Environmental and Socio-Economic Impact Assessment of Renewable Energy Using Machine Learning Models

Renewable energy sources, such as solar, hydro, wind, and geothermal energy, have emerged as key alternatives to fossil fuels in combating climate change and addressing energy security concerns in the USA and ad worldwide. Strategic use of this renewable resource is important not only for carbon emission reduction and improvement of environmental sustainability but also for maintaining future energy supplies. At the same time, such transition raises thorough assessments of environmental and socio-economic impacts. Machine learning (ML) models offer a powerful tool for predicting and analyzing such impacts, allowing for more efficient decision-making and long-term planning. These models are supposed to analyze patterns in energy production, land use, and emissions to make a more dynamic and predictive understanding of how renewable energy adoption influences CO2 levels. The principal aim of this research project was to develop and curate machine learning algorithms for predicting CO2 emissions based on renewable energy data, using the knowledge to better understand how solar, wind, hydro, and geothermal energy systems affect environmental outcomes. The predictive models developed in this research would serve as useful tools for the policymakers and major stakeholders in decision-making on investments in energy infrastructure and characterization of regulatory frameworks. These datasets for this research project were retrieved from several prominent institutions, including governmental agencies, international organizations such as the International Energy Agency-IEA and the World Bank, satellite data repositories, and USA environmental monitoring agencies. For this research project, 3 machine learning algorithms in the experiment were used, namely Logistic Regression, XG-Boost, and Random Forest. Amongst these three, the linear regression model gave the best performance, as it had the least MSE; indicating that its predictive capability was impressive. The comparative analysis of renewable energy projects in Germany, China, and California underlines that effective policy-making plays a very decisive role in the transition toward sustainable energy.