Extending social life cycle assessment to capture technological change
A case study in AI-driven laser manufacturing for the aerospace sector
Bibliographic Data
| ID | 22355409 |
|---|---|
| Authors | Ricardo Mejía-Marchena, Ricardo Mejía (0000-0001-9529-4029, Universidad del Norte), José Osorio-Tejada (University of Warwick), Alejandro Domínguez-Núñez (Center for Advanced Aerospace Technologies), Volker Hessel (0000-0002-9494-1519, University of Warwick, corresponding author) |
| Year | 2026 |
| Volume | 22 |
| Pages | 100448 |
| Publication date | 2026-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Cleaner and Responsible Consumption (JOURNAL) |
| Journal identifiers | ISSN: 2666-7843 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.clrc.2026.100448 |
| OpenAlex | W7161405528 |
| Language | EN |
| References cited | 25 |
This study addresses a limitation in Social Life Cycle Assessment (S-LCA): its reliance on value chain actors, which limits the detection of social impacts from technological changes in the core processes when supply chains are largely intact. We propose an improved approach capable of evaluating emerging technologies and their interactions with social systems. This is tested in the aeronautics sector as the first S-LCA of an industrial system integrating ultrashort-pulsed laser (USPL) technology, Artificial Intelligence (AI), and robotics. The approach aligns with the UNEP, 2009 Guidelines and ISO 14075:2024, integrating multi-level inventory data. A new Hotspot approach identifies relevant subcategories. The impact assessment uses benchmark-based scoring with stakeholder weighting, and sensitivity analyses test the robustness of conditional scoring adjustments ( a -values) and stage weightings. Under the defined scenarios, the AI-driven USPL process achieved higher social performance scores than the manual sanding in 14 of 21 social subcategories. The conditional scoring adjustments reflect the social implications, though they are based on evidence-informed assumptions; consistent results across weighting scenarios strengthen the findings. The improved S-LCA framework captures internal shifts and acts as a proactive planning and decision-support tool. It shows how emerging technologies support socially responsible industrial transitions and stakeholder-informed decisions
Aerospace · Life-cycle assessment · Manufacturing sector · Product life-cycle management · Social life · Additive Manufacturing Materials and Processes · Digital Transformation in Industry · Economic and Technological Innovation
| Citation velocity | historical |
|---|---|
| Highly cited | No |