Multi-level material cycles and market dynamics. University of Southern Denmark.

Hong Chen holds a Master’s degree in Financial Mathematics and Statistics from City University of Hong Kong, where she received systematic training in statistical modelling, stochastic analysis, data mining, and machine learning. She has since extended these quantitative methods to environmental data analysis and life cycle assessment (LCA), with research experience focusing on missing life cycle inventory (LCI) data prediction, environmental data imputation, and data-driven sustainability assessment. Through her involvement in research projects bridging LCA and artificial intelligence, Hong has developed substantial experience in applying AI methods to complex environmental datasets. Her work has contributed to improving the reliability of LCI data under conditions of high sparsity and missingness, focusing on how inventory data can be imputed, predicted, and evaluated. She has also participated in research on large-scale LCI matrix construction and the development of semantic information- and neural network-based methods for predicting environmental flows, thereby linking process descriptions with environmental flow data. Within the QuiVal project, Hong’s research focuses on the circular potential and value dynamics of secondary resources in the built environment under changing market and environmental conditions. She aims to develop a dynamic assessment framework to analyse the recycling and reuse potential of building materials across different scenarios, providing quantitative support for circular real estate valuation and the low-carbon transformation of the built environment.