Combining risk assessment, life cycle assessment, and multi-criteria decision analysis to estimate environmental aspects in environmental management system

The rating of environmental aspects plays a central role in the ISO 14001 environmental management system (EMS) and EU Eco-Management and Audit Scheme because it is the basis for the identification of an organization's environmental targets. The existing methods for the assessment of environmental aspects are grouped into three categories: risk assessment-based (RA-based), life cycle assessment (LCA)-based, and criterion-based methods. The first category accurately determines abnormal, or accidental aspects, as well as the probabilistic causality of aspect-pathway-receptor-impact relationships, but when evaluating environmental impact, it cannot provide a sound theoretical basis. The second category provides a theoretical foundation for the assessment of environmental impact, due to LCA, but cannot adequately represent the probabilistic aspect-pathway-receptor-impact relationship. The third category puts emphasis on the significance criteria, but the scoring methods are too simple. To combine the benefits of these three categories of research, this study proposes an integrated framework, combining RA-, LCA-, and criterion-based methods. The integrated framework incorporates LCA techniques for the identification of the causal linkage for aspect-pathway-receptor-impact, uses fuzzy logic to assess aspects, considers fuzzy conditions, in likelihood assessment, and employs a new multi-criteria decision analysis method-multi-criteria and multi-connection comprehensive assessment (MMCA)-to estimate significant aspects in EMS. The proposed model is verified, using a real case study-a waste-recycling factory. The results show that this method successfully prioritizes the environmental aspects. Compared with criterion-based methods, the case study demonstrates that the proposed method provides a more solid theoretical basis. This study integrates RA, LCA, and MMCA, to assess environmental aspects. The method identifies the probabilistic causality of aspect-pathway-receptor-impact relationships, enhances the theoretical foundations, and strengthens decision-making.