地球大数据支撑可持续发展目标报告(2020):“一带一路”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 a plan of action titled Transforming Our World: The 2030 Agenda for Sustainable Development， which proposed 17SDGs covering economic， social and environmental aspects. These goals represent the direction of national development and international cooperation needed to address the world’s mostpressing environmental and societal issues. In the almost f ive years since the Agenda’s adoption，the monitoring and evaluation of its implementation have been constrained by a lack of data，varying capacities， and indicators that are both intertwined and mutually restrictive. Scientific and technological innovation is a solution to this pressing issue， and this report focuses specif icallyon “Big Earth Data”， a set of technologies and methods being practiced at the intersection of big data and Earth observation. The CAS Big Earth Data Science Engineering Project (CASEarth)has released annual scientific， evidence-based monitoring results for six SDGs — 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) — by drawing on Big Earth Data’s strengths in multi-scale， near-real-time processing and system integration. This is a concrete contribution to implementing the SDGs. 　　Challenges to implementing the SDGs 　　The Global Indicator Framework for the Sustainable Development Goals and Targetswas adopted in 2017 by the United Nations as a preliminary， voluntary system for the Member States to monitor progress in implementing the SDGs. The Framework， subject toregular ref inement and updates， faces the following problems. 　　(1) Lack of data. The previous situation where there were neither evaluation methods nordata has improved for all indicators in the past f ive years. For 46% of indicators， however，there are methods but no data; for those where both are available， the results are measuredlargely in a statistical way without the support of spatial distribution information. Spatialdata that is objective and accurate at varying scales is necessary for achieving the SDGs.Specif ically， data collected Scientifically can be used to assess changes in the naturalenvironment regularly and quantitatively. They can accurately identify the spatial positionof disasters and predict their future trends， in such cases as extremely high temperaturesand heatwaves， higher frequency of f ires， ocean acidif ication， increased eutrophication，continued land degradation， reduced biodiversity and increased environmental impact onagricultural production. 　　(2) Imbalance in capacities. Developing countries are constrained by the level of theireconomic growth and carrying capacity of resources and environment in their abilitiesto collect and analyze timely， quantitative data. The lack of data has rendered invisiblesuch serious issues as high ratios of stunting， inadequacy of urban housing and publicspace， weak disaster resilience， lack of access to safe drinking water， and overuse offorests. Those who take advantage of Big Earth Data can collect objective data at global，regional， and other scales in a timely， accurate， and comprehensive way while improvingtheir compatibility and comparability， so that “no one is left behind” on data essential toachieving the SDGs. 　　(3) Intertwined， but mutually restrictive indicators. SDG indicators are wide-ranging，long-term， and intertwined. These diverse， complex indicators at multiple tiers cometogether to form a coherent， feasible whole. The creation of methods and models for objective and effective monitoring and evaluation based on compatible， quantif iable datais an urgent issue for which a solution must be found. 　　Big Earth Data 　　The United Nations launched the Technology Facilitation Mechanism (TFM) to addressthe above-mentioned issues and challenges through Science， Technology， and Innovation(STI) by pooling the collective wisdom of the scientific community， business community and other stakeholders. 　　Big Earth Data refers to the use of big data in the f ield of Earth science with spatialattributes， especially the massive Earth observation data generated by space technology(Guo et al.， 2016). Such data is mainly produced at a large spatial scale by Scientificdevices， detection equipment， sensors， socio-economic observations and computer simulation processes. Similar to other types of big data， Big Earth Data is massive， multisource，hetero geneous， multi-temporal， multi-scaled and non-stationary. Moreover， it 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 corr