The application of life cycle assessment (LCA) to quantify the environmental, economic, social and other multi-dimensional impact potential of products has always been the direction of its development.  However, each dimension of traditional LCA method is isolated from each other, and it is difficult to identify the interconnections and interactions between multidimensions; its global and static perspectives fail to capture details of spatiotemporal variations effectively.

These challenges limit the application of LCA for actual complex systems with multidimensional interweaving and high spatiotemporal heterogeneity. This necessitates an approach that can well quantify multidimensional links and spatiotemporal variations to close the gap.

Recently, a research team led by Prof. TIAN Yajun from the Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT) of the Chinese Academy of Sciences (CAS) has explored the idea of coupling big data with LCA based on the previously established GIS-LCA methodology framework.

The team proposed a methodological framework of big life cycle analysis (BigLCA), which comprehensively coupled each stage of big data and LCA method. BigLCA developed a spatiotemporal inventory analysis scheme based on a modified multi-flow and multi-node model to calculate and integrate massive data associated with various dimensions.

The study was published in Ecological Indicators on June 8.

"BigLCA upgrades the traditional life cycle assessment to life cycle analysis, from description to prediction, from mutual independence to mutual correlation between dimensions, from abstract expression to spatiotemporal expression," said Prof. TIAN. "It provides a new approach to improve the accuracy of indicator measurement and the effectiveness and applicability of decision-making."

Universal BigLCA framework（Image by LI Junjie ）

(TEXT BY LI Junjie   TIAN Yajun  XIE Kechang )

Contact:

KONG Fengru

Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences

Tel: 86-532-58261072

E-mail: kongfr@qibebt.ac.cn