Speaker Dr Jinying Xu from the DRF group joins the Lunchtime Seminar series with:
Talk Title: How to capture actual carbon data of infrastructure more intelligently? A standardised data model perspective
Abstract: Sustainability is the continued protection of human health and the environment while fostering economic prosperity and societal wellbeing. Specially, one of the most intriguing problems in sustainability is carbon measurement and analysis in infrastructure projects to meet the Net-zero 2050 target. The emission gap to achieve the net-zero target is huge. However, we are not fully aware how good or how bad with are doing for these targets. Data availability and quality are still found to be the major issues with the current labourious and unreliable manual carbon data collection. Some digital systems that are being developed still face the data trustworthiness and standardisation issues. To address the issues and support the advanced carbon management purposes, Dr. Xu proposes an intelligent carbon data management system that can enhance the data trustworthiness with the aid of digital technologies. She will share her research progress and results in developing the fundamental and standardised carbon data model.
Bio: Dr Jinying Xu is a Marie Skłodowska–Curie Fellow on the Future Roads Fellowship Programme in the Department of Engineering, University of Cambridge. She was awarded the Cambridge Zero Darwin College David MacKay research associateship and the Cambridge Centre for Smart Infrastructure and Construction (CSIC) Early Career Academics and Professionals Panellist. She received her Ph.D. from the University of Hong Kong, MPhil in construction project management from Tongji University, Bachelor of Engineering in engineering management and Bachelor of Law in Sociology from Harbin Institute of Technology. Dr Xu is interested in data science for construction sustainability, smart construction and facility management, construction digital transformation, human-organisation-technology fit, and human-machine augmentation in engineering.