%0 Journal Article
%T AI-Driven Carbon Release Reduction: A Hoochens Inc Framework for Assessing AI¡¯s Impact on Fossil Fuel Intake Optimization
%A Chinedu Egbuonu
%J Open Access Library Journal
%V 13
%N 2
%P 1-22
%@ 2333-9721
%D 2026
%I Open Access Library
%R 10.4236/oalib.1113752
%X This paper presents a novel framework for optimizing Carbon Release (CR) through an AI-driven approach to Fossil Fuel Intake (FFI) management. We propose a new training methodology for AI models to enhance their effectiveness in reducing FFI by learning from historical energy consumption trends and emissions data. By attributing specific quantities to key optimization components such as combustion efficiency, renewable integration, and AI impact, we derive a detailed matrix that quantifies their contributions to carbon reduction. Furthermore, we introduce a method for retroactively calculating the impact of each optimization item on overall CR, offering a data-driven approach to assess the effectiveness of AI interventions. This analysis is crucial for informing energy policy decisions, providing a clear understanding of how AI-based optimization strategies can significantly reduce emissions. Our findings demonstrate the potential of leveraging AI in achieving sustainable energy management, emphasizing the importance of precise quantification in guiding future efforts toward carbon neutrality.<br />
%K AI Attribution
%K Net-Zero
%K Reduction
%K Fossil Fuel
%K Optimization
%K Intake
%U http://www.oalib.com/paper/6865133