Environmental Impact

Research on environmental impact is increasingly focusing on the carbon footprint of computationally intensive technologies, particularly within machine learning and artificial intelligence. Current studies utilize various machine learning algorithms, including Bayesian methods, decision trees, random forests, and neural networks (like MLPs), to analyze environmental data from diverse sources such as satellite imagery, sensor networks, and textual compliance reports. This work aims to quantify and mitigate the environmental consequences of these technologies while simultaneously leveraging them to monitor and address environmental challenges like pollution, waste management, and compliance violations. The findings inform the development of more sustainable AI practices and provide crucial data for environmental policy and management.