Multi-Scale Prospective Modeling to Enhance Decision Making for Next-Generation Technologies

Multi-Scale Prospective Modeling to Enhance Decision Making for Next-Generation TechnologiesYuan Yao1, Runze Huang2, Eric Masanet31Department of Forest Biomaterials, North Carolina State University 2Department of Mechanical... [ view full abstract ]

Multi-Scale Prospective Modeling to Enhance Decision Making for Next-Generation Technologies

Yuan Yao¹, Runze Huang², Eric Masanet³

¹Department of Forest Biomaterials, North Carolina State University

²Department of Mechanical Engineering, Carnegie Mellon University

³Department of Chemical Engineering, Department of Mechanical Engineering, Northwestern University

Assessing the potential environmental impacts of emerging technologies at early-stage and integrating the insights from such impact analysis into RD&D (Research, Development, and Deployment) is critical to enhance decision making associated with technology investment and promotion. Such analysis can provide policy makers with insights useful for future investment and technology deployment; it also provides manufacturers and researchers with quantitative understandings of technology potential, possible bottlenecks, and future RD&D directions. However, impact assessments for emerging technologies are challenging for lack of process data, general evaluation approaches across different products, and robust methodologies over the large temporal and spatial scales. Furthermore, transferring the results from impacts assessment to insights that can directly guide R&D is even more challenging for the lack of effective methods to dynamically model the relationship between life-cycle environmental impacts of emerging technologies to technical parameters associated with R&D.

This presentation will discuss a novel multi-scale prospective analysis frameworks that are being developed to quantify the net energy, emissions, and economic implications of emerging technologies that can improve energy efficiency and reduce carbon emissions for the U.S. manufacturing industry. The framework systematically integrates engineering, economic, and environmental life-cycle models and statistical analysis to predict future impacts of new technologies at both plant and industry-wide scales. Different scenarios are designed to answer the questions such as how much impacts could be generated by adopting emerging technologies in future decades, how such impacts would be altered as the technology and industry evolve, and what key drivers of such impacts are. Mathematical optimization is applied to identify the directions that need intensive research efforts in order to achieve a specific goal of energy reduction or carbon mitigation. Case studies of how the framework can enhance decision-making related to the development and adoption of next-generation technologies such as 3D printing will be presented.