Achieving Net Zero in Energy-Intensive Industries Using AI Applications for Greenhouse Gas Reduction: A Quantitative Analysis - Saggar, Atul (Digital Repository of Theses, 2024)
Through a detailed study of recent literature, this paper describes key areas where AI technology may be applied to maximize resource use, increase process efficiency, and eventually minimize emissions. Several AI-enabled scenarios are modelled in the quantitative study, considering various industrial contexts' energy consumption, output, and emissions. The study takes advantage of SPSS and Microsoft Excel as the key data analysis tool to evaluate the data provided from 16 different companies. This study evaluates the outcomes of several scenarios to determine the potential greenhouse gas reductions that AI applications could attain. The study's findings provide significant insight into the viability and outcomes of AI-driven interventions in energy-intensive industries. The study explains the benefits of such interventions from an economic and environmental perspective, in addition to highlighting the need to use AI technology as a critical step toward net-zero emissions. Moreover, this study contributes to the growing corpus of knowledge on sustainable industrial practices by offering policymakers, industry stakeholders, and researchers a rigorous evaluation approach. Our research highlights the potential of transformative AI applications to address the pressing issue of greenhouse gas emissions in energy-intensive sectors, as we conclude. In order to understand the potential of AI-driven approaches in achieving net-zero objectives, promoting sustainable growth, and ultimately clearing the way for a more environmentally friendly and sustainable industrial landscape, the quantitative study provides a data-driven framework.