Improving Life Cycle Assessment Accuracy and Efficiency with Transformers

Life Cycle Assessment (LCA) is a methodology employed to evaluate the environmental effects of goods or services over their complete life cycle. LCA reports are key for the industry to assess their contribution on the sustainability. It is a complex and time-consuming process that can be improved with the use of deep learning models due to a large amount of data are involved in prediction. Transformers have been successful in natural language processing and can also be applied to numerical data to predict environmental impacts. By detecting the phases in a product's life cycle that generate the most significant environmental consequences and automating the data compilation and analysis procedures, they can reduce the time and expense connected with LCA. The use of transformers for LCA analysis has the potential to improve the accuracy and efficiency of sustainability assessments, providing more comprehensive information about environmental impacts. Through experimenting with real-world datasets, the proposed transformer framework has been shown to effectively contribute to making informed sustainability-related decisions by providing comprehensive information about environmental impacts. This has the potential to benefit a wide range of industries and sectors, enabling more sustainable development and decision-making.