About the Chief Scientists
Yang Tong is a research fellow, doctoral supervisor at the School of Computer Science, Peking University, and a Young Chang Jiang Scholar. He is the first author and corresponding author of some 60 CCF Class A papers, mainly including SIGCOMM*3, SIGMOD*9, and SIGKDD*5. He was responsible for three topics/sub-topics under National Key R&D Programs, and one topic sponsored bythe National Natural Science Foundation of China. His research results were reported bythe National Natural Science Foundation of China onmany occasions and were industrialized in cooperation with Huawei, ZTE, Toutiao and Redis Database.
Liu Zhi, joint leader of the program, is currently an associate research fellow at the Blockchain-Native Energy Internet Research Institute(BNEIRI), Tsing Hua University. He previously worked for the Municipal Commission of Industry and Information Technology of Lianyungang and the Thermal Power Environmental Protection Center of the Ministry of Ecology and Environment. He also played a leading role in the study on CCER PLUS standards for the "Climate Change Financing Acceleration Platform (TA-6560PRC)" project sponsored by Asian Development Bank, the study on residual hybrid power factor in EU/North America, the plan for total coal consumption control in Harbin, the research report on heavy energy consumers of CGN, the global carbon credit research program for Tsinghua University/China Beijing Green Exchange/State Grid/AVIC and the research and development of the Chinese green certificate issuance and trading platform, and published 5 reports on recommended industry policies.
About the Program
Carbon asset is an important form of data asset in the context of carbon peak and carbon neutrality, and needs to harness information technologies throughout its lifecycle, from identification of ownership, to circulation, trading, de-registration and traceability. At present, the identification of asset ownership relies on data acquisition and manual accounting, which tends to cause errors and even duplicate identification. Therefore, there is a pressing need to apply machine learning and big data technologies to cross-validate the asset, minimize errors and assist carbon asset trading.
In view of the business characteristics of carbon asset ownership identification and trading, the program is intended to build an industry-leading and controllable artificial intelligence platform that supports mainstream machine learning and deep learning model libraries, is capable of multi-source data integration (including registration of trading entities, transaction records, historical ownership identification and de-registration, macroeconomic data, meteorological data, global mechanism projects, etc.) and compatible with mainstream and controllable artificial intelligence hardware and software. Innovations are centered on carbon asset ownership identification/consumption, among other supporting transactions, including: 1) the global carbon asset database; 2) accurate identification and rating of carbon asset; 3) intelligent verification of contracts fulfilled in the thermal power industry; and 4) intelligent traceability and anti-counterfeiting.
