Graduate Student University of Notre Dame, Indiana, United States
Abstract Submission: This research introduces a dynamic Life Cycle Assessment (LCA) methodology designed to provide a more accurate and adaptable understanding of future water consumption impacts. Traditional LCA models often rely on static data and present-day assumptions, offering limited capacity to forecast long-term environmental consequences. However, future water consumption is expected to be significantly shaped by various interrelated factors, including climate change, population growth, agricultural expansion, urban land development, forecast changes, shifts in power generation, and public policy. These drivers will critically alter water demand and availability yet are inadequately represented in current LCA approaches. The proposed methodology allows users to dynamically adjust critical variables related to these factors, enabling more precise modeling of future water resource impacts. By incorporating evolving climate projections, socio-economic trends, and changes in energy infrastructure, the model provides stakeholders with a more robust framework for evaluating water consumption scenarios. This adaptable approach also facilitates the identification of potential risks and opportunities associated with future water scarcity, ensuring that decision-makers have access to reliable data for sustainable water management strategies. This research aims to bridge the gap between current LCA limitations and the complex, interconnected realities of future water use. By providing a flexible and interactive tool, this methodology enhances the predictive power of LCA models, allowing for more informed decisions in the face of environmental uncertainties and socio-economic transformations.
