Decision support for selection of new materials considering socio-economic and broader environmental aspects

Abstract

A growing awareness of the inclusion of aspects that go beyond direct environmental and health risks has characterized the evaluation of new technologies in recent years. Broader environmental and socio-economic aspects are now seen as relevant factors for the sustainable success of corresponding products. As a contribution to the realization of this mindful perspective, in a collaborative project on risk governance in nanotechnology (EU Horizon 2020 project NANORIGO), a set of criteria to support decision-making in the selection of materials at early developmental stages has been developed and integrated into a stochastic multicriteria acceptance analysis. The main categories of criteria and an optional set of corresponding weights were selected based on surveys of project partners and stakeholders. The criteria cover both broader environmental and socioeconomic aspects and are differentiated by the life cycle stages of nanomaterials and potential alternative materials. We identified and weighted 24 distinctive criteria for broader environmental aspects and 32 distinctive criteria for socio-economic aspects, with the help of a total of 62 survey participants. In order to address the level of knowledge of both laypersons and experts, the input questionnaire is divided into a tiered structure. Instead of a fully quantitative approach based on native units, semiquantitative scales are used to assess the input information. This structure aims to avoid the knowledge requirements and resources needed for a full life cycle analysis in terms of environmental or socio-economic impacts. To demonstrate its applicability for decision support, we applied the method to two case studies. Partners of the project tested our survey on cases like carbon nanofibers in car parts and nanomaterials in tire rubbers. The used method makes it possible to deal with uncertainty and ignorance in the early stages of development and still compute a comparison of the given alternatives. In this way, it can be combined with existing tools for risk assessment of (nano)materials to achieve a comprehensive prospective characterization of potential material options at an early stage of the innovation process.