In light of the emerging trends within the realm of smart grids, novel prospects for the improvement of sustainability and efficiency within the power domain have become a topical focus. Yet, complications related to the environment and functioning may emerge, necessitating a comprehensive analysis. The identification and effective control of the risk factors within the power production process related to the environment are essential for optimal functioning with minimal harm to nature within more digitized and decentralized power grids. In this paper, a framework for analyzing environmental risk factors in power energy production that specifically evaluates GHG emissions, key drivers for efficiency, and geospatial risk mapping methods has been developed for implementation. Through facility operational data, geospatial analysis, and regression models using machine learning algorithms, a system can identify areas for risk mitigation, develop a sense of where these sources are geographically concentrated, and develop a system-wide plan for decarbonization that produces quantifiable results. The analysis of the data obtained reveals that there is a smooth geospatial integration of generation data from the EPA eGRID database with operational data that shows that heat input (51.4%), net generation (22.6%), capacity (12.4%), and capacity factor (11.7%) variables combine to cover more than 85% of the variation in greenhouse gas emissions from power-generating stations. Modeling of the strategy analysis demonstrates the effectiveness of assessing other options, such as coal-to-gas switches, that could trim greenhouse gas emissions by as much as 60%, coupled with an extra 3% from efficiency improvements
