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CBAS: An International Platform of Digital Technologies Facilitating Sustainable Development Goals
The Chinese Academy of Sciences (CAS) launched the Big Earth Data Science Engineering Program (CASEarth) in 2018, which laid the foundation for the International Research Center of Big Data for Sustainable Development Goals (CBAS). Building on CASEarth’s achievements, CBAS integrates advanced digital technologies to advance the UN Sustainable Development Goals (SDGs) through five key missions: (1) developing SDG data infrastructure and information products via its SDG Big Data Platform, utilizing advanced computing, cloud services, and AI; (2) developing and launching a series of SDG satellites, including SDGSAT-1, which provides crucial Earth observation data through its Open Science Program; (3) providing new knowledge for SDG monitoring and evaluation through annual reports (Big Earth Data in Support of the Sustainable Development Goals, published since 2019) and guiding the development of big data-driven technical solutions and theoretical systems; (4) establishing a think tank for science, technology, and innovation, promoting SDGs through initiatives like the annual International Forum on Big Data for Sustainable Development Goals (FBAS) and the CBAS Fellowship Program; and (5) providing capacity development for SDGs in developing countries through international collaborations, such as the Digital Belt and Road Program (DBAR), which offers professional education and training in big data. Future CBAS efforts will focus on expanding data access, enhancing AI capabilities for SDG indicator monitoring, and strengthening international partnerships to address data gaps and ensure equitable access to technology and expertise for achieving global sustainability.
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@article{guo2024cbas,
title = {CBAS: An International Platform of Digital Technologies Facilitating Sustainable Development Goals},
author = {Huadong Guo and Dong Liang},
year = {2024},
journal = {Bulletin of the Chinese Academy of Sciences},
publisher = {EDP Sciences},
volume = {38},
pages = {2024013},
issn = {1003-3572},
doi = {10.1051/bcas/2024013},
}