Abstract
Current EU Strategies aim to rapidly advance the research, development and deployment of innovative advanced materials and chemicals to make Europe the first digitally enabled circular, climate-neutral and sustainable economy. To achieve this, an underlying adaptation of the research and innovation (R&I) process to the Safety-and-Sustainability-by-Design (SSbD) framework has been proposed. This perspective article provides an overview of already existing approaches providing guidance for implementing SSbD-like procedures in R&I in several different industrial sectors to ultimately replace substances of concern (SoC). Starting from the ECHA's Assessment of Alternatives (AoA) approach we put emphasis on the scoping phase during which the requirements for replacement will be identified. The limitations for the changes possible and trade-offs acceptable for the company need to be defined, in agreement with relevant stakeholders to be further involved in AoA scoping (e.g. for setting the trade-off levels). This includes listing the SSbD-relevant aspects in the different categories (i.e. functional performance, health, environment, social, and economic sustainability) in a customized manner, followed by weighting them in relation to their expected impact on the intended SSbD-guided multi-objective optimization procedure. An additional dimension is provided as to how to deal with uncertainties (e.g. data gaps or compromises in data quality, or which assessment methods and tools to employ); notably, it represents the company's own decision to herewith set the requirements and goals for replacement, and this can be done at different levels, such as the material or chemical itself, changes in the production processes, or within the entire system of a product's life cycle spanning across its entire value chain(s), which can be documented employing the use maps concept. Further, this article builds on the product life cycle and provides a general understanding of life cycle assessment (LCA) methodology, especially a deeper insight into prospective and anticipatory LCA, that will need to prove functional on real-life case studies from industry. Besides a clarification of these concepts, the article provides an interdisciplinary view, as required for implementing SSbD in small and medium-sized enterprises, with hints on the use of machine learning techniques for anticipatory LCA of new chemicals, materials, and products. Such methodologies will, in future, help extend classical LCA cases towards the data-scarce requirements of earlier material and product development stages.