地球大数据支撑可持续发展目标报告(2020):中国篇(英文版)

2 Challenges to Implementing the SDGs 3 Big Earth Data 4 Big Earth Data in Support of SDGs Chapter 1 Introduction In 2015， the United Nations Sustainable Development Summit adopted the 2030 Agenda for Sustainable Development， which proposed 17 Sustainable Development Goals (SDGs)covering economic， social， and environmental aspects. These goals represent the direction of national development and international cooperation. In the almost f ive years since their adoption，monitoring and evaluation of the SDGs have been constrained by a lack of data， varying capacities，and indicators that are both intertwined and mutually restrictive. The solutions to these pressingissues are entrusted to scientific and technological innovation. The CASEarth， drawing on Big Earth Data’s strengths in multi-scale and near real-time processing and system integration， hasreleased annual scientific evidence-based monitoring results for six SDGs， including SDG 2 (ZeroHunger)， 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). The reports represent a concrete contribution to the implementation of SDGs. Challenges to Implementing the SDGs The Global Indicator Framework for the Sustainable Development Goals and Targets of the 2030 Agenda for Sustainable Development was adopted by the United Nations (UN) in 2017. This framework serves as a voluntary preliminary system for the Member States to monitor progress towards the implementation of the SDGs. The framework is subject to regular ref inement and updates， and faces the following issues. (1) Lack of data. The previous situation related to the absence of evaluation methods and data has improved for all indicators over the past five years. However， despite the availability of methods， there is still a lack of data for 46% of all indicators. In regard to indicators where both methods and data are available， the results are largely measured via statistics without the support of spatial distribution information. Therefore， it is necessary to acquire spatial data that is objective and accurate， which including various spatial scales. Specifically， the data that is collected for scientific means can be used to regularly and quantitatively assess changes in the natural environment， accurately identify the spatial position of disasters， and predict their future trends. This refers to monitoring cases such as extreme high temperatures and heatwaves， the high frequency of f ires， ocean acidif ication， increased eutrophication， continued land degradation， reduced biodiversity， and increased environmental impact on agricultural production. (2) Imbalance in capacities. Developing countries are constrained in their ability to regularly and quantitatively collect and analyze data by their level of economic growth and the carrying capacity of resources and the environment. The lack of data has obfuscated serious issues such as the high ratio of stunting， the inadequacy of urban housing and public space， weak disaster resilience， the lack of access to safe drinking water， and the overuse of forests. Big Earth Data can be used to collect objective data at global， regional， and local scales in a timely， accurate， and comprehensive way. This can improve the compatibility and comparability of data， so that “no one is left behind” in regard to the information that is essential for SDG achievement. (3) The indicators are intertwined but mutually restrictive. SDG indicators are wideranging， long-term， and intertwined. These diverse and complex indicators consist of multiple tiers that come together to form a coherent， feasible whole. There is an urgent need to create methods and models for effective monitoring and evaluation based on compatible and quantifiable data. Big Earth Data The United Nations launched the Technology Facilitation Mechanism (TFM) to address the above-mentioned issues and challenges through Science， Technology and Innovation (STI). The initiative combines the collective wisdom of the scientific and business communities and stakeholders. Big Earth Data refers to large datasets in the Earth sciences that feature spatial attributes， especially the massive Earth observation data generated by space technology (Guo et al.， 2016). Such data is mainly produced at large spatial scales by scientific devices， detection equipment， sensors， socio-economic observations， and computer simulation processes. Like other types of big data， Big Earth Data is massive， multi-source， heterogeneous， multi-temporal， multi-scale， and non-stationary. Moreover， Big Earth Data has strong spatiotemporal and physical correlations， and the data generation methods and sources are controllable. Big Earth Data science is interdisciplinary， encompassing natural sciences， social sciences， and engineering. It systematically studies the correlation and coupling of