Reducing the environmental footprint of global food production is now a major and urgent challenge. Due to its colossal greenhouse gas emissions — around 30% of global emissions — and its massive impact on biodiversity, water and land resources, the agri-food sector is now under increasing pressure from regulators, associations and citizens.
To meet these challenges, Life Cycle Assessment (LCA) is an essential tool for reducing the environmental impact of food products. By taking into account the entire life cycle of a product, from the production of raw materials to the processing and recycling of waste, it enables companies in the food chain to identify the causes and levers for reducing these impacts, and then to implement appropriate action plans.
However, given the incredible variety of food products on the market, manually conducting an LCA for each food product would be a monumental task and, above all, extremely costly. To address this challenge, the use of artificial intelligence (AI) to support LCA can automate and better control the calculations, making LCA much less costly and much more reliable.
At Carbon Maps, we believe that three areas of automation are priorities for LCA.
Calculation across a very large number of SKUs
Firstly, the use of AI applied to LCA allows the mass processing of hundreds of product references, which is necessary for most large industrial companies today. By facilitating the collection and processing of data, as well as its analysis and interpretation, AI makes LCA calculation processes much faster, data collection less laborious and the results easier to control. AI makes LCA accessible to companies of all sizes, including the smallest.
Integrating agricultural impact models into LCAs
The use of AI applied to LCAs also makes it possible to better model the environmental impacts of agricultural activities. These impacts are generally difficult to integrate into traditional manual LCA methods due to the fragmentation of data and the variety of impact models that need to be integrated. Thanks to AI, a wide range of models can be integrated in a systematic way, including, for example, models of impacts on biodiversity or soil health that are not considered in traditional LCA methods, to provide a more holistic and therefore more accurate view of the environmental impacts of food.
Regular updating of LCAs
Finally, the use of AI allows LCAs to be updated much more frequently, on the basis of regularly updated data, thus reducing the time needed to make decisions. In a changing climatic and international context, the ability to have “fresh” LCAs is essential for managing the food transition and optimizing eco-design.
At Carbon Maps, we believe that the automation of LCAs is a priority issue for accelerating the food transition. By democratizing the calculation of more accurate, more reliable and less costly LCAs, it will enable all companies to benefit from this measurement and management tool, which is essential for implementing the policies that the sector needs.