AUTHOR=Cristea Dragos Sebastian , Zamfir Cristina Gabriela , Simionov Ira Adeline , Fortea Costinela , Ionescu Romeo Victor , Zlati Monica Laura , Antohi Valentin Marian , Munteanu Dan , Petrea S. M. TITLE=Renewable energy strategy analysis in relation to environmental pollution for BRICS, G7, and EU countries by using a machine learning framework and panel data analysis JOURNAL=Frontiers in Environmental Science VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.1005806 DOI=10.3389/fenvs.2022.1005806 ISSN=2296-665X ABSTRACT=

The present research uses machine learning, panel data and time series prediction and forecasting techniques to establish a framework between a series of renewable energy and environmental pollution parameters, considering data for BRICS, G7, and EU countries, which can serve as a tool for optimizing the policy strategy in the sustainable energy production sector. The results indicates that XGBoost model for predicting the renewable energy production capacity reveals the highest feature importance among independent variables is associated with the gas consumption parameter in the case of G7, oil consumption for EU block and GHG emissions for BRICS, respectively. Furthermore, the generalized additive model (GAM) predictions for the EU block reveal the scenario of relatively constant renewable energy capacity if gas consumption increases, while oil consumption increases determine an increase in renewable energy capacity until a kick point, followed by a decrease. The GAM models for G7 revealed the scenario of an upward trend of renewable energy production capacity, as gas consumption increases and renewable energy production capacity decreases while oil consumption increases. In the case of the BRICS geopolitical block, the prediction scenario reveals that, in time, an increase in gas consumption generates an increase in renewable energy production capacity. The PCA emphasizes that renewable energy production capacity and GHG, respectively CO2 emissions, are highly correlated and are integrated into the first component, which explains more than 60% of the variance. The resulting models represent a good prediction capacity and reveal specific peculiarities for each analyzed geopolitical block. The prediction models conclude that the EU economic growth scenario is based on fossil fuel energy sources during the first development stage, followed by a shift to renewable energy sources once it reaches a kick point, during the second development stage. The decrease in renewable energy production capacity when oil consumption increases indicates that fossil fuels are in trend within the G7 economy. In the case of BRICS, it is assumed that gas consumption appears because of increasing the industrial capacity, followed by the increase of economic sustainability, respectively. In addition, the generalized additive models emphasize evolution scenarios with different peculiarities, specific for each analyzed geopolitical block.