Enabling Retrospective Life Cycle Assessment in the Prospective Context for Emerging Technologies

Life cycle assessment (LCA) is increasingly applied to guide challenging RD&D and policy decisions for emerging technologies to lead a sustainable future. The approach is developed from attributional to consequential and from... [ view full abstract ]

Life cycle assessment (LCA) is increasingly applied to guide challenging RD&D and policy decisions for emerging technologies to lead a sustainable future. The approach is developed from attributional to consequential and from retrospective to prospective to incorporate the use expansion. Through the integration of scenario development, technology forecasting, and other analysis and techniques, LCA can offer prospective insights, and evaluate the potential effects of implementing a decision/policy in addition to understanding the environmental impact of existing products/systems, which is helpful for rapidly-changing emerging technologies. However, by nature LCA is retrospective and rely on inventory data in high quality and quantity, which create difficulties for prospective LCA in maintaining effectiveness and accuracy while justifying inherent uncertainty for emerging technologies. Additionally, as oppose to evaluating the future effects of a decision today, a reverse engineering/scientific problem, such as identifying an effective decision today for certain desired effect to happen in future, seems unsolved and less discussed in the necessary development of LCA to cope the challenge. 

The presentation will discuss a systematic method that is being developed to enable retrospective LCA to function in a prospective context to help address the reverse question for emerging technologies. With predetermined future objectives in sustainability, the method integrates LCA with engineering models, economic assessments, statistical analysis and forecasting tools to generate possible scenarios in which sets of technical parameters within their uncertainty are found. Optimization algorithms are developed and applied to identify the important technical targets for the emerging technology that require R&D focus and generate an innovation roadmap leads to largest average environmental and economic benefit potentials in all scenarios. Additive manufacturing and other emerging technologies are applied as case studies to demonstrate the methods and how the methods can help improve decision-makings in R&D and accelerate the development and deployment of new technologies as well as achieve environmental and economic bottom lines.