Recently, a PhD student of the Department of Environmental Science and Engineering at the Faculty of Innovation Engineering, Macau University of Science and Technology (MUST), Mr. Minghui Liang, along with his supervisor, Associate Professor Yahong Dong, published a review paper titled "A ternary framework for classifying and analyzing D-LCA across sectors: A systematic review" in the prestigious international journal Renewable and Sustainable Energy Reviews (Impact Factor: 16.3), a leading international journal in green and sustainable technology. The paper lists MUST as the first and sole institution, with Mr. Liang as the first author and Associate Professor Dong as the corresponding author.
Mr. Minghui Liang, PhD student of the Department of Environmental Science and Engineering at the Faculty of Innovation Engineering of MUST
Traditional Life Cycle Assessment (LCA) often relies on static data, which fails to reflect real-world temporal and spatial variations, leading to discrepancies between assessment results and actual environmental impacts. Dynamic Life Cycle Assessment (D-LCA) addresses this limitation by incorporating temporal resolution and spatial heterogeneity. However, inconsistent definitions and interpretations of "dynamic" across different fields have hindered its application and comparative evaluation. This study proposes a "ternary framework" that deconstructs dynamic characteristics into independent, intermediate, and dependent variables, enabling a unified classification and mechanistic analysis of dynamic characteristics across sectors.
The research finds that D-LCA studies focus predominantly on temporal dynamics, with the building sector being the most active area, primarily concentrating on dynamic impact assessment (D-LCIA). The electricity generation mix emerged as the most common intermediate variable, while carbon intensity and energy consumption were the most prominent dependent variables. Cross-sector analysis reveals that dynamic features in the building sector are driven by energy demand, occupant behavior, and temporal changes in the power structure, whereas the energy sector is influenced by fluctuations in renewable energy output and grid transformation. Future methodologies are expected to increasingly adopt high temporal-resolution data and techniques such as machine learning to enhance predictive and decision-support capabilities.
Since enrolling its first PhD cohort in September 2024, the Department of Environmental Science and Engineering under the Faculty of Innovation Engineering has rapidly achieved notable success in talent development. The publication of this paper by one of its inaugural PhD students strongly validates the university's exceptional research capabilities and education quality in this field.
Link to the paper: https://doi.org/10.1016/j.rser.2025.116329
