The impact of renewable energy consumption on reducing the outdoor air pollution death rate, in nineteen Latin America & the Caribbean countries, from 1990 to 2016, using the econometric technique of Quantile Regression for Panel Data, was researched. Results show that economic growth, and fossil fuel consumption, are positively related to CO2 emissions, while renewable energy consumption bears a negative relationship with it. Furthermore, fossil fuel consumption has a positive impact on the mortality rate and economic growth a negative one. The negative effect of renewable energy consumption on the mortality rate is only observable on the right tail of its distribution. The modelisation reveals two ways in which the consumption of renewable energy can reduce the outdoor air pollution death rates: (i) directly, by increasing renewable energies; and (i) indirectly because the increase in the consumption of renewable energies implies a decrease in the consumption of energy from fossil fuels. The phenomenon of increasing urbanisation is a point where the action of public policymakers is decisive for the reduction of outdoor air pollution death rates. Here, the question is not to reduce the level of urbanisation but to act on the "quality" of urbanisation, to make cities healthier. The research concludes that public policymakers must focus on intensifying the transition from fossil to renewable energies and improving the quality of cities.

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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