The contribution of battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) to mitigating/reducing fine particulate matter (PM2.5) emissions was researched through a panel of 29 European countries from 2010 to 2019, using the econometric technique of method of moments quantile regression (MM-QR). This research is innovative by connecting the increasing use of electric vehicles with PM2.5 emissions and using the MM-QR to explore this relationship. Two models were estimated to analyse their contribution to reducing PM2.5 in European countries. The nonlinearity of the models were confirmed. The statistical significance of the variables is strong for the upper quantiles (75th and 90th), resulting from the effectiveness of European policies to improve the environment. Electric vehicles (BEVs and PHEVs), economic growth, and urbanisation reduce the PM2.5 problem, but energy intensity and fossil fuel consumption aggravate it. This research sheds light on how policymakers and governments can design proposals to encourage electric vehicle use in European countries. To achieve the long-term climate neutral strategy by 2050, it is imperative to implement effective policies to reduce the consumption of fossil fuels and promote the adoption of electric vehicles using renewable energy sources.

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The research was performed in sixteen emerging countries from 1990 to 2014, using a two-step approach. First, a slacks-based (SBM) -data envelopment analysis (DEA) model annually estimates countries' resources and energy efficiency. In the second step, a panel quantile regression was used to assess the impacts of resources and energy efficiency, export quality, and the other variables on the ecological footprint. The SBM-DEA model revealed that Turkey and Hungary were the countries that got the better rank, and China and India got the worst rank on resources and energy efficiency mean. Quantile regression revealed that resources, energy efficiency, and trade openness reduce the ecological footprint. On the other hand, GDP, consumption of fossil fuels, and population contribute to deteriorating the environmental footprint. Export quality and urban population worsen the ecological footprint but only in some quantiles. Export quality in 10th and 25th quantiles and the case of the urban population all quantiles except the 10th one aggravates the ecological footprint. Thus, from a policy perspective, we have variables that require different kinds of intervention to mitigate/reduce the ecological footprint, i.e., requires many policy measures and the active collaboration of citizens.

The effect of energy policies on the energy performance of residential properties/houses in nineteen Portuguese districts from 2014 to 2021 was investigated. A linear random-effects model regression was used as the method in this empirical investigation. The empirical results indicated that the income per capita has a negative effect on residential properties with high energy efficiency certificates (e.g., A+, A, and B) and a positive impact on residential properties with low energy efficiency certificates (e.g., C, D, E, and F); the codes and standards energy policies for energy efficiency have a positive effect on residential properties with high energy efficiency certificates (e.g., A, B, and B−) and residential properties with low energy efficiency certificates (e.g., C, D, E, and F); the fiscal and financial incentive policies for energy efficiency have a positive effect on residential properties with high energy efficiency certificates (e.g., A+, A, and B) and a negative effect on residential properties with B− energy certificate, and also a negative effect on residential properties with low energy efficiency certificates (e.g., C and D) and a positive effect on residential properties with an F energy certificate; the information and education policies of energy efficiency have a positive effect on residential properties with high energy efficiency certificates (e.g., A+, A, and B) and residential properties with low energy efficiency certificates (e.g., C, D, and E); and, finally, the consumer credit per capita has a positive effect on residential properties with high energy efficiency certificates (e.g., A+, A, and B) and a negative effect on residential properties with low energy efficiency certificates (e.g., C, D, and F), as well as a positive effect on residential properties with an F energy certificate.

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This investigation explored the impacts of energy transition and brain drain on carbon dioxide (CO2) emissions. A panel of seventy-five countries from 2006 to 2020 and the panel quantile regression were used to realize this investigation. The empirical results from the panel quantile regression indicated that the brain drain, trade openness, and economic growth increase CO2 emissions per capita. At the same time, the energy transition, energy efficiency, and urbanization mitigate the environmental degradation in this group of countries. Moreover, the Dumitrescu-Hurlin panel causality test indicated the presence of unidirectional causality from brain drain to CO2 emissions. The same test also suggests that the brain drains at all levels except 75th has positive and expressive effects on CO2 emissions -mainly in quantiles 10th and 25th, and the energy transition at all levels decreases CO2 emissions, being this effect more intense as quantiles levels up. This research contributes to the literature twofold. First, the study contributes to the literature by finding that brain drain provokes environmental degradation, which is more pronounced when CO2 emissions per capita are low. Second, this analysis assesses the impact of brain drain and energy transition on CO2 emissions of countries with similar convergence patterns. Indeed, it has the novelty of using criteria to include the countries in the panel. This criterion selects the countries by identifying which are more homogeneous and thus reduces the noise caused by divergent countries in the panel. Therefore, this research also opens the door to exploring energy transition based on countries with similar convergence patterns.