Artificial Intelligence for Advancing LCA and EMS: A Comparative Review of Technologies and Integration Practices - Weerasinghe, Shani (2025)
This systematic literature review investigates how artificial intelligence enhances environmental management systems and life cycle assessment practices. By ana lysing 436 coded concepts from ninety-eight peer-reviewed studies published be tween 2003 and 2025, the study compares the effectiveness of AI techniques and identifies best practices for integration into sustainability frameworks. Four key Application dimensions are revealed: data management and standardization, explainable AI implementation, multi-objective optimization, and semantic model ling frameworks. The findings show that neural networks offer the highest predictive accuracy yet. face limitations in transparency and interpretability. In contrast, ensemble meth ods such as Random Forest provide a balanced approach, delivering impressive performance while supporting explainable, regulator-friendly outcomes. Key success cess factors include data harmonization strategies that reduce uncertainty by 31%, the use of explainable AI tools to ensure traceable audit trails for regulatory compliance pliance, and performance validation techniques achieving over 95% model accuracy racy with real-time processing capabilities. Furthermore, strategic optimization methods such as NSGA-II and reinforcement learning to demonstrate measurable environmental and economic benefits, including ing up to 16% greenhouse gas reduction and 43% cost savings. This study provides the first comprehensive, evidence-based synthesis of AI-enhanced EMS and LCA applications, offering actionable guidance for implementation, particularly in alignment with Corporate Sustainability Reporting Directive (CSRD), require ments.