Environmental impact of the shadow economy, globalisation, and human capital: Capturing spillovers effects using...

Most of the environmental degradation literature evaluates the determinants of polluting gas emissions as a spatially static process. However, environmental pollution is a problem that is not limited to the borders of the countries. One way to capture temporal and spatial changes in pollutant emissions is by using the benefits of spatial panel data models. This research aims to empirically examine the environmental impact of the shadow economy, the globalisation index, and the human capital index in 101 countries during 1995-2018. We employ a set of spatial autoregressive models (SAR), Durbin spatial models (SDM), and spatial lag models (SLX) of panel data to estimate direct, indirect, and total impacts. The results are stable before changes in the econometric specification and different ways of calculating spatial weights matrix. The results show that polluting gas emissions have a high spatial dependence on all specifications. The interdependence between the countries explains the spillover effect of environmental pollution on the rest of the countries that are geographically close. The policy implications derived from our research point to achieving sustainable economic and environmental development, where coordinated actions among countries and greater regulation of the behaviors of economic agents related to the shadow economy are recommended.