2025
Blanco, Carlos Felipe; Behrens, Paul; Vijver, Martina; Peijnenburg, Willie; Quik, Joris; Cucurachi, Stefano
A framework for guiding safe and sustainable-by-design innovation Journal Article
In: Journal of Industrial Ecology, pp. 47–65, 2025, ISSN: 15309290.
Abstract | Links | BibTeX | Tags: emerging technologies, innovation, prospective life cycle assessment, risk assessment, SSbD, uncertainty
@article{Blanco2025,
title = {A framework for guiding safe and sustainable-by-design innovation},
author = {Carlos Felipe Blanco and Paul Behrens and Martina Vijver and Willie Peijnenburg and Joris Quik and Stefano Cucurachi},
doi = {10.1111/jiec.13609},
issn = {15309290},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Journal of Industrial Ecology},
pages = {47–65},
abstract = {Assessing the safety and sustainability of novel technologies while they are still in the early research and development stages is the most effective way to avoid undesired outcomes. However, the journey from idea to market is highly uncertain and involves intensive trial and error as technology developers attempt to optimize material choices and product configurations. Designs evolve quickly, and assessing their risks and impacts while numerous factors remain undetermined is challenging. The standard practice is to evaluate a limited subset of scenarios that can guide design choices. However, selecting scenarios from hundreds of undetermined factors without a systematic sensitivity screening may leave out important improvement opportunities. To provide well-informed guidance, the evaluated scenarios should be selected based on factors that are most influential to the safety and sustainability impacts of the technology. We propose an approach that accomplishes this by incorporating a wide spectrum of undetermined factors, both intrinsic and extrinsic to the technology design. The assessment models are then screened for highly-sensitive factors using global sensitivity analysis. Strategies to reduce uncertainty on highly influential factors are proposed for subsequent iterations, and the residual factors for which uncertainty cannot be further reduced yet remain influential are selected as a basis for proposed “sensitive scenarios” and improvement roadmaps. We demonstrate the framework with an emerging photovoltaics case study. Over a hundred uncertain factors are reduced to less than five which, if optimized, would substantially improve the future safety and sustainability performance of the technology as well as reduce the uncertainty around it.},
keywords = {emerging technologies, innovation, prospective life cycle assessment, risk assessment, SSbD, uncertainty},
pubstate = {published},
tppubtype = {article}
}
Assessing the safety and sustainability of novel technologies while they are still in the early research and development stages is the most effective way to avoid undesired outcomes. However, the journey from idea to market is highly uncertain and involves intensive trial and error as technology developers attempt to optimize material choices and product configurations. Designs evolve quickly, and assessing their risks and impacts while numerous factors remain undetermined is challenging. The standard practice is to evaluate a limited subset of scenarios that can guide design choices. However, selecting scenarios from hundreds of undetermined factors without a systematic sensitivity screening may leave out important improvement opportunities. To provide well-informed guidance, the evaluated scenarios should be selected based on factors that are most influential to the safety and sustainability impacts of the technology. We propose an approach that accomplishes this by incorporating a wide spectrum of undetermined factors, both intrinsic and extrinsic to the technology design. The assessment models are then screened for highly-sensitive factors using global sensitivity analysis. Strategies to reduce uncertainty on highly influential factors are proposed for subsequent iterations, and the residual factors for which uncertainty cannot be further reduced yet remain influential are selected as a basis for proposed “sensitive scenarios” and improvement roadmaps. We demonstrate the framework with an emerging photovoltaics case study. Over a hundred uncertain factors are reduced to less than five which, if optimized, would substantially improve the future safety and sustainability performance of the technology as well as reduce the uncertainty around it.