In the post-industrial era, many cities have experienced the decline of heavy industry and traditional manufacturing, leading to the widespread emergence of brownfields. These often cluster geographically, forming “brownfield clusters” characterized by shared spatial and functional traits. Our research examined these phenomena within 10 resource-exhausted cities in China, employing kernel density analysis to explore the spatial dynamics within and among these clusters and their urban contexts. We identified three distinct spatial relationships between brownfield clusters and their host cities (coupling, juxtaposition, and encircling), with a detailed case study in Huangshi City further classifying the clusters into five categories based on their dominant factors, spatial morphologies, types of brownfields, and internal dynamics. The study reveals that the spatial configurations of brownfield clusters are significantly influenced by geographic features, transportation infrastructure, and policy frameworks. Based on these findings, we propose targeted regeneration strategies for each cluster type. This research not only enhances our understanding of brownfield challenges and opportunities in China’s resource-exhausted cities but also serves as a valuable reference for other cities and regions worldwide facing similar challenges.

Many cities worldwide have large amounts of industrial vacant land (IVL) due to development and transformation, posing a growing problem. However, the large-scale identification of IVL is hindered by obstacles such as high cost, high variability, and closed-source data. Moreover, it is difficult to distinguish industrial vacant land from operational industrial land based solely upon image features. To address these issues, we propose a method for the large-scale automatic identification of IVL. The framework uses deep learning to train remote-sensing images of potential industrial vacant land to generate a semantic segmentation model and further use population density and surface temperature data to filter model predictions. The feasibility of the proposed methodology was validated through a case study in Tangshan City, Hebei Province, China. The study indicates two major conclusions: (1) The proposed IVL identification framework can efficiently generate industrial vacant land mapping. (2) HRNet exhibits the highest accuracy and strongest robustness after training compared with other semantic segmentation backbone networks, ensuring high-quality performance and stability, as evidenced by a model accuracy of 97.84%. Based on the above advantages, the identification framework provides a reference method for various countries and regions to identify industrial vacant land on a large scale, which is of great significance for advancing the research and transformation of industrial vacant land worldwide.

Currently, many industrial cities in Northeast China are grappling with a surge in brownfields, population loss, and environmental degradation, largely driven by industrial decline. Consequently, brownfield regeneration has emerged as a critical pathway for exploring sustainable development in shrinking cities. This study investigates the regeneration potential of urban brownfields against the backdrop of population shrinkage and aging. Under the framework of Ecosystem Service Theory, this study adopts the Fulaerji District of Qiqihar City as a representative case, integrating its four key categories of ecosystem services into a three-dimensional “economic–social–environmental” evaluation framework. This approach facilitates the transformation of Ecosystem Service Theory from a conventional post-regeneration evaluation tool into a pre-regeneration framework for assessing potential and determining site prioritization. A tripartite evaluation system was constructed, integrating economic vitality (e.g., the population shrinkage index, the proportion of the elderly population, and transportation accessibility), social culture (e.g., the industrial heritage proximity index), and ecological regulation functions (e.g., proximity of green spaces and importance of ecosystem services). Leveraging multi-source geospatial data, land surveys, and field inspections, 12 candidate brownfield sites were identified. GIS spatial analysis and the Analytic Hierarchy Process (AHP) were employed to decipher their spatial distribution patterns and comprehensive potential grades. The findings reveal the following: (1) The evaluation results exhibit distinct spatial characteristics: high-potential sites, clustered near transport hubs and policy-supported zones, demonstrate multi-dimensional advantages, while low-potential sites, constrained by aging demographics, poor accessibility, and ecological vulnerability, are predominantly located on the urban periphery. (2) Correlation analysis between potential grades and various evaluation indicators, combined with a cross-comparison of population shrinkage levels and average values of other evaluation indicators across different potential grades, reveals that neither the population shrinkage index nor the elderly population proportion exerts a systematic negative impact on industrial brownfield regeneration potential. This finding indicates that within specific urban development contexts, brownfield regeneration potential is influenced more by structural factors such as locational conditions, policy support, and ecological service functions than by singular demographic trends. This research provides both theoretical foundation and decision-making support for differentiated brownfield regeneration and spatial governance in population-shrinking cities.

Brownfield greening (BG) has become an important approach to addressing urban land scarcity, environmental remediation, and sustainable urban development. This scoping review analysed 116 English-language publications from the Web of Science and Scopus databases. The review identified five major research themes: pollution and remediation, regeneration design, brownfield characteristics and greening benefits, planning and decision-making, and stakeholder perceptions. Findings indicate a transition from contamination-focused studies towards integrated approaches emphasising ecological restoration, social values, and multifunctional green infrastructure. Research methods have evolved from qualitative case studies to interdisciplinary approaches involving spatial analysis, ecological modelling, scenario simulation, and participatory methods. Existing studies mainly focus on regenerated sites and site-scale analyses, while contamination and remediation processes are often insufficiently incorporated into planning, design, and ecosystem-service assessments. The review highlights the diverse ecological, social, economic, and cultural benefits generated by BG and identifies key research gaps, including the need to better integrate remediation into regeneration processes, to conduct long-term monitoring, to conduct comparative international studies, and to include evidence from underrepresented regions. Overall, BG is increasingly recognised as a multifunctional strategy for sustainable urban regeneration.