The decarbonisation of the transportation sector is crucial to reducing carbon dioxide (CO₂) emissions. This study analyses evidence from European countries regarding achievement of the European Commission’s goal of achieving carbon neutrality by 2050. Using panel quantile econometric techniques, the impact of battery-electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) on CO₂ emissions in twenty-nine European Union (EU) countries from 2010–2020 was researched. The results show that BEVs and PHEVs are capable of mitigating CO₂ emissions. However, each type of technology has a different degree of impact, with BEVs being more suited to minimizing CO₂ emissions than PHEVs. We also found a statistically significant impact of economic development (quantile regression results) and energy consumption in increasing the emissions of CO₂ in the EU countries in model estimates for both BEVs and PHEVs. It should be noted that BEVs face challenges, such as the scarcity of minerals for the production of batteries and the increased demand for mineral batteries, which have significant environmental impacts. Therefore, policymakers should adopt environmentally efficient transport that uses clean energy, such as EVs, to reduce the harmful effects on public health and the environment caused by the indiscriminate use of fossil fuels.

This research aims to answer two fundamental questions of the present time: First, what is the impact of the increasing complexity of economic structures and the production of complex goods on the environment? Second, can increasing export quality lead to the improvement of the environment? Given that the relationship of the ecological footprint and its determinants has been revealed to be nonlinear, the use of the quantile approach is supported. This finding led us to the central hypothesis of this research: economic complexity and export quality first deteriorate the ecological footprint (i.e., in lower quantiles), and the middle and higher quantiles contribute to reducing or mitigating environmental damage. The effect of economic complexity and export quality on the ecological footprint was researched using a two-step approach. First, club convergence was applied to identify the countries that follow a similar convergence path. After this, panel quantile regression was used to determine the explanatory power of economic complexity and export quality on the ecological footprint of 98 countries from 1990 to 2014. The club convergence revealed four convergent groups. Panel quantile regression was used because the relationship between the ecological footprint and its explanatory variables was shown to be nonlinear for the group of countries identified by the club convergence approach. GDP, nonrenewable energy consumption, and the population damage the environment. Urbanisation contributes to reducing the ecological footprint. Export quality and trade openness reduce the ecological footprint, but not at all quantiles. The effect of trade openness mitigating the ecological footprint is lost at the 90th quantile. Export quality becomes a reducer of the ecological footprint in the 50th quantile or above, and in the higher quantiles, its contribution to reducing the footprint is vast. Economic complexity aggravates the ecological footprint in low quantiles (10th), becomes non-statistically significant in the 25th quantile, and reduces the ecological footprint in higher quantiles. Policymakers must identify the impact of the ecological footprint and consider the demand and supply side of economics.

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.