Investigating the impacts of technological innovation and renewable energy on environmental pollution in countries selected by the International Renewable Energy Agency: A quantile regression approach

Investigating the factors affecting CO2 emissions has always been a challenge. One problem with existing studies is that these studies have been relied on mean-based regression approaches, such as ordinary least squares (OLS) or instrumental variables, which implicitly assumes that the impact of variables along the distribution of CO2 emissions is the same. Unlike previous studies, the present study will use the quantile regression developed by Koenker & Bassett, which is not limited to the assumption. So that, the purpose of this study is to investigate the impacts of technological innovation and renewable energy on CO2 emissions in selected countries of the International Renewable Energy Agency (IRENA) using quantile regression over the period 1990-2016. The results of this study exhibited that the impact of renewable energy on CO2 emissions was negative and statistically significant. This impact is also enhanced in high quantiles (countries with high pollution). In all the studied quantiles, the impact of technological innovations on CO2 emissions was positive, significant and initially decreasing, while increasing again over time. The results of the symmetry test also indicated that by increasing in the volume of CO2 emissions, the variable impact of renewable energy upraised. However, no incremental trend was observed in innovation.

Investigating the impacts of technological innovation and renewable energy on environmental pollution in countries selected by the International Renewable Energy Agency: A quantile regression approach Keywords:

Technological innovation , Renewable energy , Environmental pollution , Quantile