Challenges and opportunities in Life Cycle Inventory data generation for the production of chemicals

Abstract

This study addresses the significant contribution of chemical production and use to environmental degradation and human health impacts, emphasizing the shift towards green chemicals and sustainable processes. Life Cycle Assessment (LCA) is identified as a central method for estimating the environmental impact of products, processes, and services over their entire life cycle. The study particularly focuses on the challenges in obtaining reliable and complete data sets for life cycle inventory, especially for technologies or chemicals at an early stage of development. A systematic literature review was conducted, analyzing publications since 2008. This review aimed to identify methods to reduce data gaps in chemical process inputs and outputs, with a focus on knowledge engineering and data mining, process simulation, predictive LCA using machine learning and multivariate statistics, and computer-aided molecular design. These methods were evaluated for their potential in generating accurate and comprehensive data for LCA. The study found that these methods can be effectively combined to improve the accuracy and reliability of LCA data generation. The applicability of these methods at different Technology Readiness Levels (TRLs) was also explored. Despite the uncertainties associated with some methods, such as predictive LCA and computer-aided molecular design, their application is deemed useful at various development stages. The study concludes by emphasizing the role of these methodologies in developing best practices for generating reliable LCA data, supporting informed decision-making towards sustainable chemical production. It also highlights the importance of open science practices in enhancing the transparency and reproducibility of LCA results, which is crucial for addressing potential uncertainties and advancing the field of green chemistry and sustainable processes.