Multicriteria Decision Analysis (MCDA) is increasingly applied in Chemical Alternatives Assessment (CAA), with multiple methodological approaches tested, including MAUT/MAVT, TOPSIS, ELECTRE, and AHP.
MCDA provides structured and transparent decision support, allowing decision-makers to analyse trade-offs between chemical hazards, environmental impact, human health, and economic feasibility.
MCDA can efficiently guide the selection of chemical alternatives for further testing and development, especially when a large number of chemical alternatives are considered.
In silico data generated with Quantitative Structure–Activity Relationship (QSAR) models are increasingly used in MCDA for CAA, highlighting the need for methods, such as fuzzy logic, to appropriately handle the uncertainty and vagueness inherent in these predictions.
This article reviews the application of multicriteria decision analysis (MCDA) in chemical alternatives assessment (CAA) and presents an overview of how the methodology has been applied within CAA. The study aimed to identify research that uses MCDA to identify the most harmful or least problematic chemicals and evaluate its current use in CAA. The study supports the Partnership for the Assessment of Risks from Chemicals (PARC) in developing a toolbox for safe and sustainable by design (SSbD). 520 studies were analysed, and 21 studies were included. Although MCDA in CAA is still emerging, it shows growth potential in decision analysis and chemical alternatives assessment. The reviewed studies cover various CAA applications and methodological approaches. Multiattribute utility theory (MAUT) is the most often used, followed by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), ÉLimination Et Choix Traduisant la REalité (ELECTRE), and analytic hierarchy process (AHP). Experimental data and in silico data have been used with roughly equal frequency as input data. Group decision-making involving stakeholders with conflicting interests is rarely addressed, with parameter weighting and problem structuring usually handled by authors, sometimes with expert input. Another little discussed topic is the use of external normalisation of input data. In silico generated predictions on chemical alternatives’ properties come with varying degrees of uncertainty, remaining an issue in CAA with MCDA.