AI shortens time taken to measure the sustainability impact of a product

Rather than starting from zero, the streamlined approach draws on prior studies and databases to identify the components most likely to drive the environmental impact of a product. These major contributors are then modelled in 3D to automatically extract their weight and volume. AI systems assist by assigning typical manufacturing processes and selecting appropriate data from repositories, such as Ecoinvent. The resulting assessment requires far fewer inputs, can be completed in a fraction of the time, and still provides a reliable picture of environmental hotspots.

“SLCA builds on prior knowledge to understand what matters most, instead of demanding every last detail. It uses 3D modelling to derive basic part characteristics and AI to match them with the most likely processes and materials,” added Assoc Prof Silva.

To validate the method, the team tested it on a case study of a small electronic hearing aid. A traditional full LCA of the device took three months and required 86 separate data inputs. By contrast, the SLCA took one week and used only 26 inputs, cutting input requirements by nearly 70 percent and time by over 90 percent. The streamlined results matched the full assessment with an average accuracy of 90 percent.

According to Assoc Prof Silva, this balance is key: “We ensured that the full LCA served as our ‘ground truth’. What we found is that a huge saving in time spent leads to only a minimal deviation in results—beyond a certain point, more effort does not translate into much greater accuracy.”

With SLCA, designers could test alternative concepts rapidly and iteratively, identifying which materials or processes are most environmentally burdensome before committing to them. Industries where products evolve quickly, such as consumer electronics or wearables, could benefit most immediately, while other sectors may adapt the method to their own contexts.

“Our approach is especially suited for early-stage design, where uncertainty is high. It enables teams to spot hotspots without waiting for every specification to be finalised, avoiding surprises later when a full LCA shows the impact is higher than intended,” explained Assoc Prof Silva.

Looking ahead, the research team plans to extend testing to more product types and refine the approach to make it more user-friendly. They also see opportunities to explore how AI might continue to evolve in this space, balancing automation with transparency. Ultimately, the goal is to make environmental impact assessment part of routine design practice rather than a mere afterthought.

“Right now, LCA is extremely difficult to integrate at the design stage—it is usually done when it is too late to do something about it,” said Assoc Prof Silva. “We hope this work contributes to embedding sustainability into design from the very start, where it can make the biggest difference.”