Businesses are facing increasing pressure from both consumers and governments to not only be innovative but also sustainable. The recent introduction of more sustainable reporting guidelines introduced by the EU forces companies to report on their sustainability and to start looking at what their environmental impact is and how they can mitigate it. Figuring out the complex product lifecycle, from the mining of the raw materials to how they get disposed of, can sometimes feel like too big of a mountain to climb, especially for smaller companies. Could this be where AI lends a helping hand?
Lifecycle assessments are one of the most in-depth ways for companies to see the environmental impact of their product. It maps the impact of the entire value chain of a product, a massive amount of work for companies that produce many different products. Many components and raw materials of products come from countries that place less importance on tracking and reporting their environmental footprint. When performing an LCA, this can make it difficult to accurately predict the footprint of a product. AI can use its high predictive accuracy to help fill in these missing values (Romeiko et al., 2024).
Many startups are working on LCA AI technology, one example is Makersite. They have created an AI which easily shows a company’s entire value chain, simply by uploading their bill of materials (Makersite GmbH, 2024a). Big MNOs such as Microsoft are already using the technology to envision their carbon footprint (Makersite GmbH, 2024b).
In my opinion, this technology will be extremely helpful to companies. Across all sectors there is more and more pressure from consumers, governments, NGOs and other stakeholders to become more transparent about their impact. The usage of AI will make it easier for companies to get an understanding of their value chain and where they can make a change. It is one of the AI developments which could truly have a positive impact on the world and has the potential to grow in the future.
References
Makersite GmbH. (2024a, April 2). Automated LCAs with Makersite – Makersite GmbH. https://makersite.io/get-to-net-zero/automated-lcas/
Makersite GmbH. (2024b, June 2). Microsoft’s LCA methodology with Makersite – Makersite GmbH. https://makersite.io/customer-story/microsofts-lca-methodology/
Romeiko, X. X., Zhang, X., Pang, Y., Gao, F., Xu, M., Lin, S., & Babbitt, C. (2024). A review of machine learning applications in life cycle assessment studies. The Science of the Total Environment, 912, 168969. https://doi.org/10.1016/j.scitotenv.2023.168969
