Global energy demand increases overtime, especially in emerging market economies, producing potential negative environmental impacts, particularly on the long term, on nature and climate changes. Promoting renewables is a robust policy action in world energy-based economies. This study examines if an increase in renewables production has a positive effect on the Brazilian economy, partially offsetting the SARS-CoV2 outbreak recession. Using data on Brazilian economy, we test the contribution of renewables on the economy via a ML architecture (through a LSTM model). Empirical findings show that an ever-greater use of renewables may sustain the economic growth recovery, generating a better performing GDP acceleration vs. other energy variables.

This paper examines the relationship between renewable energy consumption and economic growth in Brazil, in the Covid-19 pandemic. Using an Artificial Neural Networks (ANNs) experiment in Machine Learning, we tried to verify if a more intensive use of renewable energy could generate a positive GDP acceleration in Brazil. This acceleration could offset the harmful effects of the Covid-19 global pandemic. Empirical findings show that an ever-greater use of renewable energies may sustain the economic growth process. In fact, through a model of ANNs, we highlighted how an increasing consumption of renewable energies triggers an acceleration of the GDP compared to other energy variables considered in the model.

Among the Sustainable Development Goals, ‘Green Issues’ have attracted significant research on sustainability transitions and regional diversification. The introduction of green environmental technologies within the frame of the Fourth Industrial Revolution is crucial for the diversification of local, sustainable activities to protect the environment against negative climate changes. The present paper provides evidence of the positive correlation among green activities if, and only if, green culture and capabilities are robust and exist. Close international coordination is needed. We point out that smart energy-designed systems are a real revolution in the post-industrial society dominated by the service sectors. Therefore, promoting ‘intelligent’ meters is a robust policy action in world energy-based economies. We investigate the policy effects for smart meter rollout in European countries by testing this green policy tool on different economic literature strands. A theoretical model is introduced, showing that a sustainable and efficient policy instrument will reinforce and develop local green culture. The spatial unit of investigation is the EU-28, and it verifies the effectiveness of smart meters as a valid post-industrial design tool toward more sustainable environmental policies.

This research aims to analyze the determinants of subjective well-being in the Italian regions. To this extent, a new ISTAT-BES database has been used, with data for the Italian regions between 2004 and 2021. An artificial neural network (ANN) experiment was conducted to explore the link among these variables. Empirical findings show that subjective well-being is positively associated with education, income, and social relations. Our results align with those provided by past studies on the determinants of subjective well-being. These results imply that governments should improve subjective well-being by increasing the level of investment in education, deepening economic growth, reducing income inequality, and promoting social relations.