On the occasion of GLAD crossing the milestone of 100,000 datasets connected, we interviewed Ming Xu, the Baofeng Chair Professor of Carbon Neutrality at Tsinghua University (China) and founder of the TianGong Initiative. The following interview delves into Prof. Xu’s and his team’s experience with GLAD, illustrating not only the impressive execution of their application, but also how their aspirations for data align with the vision and goals of the Global LCA Data Access Network. 


Professor Ming Xu’s relationship with GLAD began in 2016, when he was first introduced to it through a Life Cycle Initiative’s  presentation at the Gordon Research Conference. Drawn to GLAD’s commitment to transparent data sharing, Ming became an avid advocate. 

The desire to link TianGong Database on the platform came shortly after, as Ming strongly believed it could bring the University to increased visibility, valuable feedback, and a better standing in the global data sharing network. 

“One way would be to simply share our data through Tsinghua University’s website, but that can only reach a limited number of people,” he stated as he described his goal of expanding to a global audience, offering broader access to valuable information. 

Ming expressed his aim of listing all TianGong’s datasets – around 4,000 unit processes – on GLAD. Planning for weekly updates through their GitHub repository, he saw AI’s role crucial in automating tasks and ensuring consistency in LCA. However, he acknowledged AI’s limitations in areas requiring human judgment and specific expertise, highlighting the commitment of 150 professionals devoted to generating TianGong’s data.  

Prof. Xu praised GLAD’s easy data import but stressed the need for clearer documentation to simplify the process. His suggestions for improvement include features such as bulk data upload and clearer metadata.  

Emphasizing GLAD’s neutral stance and ensuring data credibility is essential, the Professor stated that peer review from academic publications or third-party organizations could further enhance this credibility. 

For other data providers, Ming’s advice was clear: “Share your data through GLAD and other platforms as much as you can,” he urged. Turning then to GLAD users, he encouraged them to “dig deeper,” and understand the background and context of datasets, emphasizing the importance of thorough exploration. 

Overall, the journey towards sustainable solutions with the support of LCA is a collective responsibility, and GLAD, data providers, and end-users are at the forefront of achieving a paradigm shift towards a more environmentally conscious and resilient future. 


The TianGong Initiative is a non-profit, research-driven project led by Tsinghua University. Dedicated to leveraging the capabilities of data and AI, its mission is to propel environmental sustainability forward through the practice of Life Cycle Assessment (LCA). The Initiative draws inspiration from the Tiangong Kaiwu encyclopaedia (first published in 1637), renowned among historians for its insights into various early Chinese production processes. 

True to its name, TianGong functions as a comprehensive repository of datasets that embodies principles of openness, credibility, and transparency to foster deeper understanding of environmental impact assessments.  

If you wish to learn more, follow this link: https://www.tiangong.earth/