A big data approach to assess progress towards Sustainable Development Goals for cities of varying sizes

ARTICLE https://doi.org/10.1038/s43247-023-00730-8 OPEN A big data approach to assess progress towards Sustainable Development Goals for cities of varying sizes 1234567890():,; Yu Liu1, Bo Huang 1,2,3 ✉, Huadong Guo 4,5 & Jianguo Liu 6 Cities are the engines for implementing the Sustainable Development Goals (SDGs), which provide a blueprint for achieving global sustainability. However, knowledge gaps exist in quantitatively assessing progress towards SDGs for different-sized cities. There is a shortage of relevant statistical data for many cities, especially small cities, in developing/underdeveloped countries. Here we devise and test a systematic method for assessing SDG progress using open-source big data for 254 Chinese cities and compare the results with those obtained using statistical data. We find that big data is a promising alternative for tracking the overall SDG progress of cities, including those lacking relevant statistical data (83 Chinese cities). Our analysis reveals decreasing SDG Index scores (representing the overall SDG performance) with the decrease in the size of Chinese cities, suggesting the need to improve SDG progress in small and medium cities to achieve more balanced sustainability at the (sub) national level. 1 Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China. 2 Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China. 3 Department of Sociology, The Chinese University of Hong Kong, Hong Kong, China. 4 International Research Center of Big Data for Sustainable Development Goals, Beijing, China. 5 Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China. 6 Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA. ✉email: COMMUNICATIONS EARTH & ENVIRONMENT | (2023)4:66 | https://doi.org/10.1038/s43247-023-00730-8 | www.nature.com/commsenv 1 ARTICLE T COMMUNICATIONS EARTH & ENVIRONMENT | https://doi.org/10.1038/s43247-023-00730-8 he Sustainable Development Goals (SDGs)1 adopted by all members of the United Nations call for concerted efforts to achieve global social, economic, and environmental wellbeing. National governments have demonstrated strong commitment to the SDGs, but cities are critical actors in implementing the sustainability agenda—an estimated 65% of the 169 targets underlying the 17 SDGs require city engagement2. As the centre of social and technological innovations, cities will continue to drive the achievement of the SDGs3. Nevertheless, rapid urban development has also introduced pressing social and environmental problems—such as various inequalities4, air pollution5, and a lack of infrastructure6—all of which threaten city prospects. Thus, local municipal governments globally are integrating the SDGs into their development plans to address these challenges and participate in a global dialogue7,8. Implementing and achieving the SDGs requires measuring and assessing progress in different contexts and determining development priorities. Quantitative assessments of SDG progress have been undertaken at the global9,10, regional11, national12, and subnational13 levels by various government and nongovernment organisations. Among them, the SDG Index score (arithmetic mean of 17 individual SDG scores) has been highlighted as useful for comparing the overall SDG performance of different countries and provinces. The indicator framework and systematic methods arising from such research are essential for understanding SDG progress and the actions to take next14, which should be communicated to the intended target audience in a way that is easy to interpret15. At the city level, from 2016 to 2021, nearly 80 voluntary local reviews were submitted by city governments in different countries to report their progress16, while most of these reviews focused on status descriptions and governance arrangements regarding the SDGs and offered little in terms of setting baselines or evaluating progress towards SDG targets. Transforming the SDGs and their targets into a data-driven management tool to quantify progress is crucial for formulating evidencebased strategies and refining resource allocation11. However, only some large cities or capital cities/provincial capitals have measured their progress towards 15 or 16 of the 17 SDGs2,17. Largescale sustainability assessments of all 17 SDGs for all cities of varying sizes in a specific country are still limited. The shortage of relevant statistical data in many cities in developing and underdeveloped countries has worsened the situation. Among the cities at the prefecture level or higher in China, the number of small cities and their total land area are larger than those of large cities, but small cities face a more serious data shortage problem (see details in Table S5), thereby hindering the development of holistic strategies for promoting city sustainability. Thus, there is an urgent need to develop systematic methods to address the shortage of relevant statistical data in quantifying city-level progress towards SDGs, especially for small cities. The wide availability of big data with five important characteristics (large amount, fewer properties, high data generation speed, great variety of data formats and sources, and high economic benefits)18 provides tremendous opportunities to monitor SDG progress. This capability has been highlighted for indicators and targets in SDG assessment studies19,20. More than a quarter of the publications pertaining to SDG assessment using big data have focused on the indicator (target) monitoring of SDGs 1.1.1 (the international poverty line), 1.1.2 (national poverty lines), 6.6.1 (water-related ecosystems), and 15.3.1 (degraded land), which underlie SDGs 1 (no poverty), 6 (clean water and sanitation), and 15 (life on land)21. Multiple types of big data (e.g., nighttime light (NTL) satellite imagery, point of interest (POI) data, and OpenStreetMap data) have been integrated to construct a variety of monitoring indicators that reflect the current status of cities in a timely and efficient way to help assess the SDGs. The 2 same big data can also be applied to monitor multiple SDGs. For example, NTL satellite imagery was used not only to represent economic growth (SDG 8)22 but also to map poverty (SDG 1)23 and estimate inequality (SDG 10)24. On the other hand, machine learning models—including random forest25, boosted regression trees26, and artificial neural networks (ANNs)27—have been used in monitoring processes to improve evaluation efficiency21. However, these studies have focused only on the assessment of one or a few indicators (targets) of a specific SDG, and they have lacked an overall consideration of multiple SDGs. A comprehensive evaluation is a fundamental step for identifying the priorities that citie (...truncated)