The findings suggest that to attain sustainable growth, regulatory authorities need to take actions for minimizing environmental degradation not only from its own country but also from the international community to control the environmental stress.