Time: Thursday 2 December, 15:00-16:30

Venue: Main Building 317

Speaker: Professor Ming Xu, University of Michigan, USA

Speaker's introduction

Ming Xu received his B.S. and M.S. degrees in Environmental Science and Engineering from Tsinghua University in 2003 and 2006 respectively, and his Ph. He is currently a Professor in the School of Environment and Sustainability and a Professor in the Department of Civil and Environmental Engineering at the University of Michigan, USA, with research interests in environmental systems engineering and industrial ecology. He is the Editor-in-Chief of Resources, Conservation & Recycling (Impact Factor 10.204 in 2020). (CAS I). In 2015, he received the International Society for Industrial Ecology Young Scientist Award (Laudise Award) for his outstanding contributions to the field of industrial ecology and was elected to the 2024 Gordon Research Conference on Industrial Ecology. He was elected Chair of the Gordon Research Conference on Industrial Ecology in 2024 and President of the International Society for Industrial Ecology (2023-2024).

Brief description of the report.

Achieving carbon neutrality requires emissions reductions from both the production and consumption sides of the industry chain, and life cycle thinking is a key idea for reducing emissions from the consumption side. Life cycle thinking is a concrete manifestation of system thinking in the time dimension, considering a product or technology as a system as a whole, and making a comprehensive evaluation of the environmental impact of the product or technology from all aspects such as raw material extraction, processing and manufacturing, and use. In the face of carbon neutral adjustments, life cycle thinking can 1) identify the parts of a product's life cycle that cause greater environmental impact; 2) evaluate the true environmental impact of a product; and 3) guide consumers in making environmentally friendly purchasing decisions. At the same time, life cycle analysis also faces greater data challenges, and data science methods have some potential to address these challenges.

(Organised by: Department of Technology Economics and Management, Centre for Research and Academic Communication)