Track: Environmental Engineering

Abstract

Life cycle assessment (LCA) is commonly used to measure product and process environmental impacts. Nevertheless, its effectiveness is restricted by static data inputs and assumptions of steady-state conditions, limiting its ability to forecast future impact changes and neglecting dynamic issues within the system. System dynamics (SD) uses simulations to model the causal interactions between internal and external factors within complex systems. Despite the potential benefits of combining both models, the best practices for integrating LCA and SD models for optimal effectiveness are still under development. Therefore, combining LCA with SD improves the ability to model complex interactions, feedback loops and realistic impact assessments, leading to more reliable results for sustainability practices. This paper investigates integrating LCA and SD methodologies to enhance the understanding and assessing sustainability issues. Two integration approaches, Type-A and Type-B, are explored, where LCA guides SD models or SD predicts conditions for use in LCA, respectively. The study showcases the application and implementation of the LCA-SD model in sustainability evaluations, emphasizing the dynamic nature of systems and the need for more accurate and comprehensive modeling. Discussions investigate the intricacies of SD modeling using LCA analysis (Type-A Integration) and LCA modeling using SD prediction analysis (Type-B Integration), offering examples from various sectors like cement production, grain systems, and solar PV adoption. The paper highlights the benefits of the LCA-SD model, including proactive forecasting, improved understanding of complex systems, and comprehensive assessment of sustainability performance. Furthermore, it underscores the compatibility of LCA-SD with current sustainability frameworks and its ability to identify trade-offs between environmental impacts and economic objectives. However, limitations such as data constraints and the need for continuous improvement are acknowledged. In conclusion, the paper advocates for future research to focus on developing integrated proposals, addressing data limitations, and optimizing mitigation strategies for improved effectiveness in sustainable practice.