Presenter: Francesco Barilli from EMPA (SG)
INTENDED AUDIENCE
This presentation is intended for any kind of researcher interested in Life Cycle Assessment (LCA), knowledge formalisation, and interactive software tools. No prior knowledge of LCA is required. It may also be of interest to researchers exploring how AI can support the rapid development of prototype web-based applications.
MAIN CONTRIBUTION<br/ Life Cycle Assessment is a methodology used to evaluate the potential environmental impacts of products, technologies, and services. A complete LCA consists of four phases: Goal and Scope definition, Life Cycle Inventory Modelling and Computation, Impact Assessment, and Interpretation. The current focus of EMPA Life Cycle Assessment Support Centre (ELSC) is to help researchers with the implementation of the first phase, where they must define the scope, assumptions, and methodological choices that will influence the entire study and the next phases, a task that typically requires support from LCA experts.
ELSC thus created a web-based support tool that translates this expertise into an adaptive questionnaire, where previous answers influence subsequent questions . Through this interactive questionnaire, researchers are guided in a simple manner through the key information needed to define the study, including the purpose of the study, system boundaries, assumptions, and other methodological choices. So, the project explores how methodological guidance and expert knowledge in LCA can be translated into an adaptive questionnaire, bridging the gap between researchers with expertise in their own domains and the LCA expertise required to conduct transparent, reliable, and standards-compliant studies.
A second contribution of ELSC concerns the development of the interactive questionnaire itself. Indeed, we can show how AI-assisted development enabled a researcher with limited web-development experience to rapidly transform an Excel-based prototype of the LCA questionnaire into a maintainable and interactive web application that can be tested and iteratively improved with researchers.
WHAT LISTENERS WILL LEARN<br/ Listeners will see a live demonstration of the dynamic questionnaire and how it works through a case study. The demonstration will also explain how ELSC aims to support researchers during the Goal and Scope definition of an LCA by making methodological guidance more accessible through the questionnaire. They will also gain insight into how AI-assisted development can help domain scientists move from spreadsheets and static documents to web-based research software, accelerating prototyping, testing, and the rapid development of ideas that might otherwise require substantially more time and resources.