On the 70th anniversary of the United Nations (UN) in September 2015, heads of state and delegates gathered at the UN headquarters in New York and adopted the 2030 Agenda for Sustainable Development. This comprehensive sustainability framework was built on the basis of the historical experiences of human society and a shared expectation for the future. It presents a blueprint for countries to pursue global sustainable development in the next 15 years. The 17 Sustainable Development Goals (SDGs) incorporate various social, economic, environmental, and developmental targets and indicators, and have been endorsed by all countries with respective national implementation plans.
With the advancement of science, technology, and innovation (STI) accelerating, there is a growing international consensus that STI must play a key role in facilitating the implementation of SDGs. To this end, the UN established the “Technology Facilitation Mechanism” to bring together scientific communities, policy makers, business sectors, and other stakeholders for their collective ideas, insights, knowledge, and wisdom to build societies that are harmonious with the environment. The Chinese Academy of Sciences (CAS), being a member of the international scientific community, has been mobilizing its research capacities for action.
The SDGs consist of 17 goals, 169 targets, and over 230 indicators. Countries have different and very diverse development contexts. The key to success for one goal is often linked to solving issues associated with other goals. The SDGs thus constitute a vast development system that is complicated, diverse, dynamic, and interconnected. There are four major challenges in the implementation of the United Nations (UN) Sustainable Development Goals (SDGs), including: (1) missing data and the evolution of SDG indicators, (2) complementary and non-complementary interconnections between different SDGs, (3) complicated and varied problems in quantifying and monitoring indicators within different national and local contexts, and (4) difficulties in modeling indicators to monitor SDGs. Particularly, the main challenge in monitoring progress relates to the lack of data available for the development of indicators, and this lack of data has been identified for nearly half of indicators. The full implementation of SDGs will be hampered if these problems and challenges are not effectively resolved.
As an important aspect of technological innovation today, big data is bringing new tools and methodologies to scientific research. Based on Earth science, information science, and space science, Big Earth Data derives and integrates data from spatial Earth observations as well as terrestrial, oceanic, atmospheric, and human activity data from other sources. Big Earth Data is therefore characterized in terms of massive quantity, multiple sources, heterogeneous structure, and high complexity. Big Earth Data can also be non-stationary, unstructured, multi-temporal, and multi-dimensional. Effective use of Big Earth Data has offered a new key to generating knowledge about planet Earth, playing a major role in promoting sustainable development.
To this end, the Chinese Academy of Sciences (CAS) launched the “Big Earth Data Science Engineering Program” (CASEarth) in early 2018, and took SDGs as one of its top priorities. The main objectives for CASEarth to serve SDGs include converting Big Earth Data into SDG-related information, providing decision support for SDG implementation, constructing a Big Earth Data integration system for SDG indicators, and investigating the inter-linkages among different components of the Earth system. CASEarth concentrates on six SDGs, including: SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), SDG 14 (Life below Water) and SDG 15 (Life on Land).
CASEarth supports SDG indicators in three major ways. (1) Big Earth Data is used to fill in missing data and provide new sources of data for evaluation. (2) New methodologies are created to evaluate SDGs on the basis of Big Earth Data technologies and models. (3) CASEarth provides practice cases of Big Earth Data for SDGs, and aids in monitoring the progress of SDG indicators. CASEarth relies on novel methods for multi-source data acquisition, cloud data analysis, and artificial intelligence technologies to study cases at different scales. These methods also aid in developing global and regional SDG indicator assessment systems based on Big Earth Data for global and national appraisal and reporting.
The TFM is an important machine for achieving SDGs, fully concurring with China’s concept of and strategy for STI for sustainable development. Big Earth Data as an innovative technology has great potential to this end. It is the plan of CASEarth to continue research on SDGs and publish a report on Big Earth Data in Support of the Sustainable Development Goals every year. For this perspective, CASEarth warmly welcomes cooperation from all research partners, both in China and around the world.