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In the past decades, renewable energy consumption has grown considerably because of environmental degradation caused by non-renewable energy consumption. This research aims to find the causal link between renewable and non-renewable energy consumption, human capital, and non-renewable energy price for the 53 most renewable energy-consuming countries worldwide (hydroelectric) during the period 1990–2017. We use data collected from the World Bank (http://data.worldbank.org/data-catalog/world-development-indicators, 2018) and Statistical Review of World Energy (https://www.bp.com/, 2018). We test simultaneously two types of regressions in order to measure the degree of elasticity of the two types of energy by using econometric techniques for panel data. The results of the GLS models indicate that human capital has a stronger significant effect on renewable energy consumption at the global level, in the middle high-income countries and low-middle income countries, compared with non-renewable energy consumption. Besides, at the global level, there is a positive and statistically significant relationship between the non-renewable energy price and the two types of energy consumption. There is a long-run consumption of both types of energy. On the other hand, the one-way relationship between human capital and non-renewable energy price and renewable energy consumption is stronger than the relationship with non-renewable energy consumption. The policy implications derived from this study should be designed to promote human capital development in order to promote renewable energy consumption and increase the investment in renewable energy sources to guarantee their access to lower prices that reduce non-renewable energy consumption. [ABSTRACT FROM AUTHOR]

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. • We explore the environmental impact of the shadow economy, globalisation, and human capital index. • We estimate the spatial spillover effects of environmental degradation. • The shadow economy increases environmental degradation, globalization and human capital decrease it. [ABSTRACT FROM AUTHOR]