Environmental Implications of Emerging Technologies

Project Overview

Understanding the potential environmental, economic, and societal impacts of emerging technology is critical to support decision-making related to fundamental research and technology development. The Yao Lab develops innovative modeling frameworks to understand the potential impacts of early-stage technologies and identify drivers of such impacts, aiming to guide the research towards the most robust and sustainable pathway. Recent focuses include artificial intelligence (AI), carbon capture and storage, sustainable materials such as bioplastics.

The Yao Lab has multiple ongoing projects under this research direction. Those projects investigate various emerging technologies, ranging from Artificial Intelligence (AI) to green chemistry, carbon capture and storage, and next-generation manufacturing.

Artificial Intelligence (AI) has received increasing interest recently. AI refers to machines’ ability to perform activities that mimic human intelligence. AI could be implemented through different techniques in computer science, such as machine learning. Although AI shows great promise to address research and technical challenges across different fields, the potential environmental, economic, and social consequences of large-scale AI adoption have not been fully understood. Our research aims to address this gap by exploring and investigating assessment methods that could be applied to understanding the benefits and risks of different AI applications.

Check our recent publication for more information on the challenges and opportunities of AI applications.

Applications of Artificial Intelligence‐Based Modeling for Bioenergy Systems: A Review

Life Cycle Assessment (LCA) has been used to assess the environmental implications of emerging technologies in different manufacturing sectors. However, it is challenging to use the traditional LCA method to model the relationships between Life Cycle Inventory (LCI) data and vital technical parameters, preventing further analysis for understanding key driving factors and determining priorities for research and technology development. Furthermore, the sensitivity analysis of traditional LCA could be misleading for decision making or strategic planning due to the lack of considering the potential improvement of specific parameters. The Yao Lab has been developing integrated modeling frameworks to address the methodological challenges. Check Prof. Yuan Yao’s presentation on one of the new modeling frameworks developed for emerging technology.


For new projects related to other emerging technologies such as carbon capture and green chemistry, visit the Yao Lab website for more details and updates.