Multi-Source Earth Observation Enables City-Level Assessment of Urban Sustainability Across Belt and Road Region

A recent study published in the International Journal of Digital Earth presents a first city-level assessment of urban sustainability trends across more than 7,000 urban centers in the Belt and Road Initiative (BRI) region. Utilizing multi-temporal Earth observation (EO) data, the research evaluates two SDG 11 indicators—Land Use Efficiency (LUE, SDG 11.3.1) and population-weighted PM₂.₅ concentrations (PPM₂.₅, SDG 11.6.2)—from 2000 to 2020, providing a spatiotemporal analysis of sustainability of urban spatial expansion and environmental exposure.

Between 2010 and 2020, approximately 30.6% of cities exhibited improvements in LUE compared to the previous decade, while 24.3% saw a decline. Concurrently, 67.8% of cities experienced increased PPM₂.₅ exposure relative to 2000 levels, with the most severe degradation observed in Southern Asia, where annual average concentrations reached 53.9 μg/m³ by 2020. The integrated analysis reveals a dominant pattern of uncoordinated urban expansion—sprawling growth accompanied by increasing pollution exposure—present in 38.2% of sampled cities, particularly in South Asia.

In contrast, 22.6% of cities, largely located in East Asia, demonstrated the feasibility of decoupling spatial expansion from air pollution growth, with improved air quality despite sprawling land development. These results highlight the significance of regionally tailored urban and environmental policies. Notably, shrinking cities (LUE < 0) did not consistently show air quality improvements, underscoring that population contraction alone does not reduce environmental exposure in the absence of structural reform.

The research integrates satellite-derived built-up area data (GHS-BUILT-S), population grids (WorldPop), and a globally validated PM₂.₅ dataset with ~1 km spatial resolution to estimate city-level LCRPGR (Land Consumption Rate to Population Growth Rate ratio) and PPM₂.₅ indicators. Urban boundaries were defined using the GHSL-OECD Functional Urban Areas framework, enabling consistent longitudinal comparison.

A two-dimensional classification scheme was developed to cluster cities into eight urban development typologies based on LUE and PPM₂.₅ dynamics. This taxonomy provides a diagnostic framework for evaluating trade-offs between spatial expansion and environmental exposure, offering operational relevance for SDG monitoring and urban sustainability planning.

The study demonstrates the utility of open EO data for subnational SDG indicator monitoring, particularly in regions with limited statistical infrastructure. It calls for integrated strategies that combine compact urban design, effective air quality management, and data-informed governance. These insights are directly relevant to Belt and Road countries, where rapid urbanization and infrastructure expansion must be reconciled with pressing environmental and public health concerns.

The study was led by LIU Xuting, a PhD candidate at the Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, in collaboration with the International Research Center of Big Data for Sustainable Development Goals (CBAS), the European Commission Joint Research Centre (JRC), and KTH Royal Institute of Technology. Corresponding authors include Associate Professor LU Linlin and Professor GUO Huadong.