Multiple criteria decision analysis to support the design of safe and sustainable chemicals and materials.

The development of safe and sustainable chemicals and materials is essential to achieve the Zero-Pollution Ambition for a Toxic Free Environment stated in the EU Green Deal. For that, criteria need to be defined and considered since early stage of development. A Safe and Sustainable by Design (SSbD) framework is proposed in an EU Recommendation suggesting the assessment of multiple safety and sustainability aspects of chemicals and materials leaving open how the evaluation and selection of the preferable option should be done. This paper presents a proposal with different options for the use of multiattribute aggregation in an evaluation procedure for the SSbD assessment of chemicals and materials. This proposal is based on i) a review of the literature focusing on Multi-Criteria Decision Analysis (MCDA) application in the SSbD context (i.e. applications considering simultaneously safety and sustainability attributes) and ii) the definition of requisites for MCDA to be applied to the SSBD framework. In the latter, an absolute rather than a relative assessment is preferred as it should be possible for an organization developing a new chemical or material to assess if it is SSbD, without needing to obtain data on all of its possible competitors. Moreover, rank-reversals caused by the introduction of other options are avoided, i.e., assessments of one alternative that depends on other alternatives being assessed simultaneously are not the most adequate. Different options for the aggregation of attributes at different levels are discussed as well as for the consideration of data quality in the evaluation procedure. Regardless the approach selected, the use of multiattribute aggregation does not rule out a richer dashboard presenting not only the overall aggregate result, but also the results obtained in other levels of the hierarchy. Such complementary information is important to understand the strengths and weaknesses that an aggregate result might hide.