By 2025, Belgium will phase-out nuclear power. Unassessed so far, this policy reform may modify t... more By 2025, Belgium will phase-out nuclear power. Unassessed so far, this policy reform may modify the economic and environmental channels through which energy and society interfere in this country. In this paper, we investigate whether this structural energy change may adversely impact the growth of the Belgian economy (i) and its ability to meet its long-term greenhouse gas emission targets (ii). A multivariate model comprising production factors (labor, capital, and exports), nuclear and renewable energy uses, total primary energy supply, economic growth, and CO 2 emissions from the power and heating sector is combined with real time-series data spanning the 1974-2019 period. The analysis consists in sequentially assessing two distinct nexuses (energyeconomy and energy-economy-environment) over reduced-and augmented frameworks (excluding and including nuclear energy), and through a two-stage empirical strategy: time-series econometric estimations (Toda-Yamamoto causality test, Impulse Response Functions (IRFs), and the Auto-Regressive Distributed Lags (ARDL) and Machine Learning (ML) experiments with a Partial Differential Equations (PDEs) algorithm. For robustness purposes, we conduct two seminal tests which relate to dynamic predictive processes (T-Mat and Verticality tests). Besides confirming the time-series findings, our ML results highlight the necessity to timely manage the process of nuclear phase-out, along with a progressive deployment of installed renewable energy capacity. This should avoid additional economic costs, energy security threats, and undermining of climate targets. In doing so, this study combines macro-level nexus investigations with the politics and institutional determinants of nuclear energy reliance and seeks to bring inclusive knowledge on this topic.
The coronavirus disease 2019 (COVID-19), with new variants, continues to be a constant pandemic t... more The coronavirus disease 2019 (COVID-19), with new variants, continues to be a constant pandemic threat that is generating socioeconomic and health issues in manifold countries. The principal goal of this study is to develop a machine learning experiment to assess the effects of vaccination on the fatality rate of the COVID-19 pandemic. Data from 192 countries are analysed to explain the phenomena under study. This new algorithm selected two targets: the number of deaths and the fatality rate. Results suggest that, based on the respective vaccination plan, the turnout in the participation in the vaccination campaign, and the doses administered, countries under study suddenly have a reduction in the fatality rate of COVID-19 precisely at the point where the cut effect is generated in the neural network. This result is significant for the international scientific community. It would demonstrate the effective impact of the vaccination campaign on the fatality rate of COVID-19, whatever the country considered. In fact, once the vaccination has started (for vaccines that require a booster, we refer to at least the first dose), the antibody response of people seems to prevent the probability of death related to COVID-19. In short, at a certain point, the fatality rate collapses with increasing doses administered. All these results here can help decisions of policymakers to prepare optimal strategies, based on effective vaccination plans, to lessen the negative effects of the COVID-19 pandemic crisis in socioeconomic and health systems.
This paper proposes a novel approach to identify the presence of a latent factor in the co-moveme... more This paper proposes a novel approach to identify the presence of a latent factor in the co-movements of gasoline and diesel prices in the three major European Union economies, (France, Germany, and Italy) using daily data from January 3, 2005, to June 28, 2021. More precisely, we advance an artificial neural networks algorithm estimated through a machine learning experiment through the backpropagation system to show that the neural signal is altered by an element that could coincide with a latent factor in the fuel price co-movements. We consider the role of the fuel tax systems and the connection between gasoline and diesel prices in these countries. The estimations indicate the presence of an unobservable component (the latent factor) in the fuel price co-movements, capable of influencing NN. This result validates the previous findings B Cosimo Magazzino
This research aims to analyze the determinants of subjective well-being in the Italian regions. T... more 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.
By 2025, Belgium will phase-out nuclear power. Unassessed so far, this policy reform may modify t... more By 2025, Belgium will phase-out nuclear power. Unassessed so far, this policy reform may modify the economic and environmental channels through which energy and society interfere in this country. In this paper, we investigate whether this structural energy change may adversely impact the growth of the Belgian economy (i) and its ability to meet its long-term greenhouse gas emission targets (ii). A multivariate model comprising production factors (labor, capital, and exports), nuclear and renewable energy uses, total primary energy supply, economic growth, and CO 2 emissions from the power and heating sector is combined with real time-series data spanning the 1974-2019 period. The analysis consists in sequentially assessing two distinct nexuses (energyeconomy and energy-economy-environment) over reduced-and augmented frameworks (excluding and including nuclear energy), and through a two-stage empirical strategy: time-series econometric estimations (Toda-Yamamoto causality test, Impulse Response Functions (IRFs), and the Auto-Regressive Distributed Lags (ARDL) and Machine Learning (ML) experiments with a Partial Differential Equations (PDEs) algorithm. For robustness purposes, we conduct two seminal tests which relate to dynamic predictive processes (T-Mat and Verticality tests). Besides confirming the time-series findings, our ML results highlight the necessity to timely manage the process of nuclear phase-out, along with a progressive deployment of installed renewable energy capacity. This should avoid additional economic costs, energy security threats, and undermining of climate targets. In doing so, this study combines macro-level nexus investigations with the politics and institutional determinants of nuclear energy reliance and seeks to bring inclusive knowledge on this topic.
The coronavirus disease 2019 (COVID-19), with new variants, continues to be a constant pandemic t... more The coronavirus disease 2019 (COVID-19), with new variants, continues to be a constant pandemic threat that is generating socioeconomic and health issues in manifold countries. The principal goal of this study is to develop a machine learning experiment to assess the effects of vaccination on the fatality rate of the COVID-19 pandemic. Data from 192 countries are analysed to explain the phenomena under study. This new algorithm selected two targets: the number of deaths and the fatality rate. Results suggest that, based on the respective vaccination plan, the turnout in the participation in the vaccination campaign, and the doses administered, countries under study suddenly have a reduction in the fatality rate of COVID-19 precisely at the point where the cut effect is generated in the neural network. This result is significant for the international scientific community. It would demonstrate the effective impact of the vaccination campaign on the fatality rate of COVID-19, whatever the country considered. In fact, once the vaccination has started (for vaccines that require a booster, we refer to at least the first dose), the antibody response of people seems to prevent the probability of death related to COVID-19. In short, at a certain point, the fatality rate collapses with increasing doses administered. All these results here can help decisions of policymakers to prepare optimal strategies, based on effective vaccination plans, to lessen the negative effects of the COVID-19 pandemic crisis in socioeconomic and health systems.
This paper proposes a novel approach to identify the presence of a latent factor in the co-moveme... more This paper proposes a novel approach to identify the presence of a latent factor in the co-movements of gasoline and diesel prices in the three major European Union economies, (France, Germany, and Italy) using daily data from January 3, 2005, to June 28, 2021. More precisely, we advance an artificial neural networks algorithm estimated through a machine learning experiment through the backpropagation system to show that the neural signal is altered by an element that could coincide with a latent factor in the fuel price co-movements. We consider the role of the fuel tax systems and the connection between gasoline and diesel prices in these countries. The estimations indicate the presence of an unobservable component (the latent factor) in the fuel price co-movements, capable of influencing NN. This result validates the previous findings B Cosimo Magazzino
This research aims to analyze the determinants of subjective well-being in the Italian regions. T... more 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.
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