In the last century the impacts of human activity on natural processes that sustain the Earth’s biosphere, atmosphere, hydrosphere and lithosphere and that provide the bedrock of human life support systems, have grown to the extent that they pose a credible existential threat to humanity. Today, the biggest challenge for science, technology and innovation (STI) is to contribute to the pursuit of global sustainability as exemplified in the Sustainable Development Goals (SDGs) that were adopted by the United Nations (UN) in 2015. Referred to as the 2030 Agenda for Sustainable Development, the SDGs comprise an ambitious, integrated framework of goals that represent humanity’s commitment to comprehensive and transformative action in response to the world’s most pressing social, economic, and environmental problems.
In developing strategies for the successful achievement of the 2030 Agenda, the UN recognizes the importance of integrating scientific evidence in policy and decision-making processes. Through the Technology Facilitation Mechanism (TFM) and other means at its disposal, the UN encourages multi-stakeholder engagement and partnerships that can effectively mobilize and utilize STI to generate actionable knowledge and contribute practical solutions to global sustainability demands, problems, and challenges.
One of the key aspects that the UN is focusing on is improving access to, and ensuring the quality of, reliable data sources. Doing so allows us to establish what situations, risks, and ongoing policies should be considered in order to correctly analyze data and develop effective strategies. The lack of a comprehensive implementation plan for the Global Indicator Framework for the Sustainable Development Goals and Targets, adopted by the UN in 2017 as a means of measuring and monitoring progress towards the SDGs, exposes the challenges and systems gaps in data collection. It points to a pressing need for the urgent identification of well-defined collection methods, which hitherto have prevented the successful implementation of the indicator framework. The International Science Council report “A Guide to SDG Interactions: from Science to ImplementationFootnote1” further stresses the importance of data as a driver for policy-making, by highlighting the need to observe and evaluate the dynamic interaction between different SDGs when formulating implementation policies through an integrated and trans-disciplinary scientific approach.
Ensuring sustainable development therefore calls for innovative ideas utilizing new and multiple sources of data and information. This has been made possible by the rapid digitization of society in the past decades. Mass quantities of data on human activities and behaviors and on environmental changes – “Big Data” – have created enormous value and resulted in inventive services that enable the inclusion of digital concepts in a wide variety of fields and applications; for example, in disaster-risk reduction, urban management, agricultural management, and environmental and bio-diversity monitoring. Furthermore, the rapid development of data analytics has enabled wider accessibility of these concepts through services available as open sources on the Internet. These developments have empowered efforts to collect and integrate data from diverse sources to enable a more comprehensive understanding of the different processes present in these complex systems and environments.
Big data also has the potential to inform and develop cutting-edge engineering solutions. In the UNESCO Engineering ReportFootnote2 released in 2021, the World Federation of Engineering Organizations recognized that big data, artificial intelligence, and the Internet of Things have strong potential in relation to innovative, transformational engineering, especially in their application to the Sustainable Development Goals. Artificial intelligence driven by big data enables “intelligent” engineering systems and solutions that can be scaled – as is embedded in the concept of smart cities. The engineering for improving healthcare has also benefited from big data and artificial intelligence. Multiple streams of information within the big data framework, such as citizen science and crowdsourcing, have also been recognized as ways of making improvements to critical resilience strategies, such as early warning systems and disaster-related systems and infrastructures that build resilient communities. There is also great potential for improving mining operations using big data-enhanced systems and concepts.
However, even with this significant progress in big data concepts and methods, there is still tremendous potential for systematic implementation of these concepts in policy and decision-making systems. New, specialized research centers are needed to help develop resources and systems to facilitate these efforts towards the implementation of big data in achieving the SDGs. The International Research Center of Big Data for Sustainable Development Goals (CBAS),Footnote3 to be established in September 2021, will provide a unique opportunity for scientific cooperation, building robust international and multi-stakeholder cooperation that will enable the development of a comprehensive strategy for supporting the implementation of the SDGs by leveraging the great potential of big data. CBAS will be the first-ever research agency focusing on the UN SDGs through big data science, with an aim to build a big data technology platform and research center in support of the SDG implementation. The Center will feature an integration of research, technological innovation, data service, talent fostering, and capacity building, and is expected to become a think tank for relevant UN organizations and member states in the implementation of the 2030 Agenda. To enhance this cooperation, the Big Earth Data Science Engineering Program (CASEarth) of the Chinese Academy of Sciences, which has laid a solid foundation to the CBAS, is building a Big Earth Data cloud service platform as well as a Digital Earth science platform, and is designing a series of SDG satellites tailored for the monitoring of SDGs implementation, in which the SDGSAT-1 will be launched into the space in October this year.
These cooperation mechanisms and research institutes need to develop new concepts and applications for big data that enable innovation, as well as opportunities to develop science and technology-led solutions towards our urgent global challenges. To promote discussion around these emerging and exciting technologies, this special issue, “Big Data in Support of the Sustainable Development Goals,” aims to compile relevant scientific concepts and research in a two-part series to be published in August and December 2021. The first part of this series contains seven papers on data and science from experts at leading institutes.
Huadong Guo et al. introduce the concept of Big Earth Data as a special class of big data with extensive applications in Earth system science, focusing on the integration of multi-source data within a geographical context. They detail the advances made to the Big Earth Data concept by the CASEarth program, and highlight future priorities for improving and promoting the implementation of the Big Earth Data concept in relation to sustainable development.
Markku Kulmala et al. explore the concept of “Big Open Data” and how this can contribute to sustainable development. The authors focus on measurements obtained by the “Station for Measuring Earth Surface Atmosphere Relations (SMEAR)”, which is used to make comprehensive observations that help with understanding the complex interactions between densely populated urban environments and the atmosphere. It is suggested that a network of stations carrying out simultaneous observations of multiple variables could provide invaluable information to science-informed policy making for sustainable development.
Szabolcs Mihály et al. provide a sweeping review of the progress made in the integration of Earth observation and other forms of statistical and ancillary data from administrative, management, or sensor networks in Hungary. They also review the benefits and strengths that have resulted from this, including looking through the lens of the associated mechanisms. The authors advocate the establishment of a national spatial information infrastructure and make a compelling argument to ramp up efforts to popularize and disseminate big data applications in relation to the SDGs.
David Castle et al. introduce the concept of biosurveillance big data. They discuss the rapidly growing volume of this type of data and its sources, as well as the advantages of using this type of big data in policy development, aiming to improve the implementation of the SDGs.
Christopher J. Owers et al. describe their implementation of a global framework for classifying land cover called “Living Earth,” which can provide suitable classification of land cover in support of SDG targets and reporting. Living Earth fills in data and information gaps by providing actionable knowledge and information for relevant actions in SDG implementation that would not be possible without analysis-ready land cover data.
Yiyi Huang et al. present research on the use of big data analysis to quantify the utilization of urban green space in 366 cities in China. This analysis reveals that 94.01% of urban green spaces have not been utilized to their full potential. They observe that the difference between intra-urban green space and peri-urban green space is larger in Southwestern and Northwestern China than in Eastern China. These results will be essential to improving policies that enable better utilization of urban green spaces, with the potential to lift health and wellbeing indices in urban centers.
Bin Chen et al. focus on essential urban land-use categories (EULUC) and the benefits of applying these categories in urban studies. The authors explore the benefits of Earth observation data and the improvement in algorithms and data products. This has been made possible by emerging social sensing big data and auxiliary crowdsourced datasets that enable fine-resolution, and multi-scale analysis. The authors also provide a comprehensive review of current challenges and limitations, and propose new opportunities for the future application of EULUC mapping, as well as ways of improving its potential.
The Guest Editors believe that all of the papers selected for Special Issue A (and also those forthcoming in Special Issue B) describe new prospects for the application of big data, which can catalyze and operationalize the implementation of the Sustainable Development Goals.